ABSTRACT BACKGROUND Risk stratification or progression in prostate cancer is performed with the support of clinical-pathological data such as the sum of the Gleason score and serum levels

PSA. For several decades, methods aimed at the early detection of prostate cancer have included the determination of PSA serum levels. The aim of this systematic review is to provide an

overview about recent advances in the discovery of new molecular biomarkers through transcriptomics, genomics and artificial intelligence that are expected to improve clinical management of

the prostate cancer patient. METHODS An exhaustive search was conducted by Pubmed, Google Scholar and Connected Papers using keywords relating to the genetics, genomics and artificial

sequences, are expected to contribute to an earlier and accurate diagnosis for each patient in the context of precision medicine, thus improving the prognosis and quality of life of

patients. We analyze several aspects that are relevant for prostate cancer including its new molecular markers associated with diagnosis, prognosis, and prediction to therapy and how

bioinformatic approaches such as machine learning and deep learning can contribute to clinic. Furthermore, we also include current techniques that will allow an earlier diagnosis, such as

Spatial Transcriptomics, Exome Sequencing, and Whole-Genome Sequencing. CONCLUSION Transcriptomic and genomic analysis have contributed to generate knowledge in the field of prostate

carcinogenesis, new information about coding and non-coding genes as biomarkers has emerged. Synergies created by the implementation of artificial intelligence to analyze and understand

sequencing data have allowed the development of clinical strategies that facilitate decision-making and improve personalized management in prostate cancer. SIMILAR CONTENT BEING VIEWED BY

OTHERS MIRNA EXPRESSION PROFILING AND CLINICAL IMPLICATIONS IN PROSTATE CANCER ACROSS VARIOUS STAGES Article Open access 05 March 2025 PANELS OF MRNAS AND MIRNAS FOR DECODING MOLECULAR

MECHANISMS OF RENAL CELL CARCINOMA (RCC) SUBTYPES UTILIZING ARTIFICIAL INTELLIGENCE APPROACHES Article Open access 30 September 2022 MOLECULAR TRACING OF PROSTATE CANCER LETHALITY Article 12

October 2020 INTRODUCTION Detection of prostate cancer (PCa) includes the measurement of PSA serum levels and the digital rectal exam (DRE), contributing with the detection of PCa in early

stages. The localized disease, when it is confined to the prostate, is treated with radical prostatectomy (RP) [1]. In contrast, localized advanced PCa is treated with surgery, adjuvants,

radiotherapy such as external beam radiation or brachytherapy; and hormone therapy such as Luteinizing hormone-releasing hormone (LHRH) agonists and antagonists, abiraterone, and

enzalutamide; while metastatic disease is usually treated with hormone therapy such as apalutamide, and chemotherapy [2]. If PCa is diagnosed on time, the treatment can be effective and with

minimal morbidity [3]. In order to cover the proportion of indeterminate findings, novel diagnostic biomarkers have been developed such as Prostate Health Index (PHI), 4 K score, SelectMDx,

ConfirmMDx and PCA3 [4]. Although several studies have been performed to analyze different molecular biomarkers, such as variant V7 of the androgen receptor [5] or inactivation of the

_PTEN_ or _c-MYC_ gene [6], to date, none of them have been approved as a prognostic biomarker for use in clinical settings [7, 8]. Currently, there are a few molecular prognostic biomarkers

in clinical use such as OncotypeDX Genomic Prostate Score [9], Prolaris [10], ProMark [11], and Decipher [12], based on cancer-associated gene panels [4]. These molecular tests guide the

urologist to establish the appropriate treatment and predict recurrence and progression risk after localized treatment. However, it is important to keep searching for molecular markers that

can aid in early diagnosis and prognosis of the patient as well as the establishment of patient response to different treatments, such as new genes, gene fusions, AR variants and non-coding

RNAs. At present, there is an intense debate regarding PSA as a diagnostic, prognostic and screening tool in PCa, and therefore it is especially important to focus on other types of

molecular markers that can support clinical outcomes and decision making for therapy [13]. In particular, transcriptome and genomics analysis have contributed to generate new knowledge in

the study of PCa and the intracellular signaling pathways that regulate prostate carcinogenesis generating new information about its biology [14]. Otherwise, artificial intelligence and some

of its algorithms have been served for clinical application in monitoring, detection, diagnosis, and treatment to generate new clinical predictive models to PCa Management [15].

