ABSTRACT SMAD3, a protein-coding gene, assumes a pivotal role within the transforming growth factor-beta (TGF-β) signaling pathway. Notably, aberrant SMAD3 expression has been linked to

various malignancies. Nevertheless, an extensive examination of the comprehensive pan-cancer impact on SMAD3’s diagnostic, prognostic, and immunological predictive utility has yet to be

undertaken. Bioinformatics methods were employed to systematically investigate the potential carcinogenic impact of SMAD3. We extensively harnessed data from authoritative sources, including

The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), cBioPortal, Human Protein Atlas (HPA), UALCAN, and various other databases. Our study encompassed a comprehensive analysis

functional attributes of SMAD3 within the milieu of Liver Hepatocellular Carcinoma (LIHC). Our findings revealed significant variations in SMAD3 expression between cancerous and adjacent

normal tissues. High levels of SMAD3 expression were consistently associated with unfavorable prognoses across multiple cancer types,. Additionally, our analysis of SMAD3 methylation

patterns in human cancers unveiled a favorable prognosis linked to elevated DNA methylation levels in pan-cancer. Furthermore, we identified positive associations between SMAD3 expression

and RNAm6A methylation-related genes in the majority of cancers. Moreover, SMAD3 expression displayed substantial correlations with immune cell infiltration. Notably, immune checkpoint genes

exhibited significant associations with SMAD3 expression across diverse cancers. Single-cell sequencing results elucidated the pan-cancer single-cell expression landscape of SMAD3. Within

specific cancer subtypes, SMAD3 expression exhibited a noteworthy positive association with distinctive facets of malignancy. Finally, in our comprehensive analysis of drug sensitivity, we

discerned a catalog of prospective therapeutic agents. In our comprehensive analysis across multiple cancer types, we observed a significant disparity in SMAD3 expression compared to normal

tissues, and this findings suggest that SMAD3 holds promise as both a prognostic biomarker and a therapeutic target against various cancers. Difference displayed a noteworthy association

with patient prognosis. SIMILAR CONTENT BEING VIEWED BY OTHERS LZTS2 METHYLATION AS A POTENTIAL DIAGNOSTIC AND PROGNOSTIC MARKER IN LIHC AND STAD: EVIDENCE FROM BIOINFORMATICS AND IN VITRO

ANALYSES Article Open access 22 May 2025 _ZC3H12D_ GENE EXPRESSION EXHIBITS DUAL EFFECTS ON THE DEVELOPMENT AND PROGRESSION OF LUNG ADENOCARCINOMA Article Open access 18 May 2025 TMEM101

EXPRESSION AND ITS IMPACT ON IMMUNE CELL INFILTRATION AND PROGNOSIS IN HEPATOCELLULAR CARCINOMA Article Open access 30 December 2024 INTRODUCTION Cancer ranks as the second leading global

cause of mortality, responsible for approximately 14.1 million new cases and 8.2 million annual fatalities1. While recent years have witnessed the emergence of immunotherapy and targeted

therapy as novel cancer treatment strategies, their applicability remains limited to a subset of patients2. Moreover, the five-year survival rates continue to be relatively modest3.

Consequently, enhancing the effectiveness of cancer treatment necessitates the identification of more potent biomarkers4. SMAD3 is a pivotal transcription factor that modulates the

transforming growth factor-beta (TGF-β) signal and assumes a dual role in both restraining and promoting cancer. It features highly conserved regions at the N- and C-termini, known as the

mad-homology domain 1 (MH1) and MH2 regions, respectively. The MH1 region primarily governs DNA binding, while the phosphorylated MH2 region plays a critical role in the canonical TGF-β

signaling pathway, As the C-terminal phosphorylated form, known as R-SMAD, SMAD3 functions as an intracellular signaling transducer and transcriptional regulator. Activation is initiated by

TGF-β and activin type 1 receptor kinases. R-SMAD binds to TGF-β responsive elements (TREs) in the promoter regions of numerous TGF-β-regulated genes. Following the formation of the

SMAD3/SMAD4 complex, it translocates to the nucleus to activate transcription, modulate gene activity, and stimulate cell proliferation. Numerous studies have illuminated SMAD3’s involvement

in various disease states, with a growing recognition of its role in cancer5. Research has revealed that SMAD3 promotes the formation of cancer-associated fibroblasts through

macrophage-myofibroblast transition. Chromatin immunoprecipitation sequencing (ChIP-seq) has demonstrated substantial enrichment of Smad3 binding to genes related to fibroblast

differentiation in LLC tumor macrophage lineage cells. Notably, targeted deletion in macrophages and pharmacological inhibition of Smad3 effectively thwart macrophage-myofibroblast

transition, thereby impeding cancer-associated fibroblast formation and cancer progression in vivo6. Other studies have shown that macrophage-specific gene deletion and systemic

pharmacological inhibition of TGF-β1/Smad3/Runx1 signal transduction effectively prevent MMT driven CAF and tumor formation in vitro and in vivo, reflecting the important role of SMAD3 in

MTT driven CAF7. Aberrant SMAD3 activation is a pivotal driver of breast cancer metastasis. This occurs via the acetylation of SMAD3 by KAT6A at K20 and K117, promoting interactions with

TRIM24, and disrupting the interaction with the tumor suppressor TRIM33. Consequently, this leads to heightened SMAD3 activation and the expression of immune response-related cytokines,

culminating in increased breast cancer stemness, myeloid-derived suppressor cells (MDSC), and metastasis in triple-negative breast cancer (TNBC)8. Furthermore, SMAD3 plays a role in

promoting cancer progression by impeding E4BP4-mediated natural killer (NK) cell development9. Subsequent studies unveiled that NK-3-S1KD, a genetically engineered stable Smad3-silenced

human NK cell line, effectively curbs cancer progression in xenografted mouse models with human liver cancer and melanoma10. Elevated SMAD3 expression levels also correlate with the

progression of various cancers. Studies indicate that SEPHS1 promotes SMAD3 expression and enhances invasion of hepatocellular carcinoma cells11. SMAD3 also plays a crucial role in bladder

cancer; for instance, it participates in miR-5581-3p-mediated inhibition of bladder cancer cell migration and proliferation12. Additionally, cirRIP2 accelerates bladder cancer progression

through the miR-1305/TGF-β2/SMAD3 pathway13.Moreover, miR-195 inhibits cervical cancer migration and invasion by targeting SMAD314, while Circ_0001686 promotes prostate cancer progression by

upregulating SMAD3/TGFBR2 via miR-411-5p15. Additionally, numerous drugs have been found to regulate tumor proliferation and metastasis through SMAD3, including CDK4 inhibitors and

doxorubicin, which induce breast cancer cell apoptosis via SMAD3. Docetaxel inhibits SMAD3 binding to the HIF-1α gene promoter, thereby curtailing the transcriptional activation of HIF-1 and

inhibiting cell proliferation16,17. In summation, the significance of SMAD3 in the development of multiple cancers is gaining increasing recognition, rendering it a promising candidate as a

novel diagnostic and prognostic biomarker and therapeutic target for these malignancies. In this study, we conducted a comprehensive bioinformatics analysis of SMAD3-related data. Utilizing

a range of databases including HPA, cBioPortal, UALCAN, GSCA, GEPIA, CancerSEA, TISMO, TISCH, and others, we assessed the expression levels of SMAD3 across various tissues and its

associations with different cancer types. Furthermore, we delved into the prognostic significance, mutation patterns, and single-cell expression profiles of SMAD3. Our analysis extended to

exploring the connections between SMAD3 and immune cell infiltration, immune-related genes, immune checkpoints, immunomodulatory genes, methylation patterns, tumor mutation burden (TMB),

microsatellite instability (MSI), tumor stemness, and drug sensitivity. Additionally, we conducted functional enrichment analysis of genes related to SMAD3 to unveil the underlying molecular

mechanisms contributing to diverse cancers. This study collectively sheds light on the potential of SMAD3 as a prognostic marker in various cancer contexts. Our in-depth in vitro

experiments have provided substantial evidence regarding the atypical expression and biological roles of SMAD3 in Liver Hepatocellular Carcinoma (LIHC). The findings firmly establish SMAD3

as an independent and promising prognostic marker, underscoring its potential as a fresh therapeutic target for LIHC. MATERIALS AND METHODS GENE EXPRESSION ANALYSIS OF SMAD3 IN PAN-CANCER

The TIMER2.0 database18 stands as an intricate network for the assessment of immune cell infiltration within tumor tissues. It not only facilitates the scrutiny of immune cell infiltration

levels in tumors but also enables the comparative examination of SMAD3 expression profiles across various cancer types and their adjacent normal tissues. The unified and standardized

pan-cancer dataset was acquired through retrieval from the UCSC database19.Subsequently, the SMAD3 gene expression data were meticulously extracted from each sample, resulting in

comprehensive profiles across 27 distinct cancer types. Comparative analyses involved assessing expression levels in cancer samples versus paired standard samples across the same 27 cancers.

Subsequently, log2 transformation and t-tests were conducted on the expression data within each tumor type. Significance of expression discrepancies between tumor and normal tissue samples

was determined using the standard criterion of p-value < 0.05. Data analysis utilized R software (Version 4.0.2, https://www.Rproject.org), with visualization accomplished using the

“ggplot2” R package. The Human Protein Atlas (HPA) constitutes a repository of human proteins within the framework of diverse anatomical compartments, encompassing organs, tissues, and

cellular constituents, leveraging a multitude of omics methodologies. The expression of SMAD3 mRNA in normal tissues was detected using the HPA database. In addition, histological and

pathological immunohistochemical images of the SMAD3 protein were obtained from the database, and the subcellular localization of SMAD3 was determined through the utilization of indirect

immunofluorescence microscopy20,21. PROGNOSTIC ANALYSIS OF SMAD3 Our data acquisition process entailed the retrieval of a meticulously standardized pan-cancer dataset originating from the

UCSC database, specifically the TCGA TARGET GTEx dataset, which comprised a substantial cohort (PAN-CANCER _N_ = 19131 G = 60499). Within this dataset, we meticulously extracted the gene

expression data for SMAD3 from each sample. In addition, we secured a high-quality TCGA prognostic dataset, sourced from prior TCGA prognostic studies as published in Cell22. To complement

this dataset, we incorporated the follow-up data obtained from TARGET through UCSC.our study encompassed the examination of various endpoints, including overall survival (OS),

disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI), to scrutinize the association between SMAD3 expression and cancer prognosis. For this

purpose, we harnessed the robust analytical tools of Kaplan-Meier survival analysis and Cox regression analysis to delve into the intricate relationship between SMAD3 and survival

prognosis23 . DNA METHYLATION AND RNA MODIFICATION ANALYSIS The occurrence and proliferation of cancer are regulated by epigenetic inheritance and genetic events, in which DNA methylation

plays an important role24. We used data from the UALCAN database to determine SMAD3 promoter DNA methylation levels to determine the difference between the cancers and normal tissues. We

also obtained DNA methylation prognostic data from the GSCA database. More and more evidence show that RNA methylation is closely related to cancer cell proliferation, cell stress,

metastasis, and the immune response. RNA methylation-related proteins have become promising targets for cancer therapy25. Pan-cancer data were retrieved from the UCSC database, and from this

dataset, we extracted the expression profiles of the SMAD3 gene along with 44 marker genes related to Class III RNA modification (comprising m1A10, m5C13, and m6A21 genes) within each

sample. Subsequently, Pearson correlation coefficients were computed to elucidate the associations between SMAD3 and these marker genes within the context of five distinct immune pathways.

GENE MUTATION ANALYSIS OF SMAD3 The cBioPortal database26 serves as a repository for the comprehensive compilation of data pertaining to mutation frequency, mutation types, specific mutation

locations, and the three-dimensional (3D) structural attributes of prospective proteins across the entirety of TCGA cancer datasets. We downloaded the uniformly standardized pan-cancer

dataset from the UCSC database. In addition, we also downloaded the copy number variation dataset at the level-4 gene level for all TCGA samples by GISTIC27 software from the GDC. We

integrated the copy number data and gene expression data of the samples and analyzed the CNV mutation differences between pan-cancers by using R software. IMMUNE-RELATED CHARACTERISTICS OF

SMAD3 We conducted an extensive analysis of immune cell infiltration by leveraging the resources available in the TIMER 2.0 database. Leveraging a range of algorithms, including TIMER,

XCELL, EPIC, CIBERSORT, TIDE, QUANTISEQ, and others, we meticulously examined the associations between SMAD3 expression and diverse immune cell subsets. These subsets encompassed CD8 + T

cells, CD4 + T cells, B cells, macrophages, and T cell regulators (Tregs) within distinct TCGA cancer types. We extracted the expression data of SMAD3 in each sample from the TCGA TARGET

GTEx database, and calculated stromal, immune, and estimate scores according to gene expression by using the R software package ESTIMATE28. In addition, expression data of SMAD3 gene and 150

marker genes of five types of immune pathways (chemokine41, receptor18, MHC21, immunoinhibitor24 and immunostimulator45) were extracted from the database, Correlative studies on SMAD3

expression with immunoregulatory genes were also discussed by Spearman analysis. In our study, we harnessed Pearson correlation analysis to assess the associations between SMAD3 gene

expression and key parameters, including immune checkpoints (ICP), tumor mutation burden (TMB), microsatellite instability (MSI), and tumor stemness. Additionally, we conducted a comparative

analysis of SMAD3 expression levels in different tumor cell lines before and after immune checkpoint blockade (ICB) treatment, as well as between responders and non-responders. These

analyses were carried out using data from the TIDE database.Furthermore, we examined the correlation between SMAD3 expression and overall survival (OS) within the human immunotherapy cohort

and evaluated response outcomes utilizing the TISMO database29. SINGLE-CELL FUNCTIONAL ANALYSIS OF SMAD3 TISCH, an extensive and meticulously curated database, amalgamates the single-cell

transcriptome profiles of approximately 2 million cells sourced from high-caliber tumor datasets spanning 27 distinct cancer types30. We acquired the single-cell expression data of SMAD3

encompassing various pan-cancer datasets from the database. Our analysis delved into the single-cell composition of SMAD3 expression, and we meticulously examined this within the context of

three distinct datasets. CancerSEA is a database that studies tumor cell function at the single-cell level31. In our research, we mapped the correlations between SMAD3 expression and diverse

tumor functions by leveraging single-cell sequencing data. Furthermore, we provided a demonstration of the spatial transcriptomic characteristics specific to Breast Cancer (BRCA) and

Prostate Adenocarcinoma (PRAD) based on data sourced from the SpatialDB database30. CO-EXPRESSED GENES AND ENRICHMENT ANALYSIS OF SMAD3 Our research methodology involved several key steps.