Alternatively, several studies have combined histology with genomic data, integrating omics information with pathological images in PCa [16, 17] and with implementation of artificial

intelligence algorithms such as deep learning and machine learning have served to establish a connection from different branches of omics to get clinical prediction models, thus, creating an

integrative perspective that facilitates the discovery of new diagnostic, prognostic and therapeutic molecular biomarkers. Finally, the importance of precision medicine and the fusion

between sequencing and artificial intelligence is established with the aim of creating synergies that allow the development of more specific and advanced systems that facilitate obtaining

relevant clinical strategies for decision-making and personalized management of PCa patients. METHODS Aiming to search for new molecular biomarkers involved in the diagnosis, prognosis and

prediction, an exhaustive search was conducted by Pubmed, Google Scholar and Connected Papers using keywords relating to the genetics, genomics, transcriptomics and artificial intelligence

in PCa, it includes “biomarkers”, “non-coding RNAs”, “lncRNAs”, “miRNAs”, “repetitive sequence”, “risk”, “prognosis”, “prediction”, “therapy”, “exome”, “whole-genome sequencing”, “RNA-Seq”,

“transcriptome”, “artificial intelligence”, “machine learning”, and “deep learning”. BIOMARKERS AND PRECISION MEDICINE IN PROSTATE CANCER The National Cancer Institute of the United States

of America defines a biomarker as a biological molecule that can be detected in blood, tissue, or bodily fluids that can be measured and whose values allow the identification of a normal or

abnormal process, as well as a disease [18]. From the large variety of molecular markers currently in existence, they can be classified according to the clinical context for which they will

be used. For example, there are diagnostic, prognostic, and predictive molecular biomarkers [19]. This process has led to the era of precision medicine where the selection of treatment is

based on the molecular characteristics of the tumor of each patient [20] (Fig. 1). In the following paragraphs, we will mention and describe some molecular biomarkers that have recently been

reported as useful in PCa patient management, including coding and non-coding genes. CODING GENES AS MOLECULAR MARKERS OF PROSTATE CANCER UNDER CLINICAL INVESTIGATION Several novel

biomarkers for PCa have been proposed, however, their clinical utility remains to be discussed. Nevertheless, coding genes used as biomarkers such as AR, BRCA2, PTEN and the gene fusion

TMPRSS2-ERG have predictive value for treatment response and are used in clinical practice [21,22,23]. Although these molecular markers offer valuable prognostic information for clinical

practice, they are only functional in a subset of patients and more clinical trials are needed to validate their utility (Table 1). On the other hand, several mechanisms involved in prostate

tumorigenesis such as epigenetic changes, alternative splicing, and the presence of gene variants, are possible novel biomarkers based on coding-genes with potential clinical utility [24].

For example, regarding epigenetic regulators, the coding genes _MGMT_ [25], _DNMT1_ [26], and _JMJD3_ [27], which are involved in DNA methylation, have been associated with the risk of PCa

mortality (HR 0.90; _p_-value = 3.5 × 102) [25] as well as prostate tumor development (_p_-values = 0.03 and 0.05, respectively [26, 27]). Similarly, other proteins related to splicing

process, such as KDM4B [28], CDK9 [29] and SF3B2 [30] have been recently associated with generation of androgen receptor variant AR-V7 (_p_-value < 0.05 [28,29,30]). Furthermore, the

splicing variants by themselves are of particular interest for PCa research [31], androgen receptor variants [32] have been described as important factors in PCa development and prognosis,