Initially, we procured a set of 50 Smad3-interacting genes through the STRING website. Subsequently, to acquire a practical list of Smad3-binding proteins, we utilized the “similar gene

detection” feature of GEPIA2. This process relied on the TCGA tumor and normal tissue dataset, from which we extracted the first 100 SMAD3-related genes. Concurrently, we conducted a Pearson

correlation analysis to further refine the selection of genes that exhibited a significant correlation with SMAD3. Moving forward, we conducted comprehensive Gene Ontology (GO) and Kyoto

Encyclopedia of Genes and Genomes (KEGG) enrichment analyses32,33,34. Additionally, transcription factor enrichment among associated genes was explored using the Metascape database. SMAD3

promoter sequence was obtained from NCBI, and candidate transcription factor protein sequences were acquired from UniProt. Interactions with SMAD3 promoters were predicted using AlphaFold

3.0. Transcription factor binding sites were predicted using the JASPAR website (https://jaspar.elixir.no/).focusing on the top ten genes associated with SMAD3 across pan-cancer datasets35,

p-value < 0.05 was considered to be statistically significant. GSEA ENRICHMENT ANALYSIS AND DRUG SENSITIVITY PREDICTION OF SMAD3 Within the PAAD and LIHC groups, we harnessed Gene Set

Enrichment Analysis (GSEA) to probe the biological signaling pathways. In this analysis, we focused on the top 5 terms derived from both the KEGG and HALLMARK databases. Notably, we

considered KEGG pathways with notable enrichment results, as indicated by the enrichment score (ES) and gene ratio. Specifically, gene sets with |NES| ≥ 1 and NOM _p_ ≤ 0.05 were deemed to

be significantly enriched36. In our quest to explore the nexus between drug sensitivity and the expression of SMAD3, we engaged in a rigorous drug sensitivity analysis employing the GSCALite

platform. For this endeavor, Spearman correlation analysis served as our indispensable tool. Within our analytical framework, a positive correlation unveiled that neoplastic cell

characterized by heightened SMAD3 expression displayed an elevated proclivity towards drug resistance, whereas a negative correlation signified that those same cells manifested an increased

receptivity to pharmacological agents. CELL CULTURE AND TRANSFECTION The human Liver Hepatocellular Carcinoma (LIHC) cell lines JHH-5, HepG2, SK-HEP-1, Hep3B, Hu-7, LI-7, and LM3 were

procured from the Shanghai Institute of Cell Biology, China. The immortalized liver cell line HL-7702 was obtained from Shanghai Fuxiang Biotechnology Co., Ltd. These cell lines were

cultivated in DMEM (Gibco) supplemented with 10% FBS (Hyclone) and exposed to 5% carbon dioxide environment at 37 °C. We acquired SMAD3 small interfering RNA (siRNA) from GenePharma in

Shanghai, China. In LM3 cells, the multiplicity of infection (MOI) was set at 10. To enhance transfection efficiency, Polybrene was employed, and positive cells were subsequently selected

using puromycin screening. WESTERN BLOT ANALYSIS The Ethics Committee of the Second Affiliated Hospital of Nanchang University granted authorization for the utilization of human tissue in

this research. To extract cellular and human tissue lysates, a radioimmunoprecipitation assay buffer (Solarbio, China), fortified with a cocktail of protease inhibitors, was employed.

Subsequently, a 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis technique was implemented to facilitate the separation of the resulting pyrolysis products. These products were

subsequently transferred onto polyvinylidene fluoride (PVDF) membranes, followed by incubation with primary antibodies. Notably, the primary antibodies employed included SMAD3 (1:1000,

66516-1-Ig, Proteintech, China) and GAPDH (1:2000, 60004-1-Ig, Proteintech, China). In the subsequent steps, PVDF membranes were subjected to further incubation with relevant secondary

antibodies. Ultimately, the membranes were exposed to an enhanced chemiluminescence (ECL) substrate (Proteintech, China), and the discernible protein bands were documented via the SH-Cute

523 imaging system for analysis. WOUND HEALING AND TRANSWELL EXPERIMENTS Wound Healing Assay: Hu7, LM3, and Hep3B cells, at a cell density of 2.5 × 105 per well, were seeded into 6-well

plates. Subsequently, transfection of siSMAD3#1 and siSMAD3#2 was conducted in these plates. After a 24-hour incubation period, a 200 mL pipetting head was employed to create cell scratches

within the plates. The cells were then provided with serum-free medium. Subsequent images were captured at intervals of 0 h, 12 h, and 24 h using an inverted microscope (IX81, Olympus

Company, Japan). Transwell Assay: Cells were suspended and enumerated using serum-free medium. These cells were then introduced into the upper chamber at a density of 5 × 104, while the

lower chamber received 500 mL of full medium. Following a 24-hour incubation period, the cells were fixed with 4% paraformaldehyde for 10 min. Subsequently, 1% crystal violet was utilized to

stain the cells for a period of 10 s. Any remaining cells in the upper chamber were gently removed, and images were captured using a brightfield microscope (BX53, Olympus Company, Japan).

Furthermore, the number of cells that had traversed the chamber was counted across four randomly selected fields under the microscope. EDU ASSAY The transfected Hu-7, LM3, and Hep3B cells,

with a seeding density of 2 × 104, were plated in 24-well plates and subsequently incubated for a duration of 72 h. Following this incubation period, the cells were subjected to culture with

EdU reagent for a duration of 2 h. Following these steps, cellular specimens were meticulously subjected to fixation employing a solution of 4% paraformaldehyde and 0.5% Triton X-100.