such as variant AR-V3 and its prognostic value (_p_-value = 0.05) [33]. Likewise, HRAS [34] and PRUNE2 [35] are novel variants related to PCa development with clinical utility yet to be

confirmed. Therefore, research focused on finding new coding genes with clinical application as biomarkers, could improve PCa prognosis and treatment. Another example of coding genes that

may have potential clinical utility for PCa correspond to gene mutations involved in hereditary cancer, where it represents the etiology of 5–10% of all neoplasms [36], in which PCa has been

associated with family history of cancer [37]. Paradoxically, few high-susceptibility genes consistently related to the hereditary of PCa have been identified, presenting a pattern of

dominant autosomal inheritance [38], that has been linked to phenotypic variation and genetic heterogeneity, limiting its association with PCa predisposition [39]. Currently, the analysis of

Pathogenic Variants (PV) in predisposition genes associated with defects in homologous recombination and mismatch repair [40] which represents therapeutic targets to PARP1 inhibitors and

chemotherapies with platinum compounds, particularly in patients with metastatic and castration-resistant disease [41, 42]. The use of multi-gene panels in germline diagnosis has identified

PV in 7% to 12% of PCa patients [43, 44], highlighting _BRCA1, BRCA2, ATM, BRIP1, CHEK2, NBN, BARD1, RAD51C, MRE11A_ and _PALB2_ (homologous recombination repair); _MLH1, MSH2, MSH6_ and

_PMS2_ (mismatch repair) as high risk genes, which have clinical guidelines; option for risk reduction surgeries, and personalized treatment, which benefits the PCa patient [45]

(Supplementary Table 1). Although hereditary PCa does not imply a generalized molecular diagnosis, it does entail the identification of metastatic disease; early age of onset, and cancer

family history, who will have benefit for the therapeutic options and family prevention as a result of the molecular approach [46, 47]. Although all these molecular biomarkers have a

potential clinical application, current clinical trials have not been able to determine whether they have sufficient sensitivity and specificity to be considered for clinical purposes, as

well as all the genes discussed above are coding genes. Therefore, it is important to focus on the search for new biomarkers, like non-coding genes, which can contribute to the diagnosis and

prognosis of PCa patients. NONCODING GENES AS MOLECULAR MARKERS IN PROSTATE CANCER Most of molecular biomarkers in PCa are based in coding genes, but as previous studies have demonstrated;

mRNAs tend to have less tissue- and stage-specific expression. In contrast, non-coding RNAs tend to have more tissue-specific and stage-specific expression in disease, which is one of the

main reasons noncoding RNAs have been proposed as molecular biomarkers in cancer [48]. In the following paragraphs we describe some of the newest candidates as specific molecular biomarkers

in PCa clinical research. MIRNA One of the most studied small ncRNAs are microRNAs (miRNAs), these are single stranded RNAs of 21–25 nucleotides in length that regulate the

post-transcriptional degradation of messenger RNAs and inhibit their translation into proteins. Because of their high stability in body fluids [49] as well as to changes of physical and

chemical conditions [50], miRNAs are interesting molecules to be used as biomarkers in cancer. Free miRNAs can be found in several bodily fluids, such as blood, urine, semen, among others

[51] and their expression levels are tissue-specific and have been found to be deregulated in cancer [52]. Moreover, they exhibit differential expression between tumor and normal tissues and

are useful for tumor classification according to the lineage of origin, differentiation stage, and tumor aggressiveness [53]. It has been reported that circulating miRNAs can be packed in

extracellular vesicles (EV) or in association with proteins such as Argonaute2 or lipoproteins in bio-fluids including blood and urine [54,55,56]. Some miRNAs, such as miR-21, miR-221,

miR-1290, and miR-375, have been overexpressed and associated with prognosis in CRPC patients [55, 57]. Yaman and collaborators quantified the levels of miR-21, miR-142, and miR-221 in PCa