Subsequent to the fixation process, the specimens underwent staining procedures utilizing Hoechst stain. Quantitative assessment of the EdU inclusion rate was accomplished through the

utilization of ImageJ software. STATISTICAL ANALYSIS Through the online databases mentioned above, the statistical analysis was automatically computed in this study. These results were

considered as statistically significant at *_P_ < 0.0001. RESULTS ANALYSIS OF GENE EXPRESSION Figure 1 presents a flowchart illustrating the complete research process. In the HPA

database, we conducted an examination of SMAD3 expression in various normal tissues. The results of our analysis revealed heightened levels of SMAD3 mRNA expression in several tissues,

including the esophagus, skeletal muscle, bladder, tongue, choroid plexus, ovary, and skin (Fig. 2A). Furthermore, we performed an analysis of SMAD3’s differential expression in both tumor

and normal tissues by leveraging the TIMER 2.0 database. Our investigation revealed a substantial upregulation of SMAD3 expression in six cancer types, including ESCA, STAD, LUSC, LIHC,

CHOL, and HNSC. Conversely, we observed significant downregulation in seven other cancers, specifically COAD, BRCA, PRAD, UCEC, KIRC, THCA, and SKCM (Fig. 2B). Simultaneously, we extracted

SMAD3 expression data from normal tissues in the GTEx database and integrated it with the data from TCGA. Our analysis unveiled a remarkable up-regulation of SMAD3 in 15 cancer types, which

include ACC, CHOL, COAD, ESCA, GBM, HNSC, KICH, LAML, LGG, LIHC, LUAD, LUSC, PAAD, STAD, and TGCT. In contrast, we observed significant down-regulation in 7 other cancer types, specifically

BRCA, KIRC, OV, PRAD, SKCM, UCEC, and UCS (Fig. 2C). Additionally, we conducted a paired difference analysis for SMAD3 expression across pan-cancer datasets. Our analysis revealed a

significant increase in SMAD3 expression in LIHC, LUSC, OSCC, CHOL, STAD, and HNSC. Conversely, a decrease in SMAD3 expression was observed in BRCA, UCEC, PRAD, and COAD (Fig. 2D). We

employed R software to compute the variations in gene expression across tumor samples at different clinical stages. Utilizing an unpaired Student’s t-Test, we performed a significance

analysis of the differences between pairs of samples. In addition, we conducted variance analysis to test the differences among multiple groups of samples. Our observations indicated

significant differences in eight types of tumors, including LUAD, BRCA, KIPAN, KIRC-PAAD, OV, TGCT, and BLCA (Fig. 2E-F). Furthermore, we acquired the IHC results from the HPA database to

corroborate the protein expression of SMAD3. As depicted in Fig. 2G-H, tumor tissues displayed substantial SMAD3 IHC staining, whereas normal tissues from the liver, lung, pancreas, and

brain exhibited low to moderate staining. Notably, these findings align with the respective levels of gene expression. To gain insight into SMAD3’s subcellular localization, we conducted

immunofluorescence localization in A-549 and U251-MG cells. Our observations indicated that SMAD3 predominantly localized within the nucleus and cytosols. PROGNOSTIC VALUE OF SMAD3 We

employed Overall Survival (OS), Disease-Specific Survival (DSS), and Progression-Free Interval (PFI) to investigate the prognostic implications of SMAD3 across various cancer types. Notably,

high expression of SMAD3 was associated with unfavorable outcomes in LAML, PAAD, ACC, UVM, and LUAD (Fig. 3A-B). Conversely, SMAD3 exhibited high expression in GBMLGG, KIRC, and KIPAN,

which correlated with favorable OS. In terms of DSS, SMAD3 was highly expressed in ACC, PCPG, LUAD, and PAAD, and this high expression was associated with shorter DSS (Supplementary Fig. 1S

A-B). Additionally, our results revealed that SMAD3 was overexpressed in ACC, PAAD, STAD, UVM, LIHC, and STES, and this overexpression was linked to a shorter PFI (Supplementary Fig. 2S

A-B). Consequently, SMAD3 overexpression was particularly prominent in PAAD and ACC, where OS, DSS, and PFI were notably abbreviated. CORRELATION OF SMAD3 EXPRESSION WITH DNA METHYLATION AND

RNA MODIFICATION The UALCAN online tool and GSCA database furnished a valuable platform for our exploration of SMAD3 promoter methylation levels across different patient groups and normal

populations in various cancer types. Notably, our findings indicated a considerable increase in the promoter methylation levels of SMAD3 in eight tumor groups (BRCA, READ, HNSC, KIRP, UCEC,

PRAD, COAD, KIRC) compared to their respective normal counterparts (Fig. 4B). Conversely, in seven tumor groups (LUAD, LUSC, TGCT, CHOL, THCA, BLCA, LIHC), the SMAD3 methylation levels were

significantly lower than those observed in normal groups. Moreover, we evaluated the impact of SMAD3 methylation expression on the survival prognosis using the GSCA database. The results

were rather promising, demonstrating that high SMAD3 methylation expression tended to predict a more favorable prognosis in nearly all statistically significant prognostic analyses (Fig. 4C,

Supplementary Fig. 3SD-G). This suggests that SMAD3 may influence the prognosis of cancer patients through its methylation status. Furthermore, we observed a positive correlation between

SMAD3 expression and m6A, m5C, and m1A regulatory genes in most cancers, with the exception of NB and CHOL (Fig. 4A). This intriguing finding implies that SMAD3 may play a role in

carcinogenesis by affecting the expression of RNA methylation regulatory genes. SMAD3 MUTATION IN PAN-CANCER Our analysis of genetic alterations across various cancers, conducted through the

cBioPortal platform, allowed us to delve into the landscape of SMAD3 gene variations in pan-cancer. The results unveiled distinct patterns in different malignancies, with COAD exhibiting

the highest frequency of SMAD3 variations at 6.40%, primarily in the form of mutations. Following COAD, the top five cancers with the highest variation frequencies were UCEC (5.48%), SKCM

(3.83%), MESO (3.45%), and KICH (3.08%). Notably, MESO was unique in its pattern of variation, characterized by amplifications (Fig. 5A). In further exploration of these variations, we

identified missense and truncating mutations as the primary mutation types affecting SMAD3, as depicted in Fig. 5B. These insights into the genetic alterations in SMAD3 shed light on its

role in the context of different cancers. Five independent studies conducted on endometrial, lung, gastric, and colorectal adenocarcinomas revealed that R268C/H missense mutations underwent

changes within the MH2 domain. Additionally, SMAD3 mutations were correlated with various post-translational modifications, including phosphorylation, acetylation, ubiquitination, and

malonylation. The alterations in the conformation of the SMAD3 protein resulting from the R268C/H mutations are visually depicted in Fig. 5C. Changes in copy number in parts of the genome

are known to be a feature of many cancers37. We utilized the R software to compute disparities in tumor gene expression across clinical stage-specific samples. Significance analysis for

pairwise disparities was conducted using unpaired Wilcoxon rank sum and signed rank tests, while the Kruskal test was employed to assess differences among multiple sample groups. Figure 5D

illustrates notable disparities in copy number variations (CNVs) across 14 distinct cancer types (GBMLGG, LGG, CESC, COAD, COADREAD, BRCA, STES, SARC, STAD, HNSC, LIHC, READ, SKCM, UCS).

ANALYSIS OF IMMUNE CELL INFILTRATION AND IMMUNOINFILTRATION OF SMAD3 The UCSC database was used to download a unified and standardized pan-cancer dataset, and the R software package ESTIMATE

was used to calculate stromal, immune, and ESTIMATE scores for each patient in each tumor based on gene expression. To ascertain the statistically significant associations pertaining to

immune infiltration scores, we computed the Spearman’s correlation coefficient between gene expression and immunoinfiltration scores for each tumor sample. This analysis was conducted by

means of the corr.test function within the R package. The results showed that LGG, COAD, COADREAD, PRAD, NB, READ, and UVM showed a significant positive correlation among the three immune

infiltration scores. At the same time, STES, KIRP, KIPAN, HNSC, KIRC, LUSC, LIHC, WT, BLCA, THCA, and ACC were all negatively correlated (Supplementary Fig. 4S、Figure 5S、Figure 6S). In

recent investigations, a compelling body of evidence has emerged, affirming the intricate relationship between immune cell infiltration and the genesis, progression, and metastatic cascade

of an array of malignancies38,39. In the quest to discern the interplay of SMAD3 expression with the presence of tumor-infiltrating immune cell populations, we conducted a comprehensive

analysis utilizing the TIMER 2.0 database. Employing a repertoire of algorithms, such as TIMER, EPIC, QUANTISEQ, XCELL, CIBERSORT, TIDE, and others, we unveiled noteworthy findings. Our

analysis revealed a significant positive correlation between SMAD3 expression and the infiltration levels of various immune cell types, including B cells, cancer-associated fibroblasts

(CAFs), NK cells, neutrophils, monocytes, and endothelial cells (Endo). Interestingly, there was no significant immune-related association between SMAD3 expression and macrophages,

progenitor cells, eosinophils, hematopoietic stem cells (HSCs), myeloid-derived suppressor cells (MDSCs), follicular helper T cells (FhTs), dermal epithelial cells, and mast cells. Moreover,

our investigation identified a strong positive correlation between SMAD3 expression and CD4 + T cell infiltration in prostate adenocarcinoma (PRAD), liver hepatocellular carcinoma (LIHC),

cervical squamous cell carcinoma (CESC), and diffuse large B-cell lymphoma (DLBC). Conversely, we observed a significant negative correlation between SMAD3 expression and CD8 + T cell

infiltration in glioblastoma (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal papillary cell carcinoma (KIRP), low-grade glioma (LGG), and lung squamous cell carcinoma

(LUSC). as illustrated in Fig. 6. RELATIONSHIP BETWEEN SMAD3 AND IMMUNE CHECKPOINTS, IMMUNE MODULATORS In recent years, immune checkpoint inhibitors (ICIs) have achieved significant

milestones in the realm of cancer immunotherapy. However, a substantial number of patients continue to exhibit either a lack of response or develop resistance to immune checkpoint

inhibitors40. We observed that SMAD3 displayed associations with multiple immune checkpoint inhibitors and immune checkpoint stimulators. Subsequently, we conducted an investigation into the

relationships between SMAD3 and immune checkpoints as well as immune modulators. Across the majority of cancer types, SMAD3 expression displayed a substantial association with the

expression profiles of established immune checkpoint molecules, a trend that was consistently observed. Notable exceptions to this pattern included kidney chromophobe (KICH), esophageal

carcinoma (ESCA), acute lymphoblastic leukemia (ALL), cholangiocarcinoma (CHOL), and mesothelioma (MESO), where the correlation between SMAD3 expression and immune checkpoint markers was not

statistically significant. The encompassed immune checkpoints encompassed lymphocyte-activation gene 3 (LAG3), CD40, cytotoxic T-lymphocyte-associated protein 4 (CTLA4), CD80, programmed

cell death 1 (PDCD1), T cell immunoreceptor with Ig and ITIM domains (TIGIT), CD86, and tumor necrosis factor receptor superfamily, member 9 (TNFRSF9) (Fig. 7A). These findings portend a

prospect of synergy between SMAD3 and established immune checkpoint molecules. To appraise the influence of SMAD3 expression on immunotherapeutic outcomes, we conducted a comparative

analysis of SMAD3 expression levels in distinct tumor cell lines, both antecedent to and post-administration of immune checkpoint blockade (ICB) interventions, and across respondent and

non-respondent subpopulations. Eminent among our observations is the salient decrease in SMAD3 expression within CT26 and T11 cells subsequent to anti-CTLA4 and anti-PD-1 treatments.

Furthermore, a conspicuous reduction in SMAD3 expression was discerned in CT26 cells displaying favorable responses to anti-PDL-1 treatment, alongside the corresponding downregulation

observed in YTN16 cells that manifested a positive response to anti-CTLA4 intervention (Fig. 7C). Subsequently, we proceeded to assess the relationship between SMAD3 expression and the

resultant response patterns, as well as overall survival (OS), within the human immunotherapy cohort (Fig. 7B). The findings unveiled that, within the ambit of 24 immunotherapy cohorts,

SMAD3 emerged with an Area Under the Curve (AUC) value exceeding 0.5 in nine instances, denoting its predictive utility. Remarkably, SMAD3 exhibited superior predictive efficacy when

contrasted with Tumor Mutation Burden (TMB), T cell clonality (T clonality), and B cell clonality (B clonality). Nevertheless, SMAD3’s predictive performance, while notable, was surpassed by

the predictive value of the CD274 score (AUC > 0.5 in 21 immunotherapy cohorts), the CD8 score (AUC > 0.5 in 18 immunotherapy cohorts), the Interferon Gamma (IFNG) score (AUC > 0.5

in 17 immunotherapy cohorts), and the Merck 18 score (AUC > 0.5 in 16 immunotherapy cohorts). In the aggregate, SMAD3 expression emerges as a potent biomarker, signifying its efficacy in

the anticipation of immunotherapeutic responses. Finally, an examination of SMAD3 expression levels across various datasets revealed its pronounced upregulation in METABRIC,

ICB-Lauss2017-ACT, Patel 2017, and MDSC, thereby emphasizing its salient role in these contexts (Fig. 7D). We additionally conducted an analysis of the correlation between SMAD3 and immune

regulatory genes across 40 tumor types. This analysis encompassed Major Histocompatibility Complex (MHC), immunostimulator genes, immunoinhibitor genes, chemokines, and chemokine receptors,

aimed at gaining deeper insights into the interplay between SMAD3 and immune regulatory genes within the context of cancer. The results revealed a robust association between SMAD3 expression

and immune regulatory genes in malignant tumors (Supplementary Fig. 7S). RELATIONSHIP BETWEEN SMAD3 EXPRESSION AND TUMOR STEMNESS, AND GENOMIC HETEROGENEITY In addition to metastasis and

heterogeneity, it is well-established that immune checkpoint gene expression and immune system cell infiltration are intricately linked to tumor stemness38. Our analysis of SMAD3 expression

alongside tumor stemness markers, DNAss and RNAss, revealed noteworthy findings. We observed significant negative correlations between SMAD3 expression and DNAss in four tumor types,

specifically COAD, COADREAD, BRCA, and UCEC. Conversely, significant positive correlations between SMAD3 expression and DNAss were identified in several tumor types, including CESC, STES,

THYM, THCA, UVM, and HNSC, as depicted in Fig. 7G. Furthermore, our analysis unveiled significant negative correlations between SMAD3 expression and RNAss in 18 tumor types, encompassing