patients and reported that overexpression of these three miRNAs were associated with an advanced PCa stage [58]. Other groups have identified miRNAs in plasma and serum of patients with

locally advanced and metastatic PCa, with BPH and in healthy individuals, showing that differences between each group (i.e., higher levels of miRNAs in patients with locally advanced and

metastatic PCa highlight the role of miRNAs as diagnostic biomarkers [59]. Several groups have studied the diagnostic, prognostic, and predictive characteristics of miRNAs circulating in the

plasma and serum of PCa patients finding differentially expressed miRNAs according to the Gleason index [60], response to treatment with docetaxel [61], and high blood PSA values [62]. In

another study, a panel consisting of four miRNAs was proposed as a biomarker for the diagnosis of PCa [63]. The four miRNAs (miR-4289, miR-326, miR-152-3p and miR-98-5p) were upregulated in

plasma of PCa patients compared to healthy controls and was able to differentiate between PCa patients and control individuals with an area under the ROC curve of 0.88, proving their

diagnostic accuracy. In the study conducted by Sharova and collaborators [49], a circulating miRNA test consisting of measuring the level of 3 circulating miRNAs (miR-106a, miR-130b and

miR-223) was proposed to differentiate between localized PCa and BPH patients. In this test two ratios are calculated: miR-106a/miR-130b and miR-106a/miR-223 ratios, the results showed a

better performance (specificity: 0.806, sensitivity: 0.833, accuracy: 0.821) in comparison to PSA (specificity: 0.065, sensitivity: 0.889, accuracy: 0.507), the area under the ROC curve for

miRNA test was 0.84 while for PSA was 0.56. This test could be helpful for PCa screening to avoid unnecessary biopsies and assessment of PCa risk. Indeed, the use of miRNAs as biomarkers in

PCa has shown promising results for risk assessment, diagnosis, and prognosis. Implementation of miRNA-based tests in combination with gene-based biomarkers could improve the clinical

management of PCa patients. LONG NON-CODING RNAS As mentioned above, RNA molecules seem to have a critical role in cancer pathways including those within PCa. Long non-coding RNAs are known

to be RNA transcripts longer than 200 nucleotides with no protein-coding potential [64], these two major differences distinguish them from mRNA transcripts and any other non-coding RNA.

LncRNAs have been implicated in several biological processes such as chromatin-reprogramming, genomic imprinting, transcriptional regulation in _cis_ and _trans_ and post-transcriptional

regulation of mRNAs [65,66,67]. Among some pathological features in which lncRNAs are involved are cell proliferation, tumorigenesis and malignant transformation [68], this is why several

studies have proposed lncRNAs as tumor-suppressor genes and oncogenes [69, 70]. Lately, lncRNAs have drawn the attention not only because of their critical role in cancer, but because of

their potential as molecular biomarkers due to their tissue-specific and tumor-specific expression [68, 71]. Some lncRNAs, such as PCA3, SChLAP1, and PCAT1 have been proposed as good

candidates for biomarkers mainly due to their differential expression in PCa patients [72]. PCA3 is an overexpressed PCa-specific oncogene discovered in 1999 by Bussemakers [73]. PCA3 is

already considered a PCa biomarker, and it is measured by the commercial test PROGENSA approved in 2012 by the FDA [74,75,76] helping to reduce ~40% of unnecessary biopsies providing a great

utility in urological diagnosis [77]. PROGENSA PCA3 test has a sensitivity of 62% and a specificity of 75% [78] demonstrating why lncRNAs can be one of the molecular markers with clinical

utility. Similarly, SChLAP1 is known for its high expression levels in PCa. This lncRNA antagonizes the SWI/SNF complex promoting aggressiveness and metastasis of the tumor [79]. Its

effectiveness as a biomarker has been proved by assays such as RNA in situ hybridization leading to the development of several tests based on the detection of SChLAP1 expression levels and