LGG, COADREAD, LAML, BRCA, KIRP, STAD, PRAD, UCEC, THYM, LIHC, THCA, READ, OV, TGCT, UCS, BLCA, and CHOL, as illustrated in Fig. 7H. Cancer stem cells (CSCs) are a population of

self-renewing cells that play a pivotal role in tumor development, facilitate metastasis, and contribute to increased resistance to cancer treatments41. It is important to note that the

impact of immunotherapy can be influenced by tumor heterogeneity. Therefore, we conducted an examination of the Pearson’s correlation coefficients between SMAD3 and tumor heterogeneity

indicators, including Tumor Mutational Burden (TMB) and Microsatellite Instability (MSI). The analysis revealed that SMAD3 expression was significantly negatively correlated with TMB in

tumor types such as GBMLGG, BRCA, LUAD, KIPAN, PRAD, KIRC, THCA, SKCM, BLCA, and DLBC. Conversely, SMAD3 expression exhibited a significant positive association with TMB in SARC, HNSC, ACC,

THYM, and PAAD (Fig. 7E). Furthermore, the Microsatellite Instability (MSI) displayed a positive correlation with SMAD3 expression in tumor types including GBMLGG, COAD, SARC, KIPAN, LUSC,

and UVM. Conversely, a negative correlation was observed between MSI and SMAD3 expression in BRCA, PRAD, HNSC, THCA, and DLBC (Fig. 7F). SINGLE-CELL ANALYSIS OF SMAD3 Utilizing the TISCH

database, we conducted an in-depth exploration of SMAD3’s single-cell expression patterns across diverse cancer types. Our analysis unveiled that SMAD3 exhibited notably high expression

levels in various cell types, including CD4Tconv, Treg, Tprolif, CD8T + cells, CD8Tex, B cells, dendritic cells (DC), endothelial cells, and fibroblasts. Particularly striking was the high

expression of SMAD3 in monocytes (Mono) and macrophages (Macaro) (Fig. 8A). To delve deeper, we acquired precise SMAD3 expression data within specific cell groups from liver hepatocellular

carcinoma (LIHC), prostate adenocarcinoma (PRAD), and pancreatic adenocarcinoma (PAAD). This information was sourced from datasets GSE125449, GSE143791, and GSE111672. Our analysis revealed

the expression of SMAD3 within key cell groups, including CD8 + T cells, monocytes/macrophages, and endothelial cells (Fig. 8B). Our exploration extended to the investigation of SMAD3’s

functions at the single-cell level using the CancerSEA database. The heatmap results unveiled significant insights into the correlations between SMAD3 and various biological processes. SMAD3

exhibited positive correlations with critical processes such as angiogenesis, hypoxia, inflammation, metastasis, and stemness across diverse cancer types. In contrast, SMAD3 displayed

negative correlations with processes related to DNA damage, DNA repair, invasion, and quiescence (Fig. 8C). In addition, our investigation extended to the SpatialDB database, revealing that

SMAD3 is consistently expressed in a majority of monocyte- and macrophage-rich tumors. Our analysis determined that SMAD3, in conjunction with monocyte/macrophage markers such as CD68 and

CD14, exhibited varying levels of spatial transcriptomic expression patterns in breast cancer (BRCA) and prostate adenocarcinoma (PRAD) (Fig. 8D). ENRICHMENT OF SMAD3-RELATED PARTNERS A

total of 50 SMAD3-binding proteins were identified, each supported by experimental data acquired through the STRING tool, which illustrates the interaction network of these proteins (Fig. 

9A). Utilizing the GEPIA2 tool to integrate TCGA pan-cancer expression data, we identified the top 100 genes that exhibit a correlation with SMAD3 expression. Among these correlated genes,

SMAD3 expression displayed positive associations with the top 10 genes, namely, BICD2 (correlation coefficient, _R_ = 0.5), FAM160A1 (_R_ = 0.52), KLF3 (_R_ = 0.49), MAPK6 (_R_ = 0.48),

SEMA4B (_R_ = 0.52), SLK (_R_ = 0.48), TMOD3 (_R_ = 0.52), TP63 (_R_ = 0.53), ZDHHC5 (_R_ = 0.49), and PCYT1A (_R_ = 0.48), with all correlations being statistically significant (_P_ < 

0.001) (Fig. 9J). In an intersection analysis, two of the aforementioned genes, SMURF1 and SP1, were identified as belonging to both the SMAD3-binding proteins and the top correlated genes

with SMAD3 expression (Fig. 9B). To perform KEGG and GO enrichment analyses, the two datasets were combined. KEGG data suggest that the effect of SMAD3 on tumor pathogenesis, which was

depicted in Fig. 7C, may involve the “TGF-beta signaling pathway,” “Transcriptional misregulation in cancer,” and “Human T-cell leukemia virus 1 infection.” (Fig. 9C). Additionally, we

performed GO enrichment analysis and discovered that SMAD3-interacting genes are related to the biological processes “reaction to endogenous stimuli,” “positive regulation of nucleic

acid-templated transcription,” and “positive regulation “of biological process (BP) (Fig. 9D). Second, we found that Smad3-related genes were primarily enriched in “nuclear part,” “nuclear

lumen,” and “nucleoplasm” in the cellular component (CC) (Fig. 9E), and that “transcription regulatory region DNA binding”, “regulatory region nucleic acid binding” and “double-stranded DNA

binding” were enriched in cellular function (MF) (Fig. 9F). Additionally, we assessed Smad3-related genes using the Metascape database and conducted transcription factor enrichment analysis.

The top three enriched transcription factors identified were “GGGYGTGNY UNKNOWN,” “PAX Q6,” and “CEBPB 01” (Fig. 9G). Using AlphaFold 3.0, we predicted the molecular docking of CEBPB with

the SMAD3 promoter, and using PyMOL software to visualize protein-DNA interactions, the docking result is: Docking Score: -194.16, Confidence Score: 0.7612 we identified specific binding

sites of CEBPB on the SMAD3 promoter using the JASPAR website. The highest sequence scores were observed at positions − 1794 to -1785 and − 1523 to -1514 (Fig. 9H-I). GSEA ENRICHMENT OF

SMAD3 AND RELATIONSHIP BETWEEN SMAD3 EXPRESSION AND DRUG SENSITIVITY We performed functional enrichment analysis of SMAD3 expression in pancreatic adenocarcinoma (PAAD) and liver

hepatocellular carcinoma (LIHC) using Gene Set Enrichment Analysis (GSEA). In PAAD, SMAD3 expression was predominantly associated with KEGG enrichment terms related to Glycine, Serine, and

Threonine Metabolism, the Renin-Angiotensin System, Apoptosis, and Neuroactive Ligand Receptor Interaction (Fig. 10A). Additionally, Apoptosis, Notch Signaling Pathway, TNFA Signaling VIA

NFKB, P53 Pathway, and Interferon Gamma Response in PAAD were all linked to SMAD3 in HALLMARK terms (Fig. 10C). According to KEGG enrichment terms, Complement and Coagulation, Prostate