linking them with the patient’s clinical-stage [80]. Therefore, SChLAP1 is considered as a promising biomarker of clinical utility and one of the best genes for prediction of metastasis and

biochemical recurrence in PCa patients [79, 81]. Along with these, Luo and collaborators reported that lncRNA-p21 is overexpressed in neuroendocrine PCa (NEPC) and that a treatment based

upon enzalutamide increases its expression, and thus, the neuroendocrine differentiation; all of this is caused by the alteration of the Enz/AR/lncRNA-p21/EZH2/STAT3 axis [82]. PCAT1 is

another upregulated oncogenic RNA originally identified in PCa by RNA-sequencing analysis [83]. It is related to cell proliferation, apoptosis, migration, and invasion as well as

epithelial-mesenchymal transition and cancer progression via the _Wnt/β-catenin_ signaling pathway [84]. Finally, PCAT1 negatively regulates BRCA2 tumor suppressor protein, positively

regulates Myc oncoprotein [85] and it might be also acting as a miRNA sponge involved in cell growth [83]. Hence, PCAT1 is considered as a potential biomarker for PCa prognosis and

prediction, supporting the statement that lncRNAs represent potential molecular biomarkers in the management of PCa (Table 2). Most of these candidates and a large number of transcriptional

units were found due to the breakthrough of the high-throughput massive sequencing technology, specifically, RNA-Seq. Finally, lncRNAs could be used in combination with gene-based biomarkers

and gene fusions to increase the sensitivity and specificity of molecular diagnostic tests, which will improve clinical patient management including early detection, diagnosis, prognosis,

and prediction of response to treatment [86]. REPETITIVE SEQUENCES Repetitive sequences are large quantities of repeated elements throughout the haploid genome, meaning they are repeated DNA

nucleotides found more than twice in the genome that comprises about 55% of the human genome or even more [87]. Their classification can vary from author to author, and it can be based on

the origin, function, structure, and genomic distribution of the DNA, but it is mainly based on the latter. The five categories are simple sequence repeats, segmental duplications, tandem

repeats and satellite DNA sequences, processed pseudogenes, and transposable elements [88]. Repetitive sequences are also considered as potential molecular biomarkers in diseases like cancer

because some of them are overexpressed in different types of tumors cells [89]. Genome sequencing and transcriptome sequencing have improved the discovery and detection of repetitive DNA

and RNA elements that cannot be identified by classic biochemical methods [90]. Solovyov and collaborators [91] determined that RNA repetitive sequences are not fully detected when using the

poly(A) protocol in RNA-seq procedure, while on the other hand, analyzing the expression of total RNA sequencing can not only identify the repetitive sequences more accurately but

delimitate immune phenotypes in cancer and response to immunotherapy [91]. Among the candidates for biomarkers in PCa we can found the _HERV-K_ sequence, which is highly expressed in

malignant prostate tissue when comparing it with normal prostate tissue, it is considered as a possible early disease detection biomarker detected in PCa patient blood, and it can even

increase PSA test efficiency [92, 93]. Moreover, _LINE-1_ is a DNA sequence that encodes the RNA-binding protein ORF1p and presents an increased expression in PCa tissues. Its overexpression

is associated to cancer tumorigenesis and its hypomethylation to PCa progression [94]. Although the experimental evidence regarding the importance of repeated sequences is not as abundant

as other RNA biotypes, these transcripts have the potential to be considered as biomarkers in PCa, nevertheless, more studies are needed to prove its applications as biomarker for diagnosis,

prognosis, and treatment management of PCa patients. The contribution of different sequencing methodologies has improved biomarker discovery in the field of non-coding transcripts.

IMPORTANCE OF HIGH-THROUGHPUT MASSIVE SEQUENCING IN PROSTATE CANCER DNA and RNA massive parallel sequencing has a large impact on the generation of new knowledge concerning molecular markers

in cancer because it explores the whole genome and transcriptome, allowing the detection of global point mutations, insertions, deletions, variations in copy number, translocations, fusion

genes, novel-transcript discovery, transcript abundance estimation, differential gene expression and differential splicing of mRNAs [95] (Fig. 2). The application of RNA-Seq provides a

quantitative pattern of coding and non-coding genes with transcriptional aberrations within the cell in a disease. This technique is an emerging sequencing technology that has a promising

future in disease diagnosis, prognosis, prediction and treatment [96]. Among some studies based on RNA sequencing as a potential tool for finding new PCa biomarkers and drug targets,

Berglund and collaborators analyzed the heterogenicity of PCa through a spatial-transcriptomic study in which several expression profiles were identified within a tissue region obtained

after RP (Gs 3 + 4, pT3b, PSA = 7.1). These expression profiles allowed the stratification of the tissue regions into cancer components or groups such as cancer, stroma, reactive stroma,

normal glands and prostatic intraepithelial neoplasia (PIN). They also found specific genes as potential biomarkers within the results, for example, _SPINK1_, _PGC_, and _CPP_ as specific

markers of PCa (Gs 3 + 3), _NR4A1_ as a specific marker of reactive stroma, and _NPY_ as a specific marker of PIN. The fact that these markers are expressed in specific and different

locations, demonstrates the level of heterogenicity in prostatic tumors and that studies based on RNA sequencing technologies can open the door to the discovery of novel molecular biomarkers

[97]. On the other hand, there is an urgent need to classify patients according to the most appropriate and effective therapy to increase the efficacy of treatment and reduce unnecessary

interventions that have no effect on the patients (Fig. 3). An example of this characteristic is a study supporting the use of exomes in precision medicine has been reported by Robinson and

collaborators, who demonstrated that actionable mutations detected with the aid of exomes in castration-resistant PCa patients can help determine the best treatment to use and responses of

the patients. The results established a mean rate of 4.4 mutations per Mb, in addition to a gain and loss of chromosome regions, with gains in _AR_ and losses in the genes _CHD1, PTEN, RB1_,

and _TP53_. The relevance of this study is that the molecular changes are actionable in 90% of the samples of CRPC patients, and in particular, patients with mutations in genes such as

_BRCA2_ (12% of cases) and _ATM_ (22% of cases) benefited from treatment with PARP inhibitors (olaparib) [98]. In another study, Armenia and collaborators identified 70 significantly mutated

genes that had not been previously associated with PCa, some of them are _CUL3_ (a ubiquitin ligase that function as a scaffold in the proteasome system), _SPEN_ (a transcription factor

involved in repression of gene expression), and _KMT2C_ and _KMT2D_ (epigenetic regulators with histone-lysine N-methyltransferase activity) by analyzing exome sequencing data from 1013 PCa

samples [99]. The markers found in this, and other studies could be used as part of gene signatures aimed to stratifying patients with localized and metastatic PCa. Furthermore, recent

whole-genome studies have identified mechanisms that generate complex chromosome rearrangements in PCa. Baca and collaborators sequenced the whole genome of 57 prostate tumors and identified

several DNA translocations and deletions that arose independently during oncogenesis and progression. They called this phenomenon “chromoplexy” referring to the coordinated and considerable

dysregulation of multiple cancer genes supporting a model of punctuated cancer evolution [100]. Therefore, studies based on genomics generate information that could help oncologists to

predict the response to treatment, allowing more personalized and effective management of patients with advanced PCa, and considering that not only the coding proportion of the genome has

this potential, the non-coding fraction of the genome should also be included. This experimental and clinical approach provides information about the emerging responses that current

therapies, such as androgen deprivation, and their effect in PCa patients. Sequencing analysis can also provide the necessary data for a more specific and enriched molecular classification

of PCa and could provide delineated subtypes among patients for better management [101]. ARTIFICIAL INTELLIGENCE IN PROSTATE CANCER RESEARCH Current methods for the detection of PCa show

limitations in its detection [102], representing a need to improve PCa diagnostic and patient stratification with complementary tools. Artificial intelligence (AI) has proven to be an

essential implement for clinical diagnoses, and it refers to the ability of a computational process to recognize patterns and make decisions that previously required human intellect to

achieve a certain purpose [103]. Machine learning (ML) is a discipline that teaches computers how to build models from the massive data sets that they are assigned with and learn from them.