Cancer, Ubiquitin Mediated Proteolysis, ERBB Signaling Pathway and Oocyte Meiosis are primarily associated with SMAD3 expression in LIHC (Fig. 10B). In LIHC, the analysis of SMAD3 expression

revealed significant associations with HALLMARK enrichment terms. Specifically, SMAD3 was linked to processes such as PI3k AKT MTOR Signaling, Wnt-β Catenin Signaling, Mitotic Spindle, Heme

Metabolism, and Unfolded protein response (Fig. 10D). We assessed the drug sensitivity associated with SMAD3 expression in various cancers using GSCALite. The analysis revealed that the 50%

inhibitory concentration (IC50) values of nine drugs, namely, 17-AAG, Dasatinib, Docetaxel, Lapatinib, PD-0325901, RDEA119, Trametinib, Selumetinib, and Bleomycin, exhibited an inverse

correlation with KIF18A expression. On the other hand, the IC50 values of 15 different medications, including AR-42, BX-912, CAY10603, FX866, GSK1070916, I-BET-762, KIN001-102,

NPK76-II-72-1, Navitoclax, QL-XL138, THZ-2-102-1, UNC0638, Vorinostat, WZ3105, and YM201636, were positively correlated with SMAD3 expression (Fig. 10E). SMAD3 PROMOTES THE PROLIFERATION AND

MIGRATION OF LIVER CANCER CELLS The protein expression levels of SMAD3 in Liver Hepatocellular Carcinoma (LIHC) tissues exhibited a notable elevation, with the outcomes meticulously

analyzed utilizing ImageJ software (Fig. 11A). We extended our scrutiny to assess the protein expressions of SMAD3 in a spectrum of LIHC cell lines, encompassing JHH-5, HepG2, SK-HEP-1,

Hep3B, Hu7, Li-7, and LM3. Strikingly, we observed that the expression of SMAD3 in the Hep3B, Hu7, and LM3 cell lines surpassed that in other LIHC cell lines (Fig. 11B). Subsequently, we

employed specific siRNAs targeting SMAD3 to achieve knockdown of SMAD3 expression in the Hep3B, Hu7, and LM3 cells (Fig. 11C, D, E). Following this, In our pursuit of unraveling the

intricate interplay between LIHC cells and SMAD3 expression across discrete cohorts, we embarked on a series of functional experiments. Notably, the outcomes derived from EDU assays, wound

healing assays, and Transwell assays synergistically unveiled a compelling narrative. The act of SMAD3 knock-down ushered forth a marked diminution in the proliferation and migratory

potential of Hep3B, Hu7, and LM3 cells (Fig. 11F, G, H). These findings compellingly underscore the promoting role of SMAD3 in the proliferation and migration of LIHC cells. DISCUSSION The

similarities and differences between tumors can be shown by pan-cancer analysis, which can support to the theoretical framework guiding efforts in cancer prevention, the design of

therapeutic targets, and the prospective screening of therapeutic drugs42. SMAD3 is central to the TGF-β pathway and plays an important role in immune-inflammatory responses. Recent research

suggests that SMAD3 is connected to the survival and prognosis of malignant tumors and plays a role in the etiology and advancement of colorectal cancer, lung cancer, breast cancer, bladder

cancer, prostate cancer, cervical cancer, gastric cancer, and osteosarcoma12,14,15,43,44,45,46. We embarked on an exhaustive and all-encompassing analysis of 33 different global cancer

types to discern the molecular characteristics of SMAD3. This analysis involved the utilization of diverse databases, including TCGA, GTEx, UALCAN, TIMER2.0, GSCA, and cBioportal. Our

investigation aimed to elucidate SMAD3’s role in tumor development and potential regulatory mechanisms, encompassing gene expression, prognosis, gene mutations, immunological infiltration,

single-cell analysis, immune checkpoints, DNA methylation, RNA methylation, tumor mutation burden (TMB), microsatellite instability (MSI), tumor characteristics, and drug sensitivity.

Previous research has demonstrated that SMAD3 has a role in the progression and development of tumors and is expressed differently in cancer47. we utilized data from the GTEx and HPA

databases to validate the tissue-specific expression of SMAD3. Our results indicate a significant increase in SMAD3 mRNA expression across most cancer types (including ACC, CHOL, COAD, ESCA,

GBM, HNSC, KICH, LAML, LGG, LIHC, LUAD, LUSC, PAAD, STAD, and TGCT) compared to normal samples. Combined pairwise difference analysis results, SMAD3 showed significantly increased

expression in LIHC, STAD, LUSC, CHOL, and HNSC, whereas it was decreased in BRCA, PRAD, and UCEC. These findings suggest that SMAD3 expression changes in different cancer types may be

influenced by various tumor biological characteristics, gene regulatory mechanisms and the tumor microenvironment. DNA methylation in tumors is widely acknowledged as a crucial epigenetic

regulatory mechanism in cancer development24. Specifically, hypermethylation of certain genes often leads to their downregulation or silencing. In this study, we also analyzed the DNA

methylation status of SMAD3 across multiple cancer types. We found that lower levels of DNA methylation in LIHC and CHOL were associated with increased SMAD3 expression, whereas higher

methylation levels in BRCA, PRAD, and UCEC correlated with decreased SMAD3 expression. This suggests that DNA methylation modifications of SMAD3 may play a significant role in these cancer

types. Further researches are needed to elucidate the potential function of SMAD3 methylation in different cancers. Furthermore, we explored the potential of SMAD3 as a biomarker for cancer

diagnosis and prognosis. By analyzing SMAD3 expression levels in cancer patients, we observed that high SMAD3 expression in tumor tissues correlated with poor prognosis. This association was

particularly pronounced in PAAD and ACC, where high SMAD3 expression was significantly linked to shortened overall survival (OS), disease-specific survival (DSS), and progression-free

interval (PFI). Therefore, SMAD3 may serve as a critical diagnostic and prognostic biomarker in personalized and precision cancer therapies, especially notable in PAAD and ACC. An integral

component of the tumor microenvironment (TME), tumor immunity is closely related to tumor immunity and drives tumor growth48,49,50. Data from the TIMER2.0 database showed a significant link

between SMAD3 expression and various immune cell infiltrations in different cancers. Our study also evaluated immune and stromal elements in the tumor microenvironment using immunological

and stromal scores, and indirectly assessed tumor purity through the ESTIMATE score51. The results showed a positive correlation with three immune infiltration scores in seven cancer types.

In contrast, negative associations were shown in 11 other cancer types. These findings suggest SMAD3 might play important roles in the importance of tumor immune microenvironment. Tumor

mutation burden (TMB), microsatellite instability (MSI), and programmed death-ligand 1 (PD-L1) are frequently employed as markers to anticipate the efficacy of immune checkpoint inhibitors

(ICIs). TMB and MSI strongly correlated with PD-1/PD-L1 inhibitor efficacy, and most clinical studies using TMB and MSI as predictive markers reached endpoints with significant success

rates52. Notably, high SMAD3 expression was positively correlated with increased TMB in GBMLGG, BRCA, LUAD, KIPAN, PRAD, KIRC, THCA, SKCM, BLCA, and DLBC, and decreased TMB in SARC, HNSC,

ACC, THYM, and PAAD. In addition, SMAD3 was positively correlated with microsatellite instability (MSI) expression in GBMLGG, COAD, SARC, SARC KIPAN, LUSC and UVM. Additionally, we

investigated the relationship between SMAD3 and tumor stemness. Cancer stem cells possess the unique ability to regenerate and generate a variety of tumor cells, making them indispensable in

the contexts of tumor survival, growth, metastasis, and recurrence53,54. Our analysis of SMAD3 expression and tumor stem cell markers DNAss and RNAss showed that SMAD3 was negatively

correlated with COADREAD, BRCA and UCEC. In terms of immune checkpoints, we found more than 30 immune checkpoints that were positively associated with most cancers, except KICH, ESCA, ALL,

CHOL, and MESO. Furthermore, our analysis shows that low expression of SMAD3 generally predicts a favorable response to immunotherapy. Taken together, these results demonstrate a close

relationship between SMAD3, TMB, MSI, MMR, DNAss, RNAss, and immune checkpoints, highlighting the potential of SMAD3 as a novel biomarker for predicting immune checkpoint inhibitor (ICI)

efficacy. However, the predictive abilities of TMB and MSI vary significantly across different types of cancer, warranting further research to validate these relationships. Using STRING and

GEPIA2 databases, we integrated and analyzed information on SMAD3-related genes and proteins.Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis can

analyze pivotal genes55. Our findings indicate a significant association of SMAD3 with key biological pathways including the “TGF-beta signaling pathway,” “Transcriptional misregulation in

cancer,” and “Human T-cell leukemia virus 1 infection.” Particularly noteworthy is SMAD3’s pivotal role within the TGF-beta signaling pathway, which is intricately linked to various aspects

of tumorigenesis56. This pathway notably influences tumor progression by modulating epithelial-mesenchymal transition, thereby impacting the migration and invasion capabilities of tumor

cells57.Furthermore, Bioinformatics analysis showed that SMAD3 has important diagnostic and prognostic significance in LIHC, and its expression may be related to the effectiveness of

immunotherapy for LIHC. Here, we use molecular biology techniques to verify the role of SMAD3 in LIHC. We demonstrate that SMAD3 expression is elevated in LIHC tissues and cell lines. SMAD3

knockdown inhibits proliferation and migration of hepatoma cells. These findings suggest that SMAD3 is involved in carcinogenesis of liver cancer and has therapeutic promise. Further

molecular studies are needed to elucidate the specific mechanism of SMAD3 in LIHC. In summary, SMAD3, as a signaling molecule, exhibits significant expression variations across different

types of tumors, profoundly influencing tumor development and treatment responses. According to data from the TIMER2.0 database, SMAD3 expression correlates significantly with immune cell

infiltration in various cancers, suggesting its potential role in the tumor immune microenvironment (TME). Additionally, SMAD3 expression correlates closely with immune checkpoint inhibitor

(ICI) treatment response predictors such as tumor mutational burden (TMB) and microsatellite instability (MSI). High SMAD3 expression positively correlates with increased TMB and MSI in

several cancers, while it correlates negatively in others, indicating complex relationships that warrant further investigation between SMAD3 expression patterns and TMB/MSI. Furthermore,

SMAD3’s DNA methylation status correlates with its expression changes, reflecting its involvement in intricate regulatory mechanisms during tumor development.In cancers like pancreatic

adenocarcinoma (PAAD) and adrenocortical carcinoma (ACC), elevated SMAD3 expression significantly correlates with poor prognosis, highlighting its potential as a biomarker for cancer

diagnosis and prognosis. However, SMAD3’s varied roles across different cancers underscore the need for further research to validate its potential as a universal cancer biomarker. Moreover,

its critical role in the TGF-β signaling pathway and its potential applications in regulating tumor cell migration and invasion are suggested. While this study briefly validates SMAD3’s

impact on proliferation and migration of liver hepatocellular carcinoma (LIHC) cells, further molecular biology experiments are necessary to elucidate specific mechanisms, including

immunohistochemistry and immunocytochemistry approaches.Lastly, considering data extraction from multiple databases, potential systematic biases should not be overlooked. These limitations

emphasize the necessity for broader research efforts to fully clarify SMAD3’s roles in cancer and its potential as a therapeutic target for cancer treatment. CONCLUSION Our study

systematically examined the significant correlation between SMAD3 expression and clinical features, prognosis, mutation status, DNA methylation, RNA methylation, immune checkpoints,

immunomodulatory genes, TMB, MSI, DNAss, RNAss, and drug susceptibility in several cancers, which helps us better understand the potential role of SMAD3 in pan cancer. DATA AVAILABILITY The

datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

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  Download references ACKNOWLEDGEMENTS We would like to express our sincere gratitude for the contributions provided by the TCGA Pan-cancer Project and Sangerbox websites. FUNDING This study

was supported by the National Natural Science Foundation of China [82060049, 82170289 and 82060048] and Jiangxi Natural Science Foundation [20202BAB216009]. AUTHOR INFORMATION AUTHORS AND

AFFILIATIONS * Jiangxi Key Laboratory of Molecular Medicine, Jiangxi Medical College, The Second Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, 330006, China Tao

Zhou, Jiejing jin, Jinyang Xie & Rong Wan * Department of General Surgery, Jiangxi Medical College, The Second Affiliated Hospital of Nanchang University, Nanchang University, Nanchang,

330006, China Dan Dan Zhang * Department of Cardiovascular Medicine, Jiangxi Medical College, The Second Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, 330006,

China Jianhua Yu * Department of Pharmacology, School of Basic Medical Sciences, Nanjing Medical University, Nanjing, 211166, China Chao Zhu & Rong Wan Authors * Tao Zhou View author

publications You can also search for this author inPubMed Google Scholar * Dan Dan Zhang View author publications You can also search for this author inPubMed Google Scholar * Jiejing jin

View author publications You can also search for this author inPubMed Google Scholar * Jinyang Xie View author publications You can also search for this author inPubMed Google Scholar *

Jianhua Yu View author publications You can also search for this author inPubMed Google Scholar * Chao Zhu View author publications You can also search for this author inPubMed Google

Scholar * Rong Wan View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS ZC and WR contributed to the conception and design of the study. ZT,

ZDD, JJJ, YJH and XJY drafted the manuscript. ZT and ZDD collected and analyzed the data. ZC and WR revised the manuscript. All authors contributed to the manuscript revision and read and

approved the submitted version. CORRESPONDING AUTHORS Correspondence to Chao Zhu or Rong Wan. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ETHICS

STATEMENT This study was conducted in accordance with all appropriate guidelines and regulations stipulated by the Declaration of Helsinki. The experimental received approval from the

institutional review board at The Second Affiliated Hospital of Nanchang University. Written informed consent was secured from all participants who agreed to partake in this research.

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immunological and prognostic potential of SMAD3 in pan-cancer and validation in LIHC. _Sci Rep_ 15, 657 (2025). https://doi.org/10.1038/s41598-024-84553-3 Download citation * Received: 19

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initiative KEYWORDS * smad3 * Pan-cancer * TGF-β * LIHC * Diagnosis * Prognosis * Immune infiltration