This technologic approach is based on statistic algorithms, most of these algorithms are mathematical models that map the variables (features) of a data sample into a set of outcomes [104,

105]. Then, these algorithms go through a process of training to be able to predict the labels by analyzing the features [15]. The types of learning used in these models are mainly

classified as supervised learning and unsupervised learning (Fig. 4A). Supervised learning uses explicit data sets determined by experts, the computer uses the programmed algorithms to

minimize the prediction error, which is measured by the difference between the predicted labels and the known labels such as lineal logistic regression and random forest [106]. On the other

hand, unsupervised learning relies on samples that are separated into different classes based on the features of the training data such as principal component analysis [106]. It has been

suggested that ML could improve some aspects of biomedicine such as disease diagnosis, monitoring, anatomical imaging of organs, tissue biopsies and personalized treatment by using a

collection of molecular and phenotypic data [107]. It has also been proved as useful for its application in the human genome project and advances in cancer research and management [106]. The

advantageous outcome of ML also applies to PCa research by improving diagnostic and prognostic accuracy, treatment, imaging, surgical interventions, genomics and transcriptomics. It has

been reported that machines can be trained to recognize complex patterns in sequencing data together with radiographic images (such as those generated from computed tomography scanning and

magnetic resonance) by classifying pixels for segmentation and registration [108]. Its techniques can identify specific genes or sets of genes within expression profiles and specific

expression rate that can predict a certain clinical outcome such as progression, biochemical recurrence or metastasis in PCa [109]. There are commercial genomic classifiers available, such

as Decipher, that use the random forest algorithm for prediction of PCa metastasis based on the expression analysis of 22 RNA biomarkers of aggressive PCa [15, 110]. Besides, some studies

have applied ML algorithms to identify and associate non-coding RNA biomarkers for PCa diagnosis such as lncRNAs [111, 112], and _s_everal reports have developed specific algorithms, such as

XGBoost by Zhang and collaborators that associate lncRNAs with several cancer types [113], this algorithm is the basis of an improved method called CRlncRC2 which was found to be more

sensitive and specific than his previous version CRlncRC. Moreover, miRNAs are another potential biomarker identified through ML algorithms. Bertoli and collaborators used a support vector

machine model to detect 29 miRNAs for diagnostic PCa with 97% of accuracy and 7 miRNAs which can be used in prognostic of PCa with about 66% accuracy [114]_._ Another study group developed a

boosted random forest-based algorithm called MEDICASCY to detect cancer drug side effects, indications, efficacy, and mode of action using the chemical structure of the drug. This algorithm

showed an 80% precision for detecting drugs that can help inhibit the growth prostatic tumors, as well as ovarian and breast tumors [115]. Likewise, deep learning (DL) is a branch derived

from machine learning than can be used to recognize and classify tissue structures in digital information corresponding to a pathology [116]. Tolkach and collaborators developed a trained

model based on the technology of deep that recognized tumor tissue from images of 400 histological slides from different patients, as well as a novel algorithm based on three-dimensional

reconstruction of PCa architecture that can improve the Gleason grading [117]. Similarly, there are other algorithms that have been applied in clinics, for example, a Support Vector Machine

(SVM) model was used for the detection of positive and negative biopsies through dynamic contrast-enhanced and diffusion tensor imaging data [15]. In a 2020 DL study, a deep neural network

method was used to identify AR mutations during treatment for PCa. The predictions made by the algorithm can recognize mutants that resist the inhibitor darolutamide and other mutations of

pharmacological interest in PCa [118]. Therefore, the development and application of AI using ML in clinical practice could open an infinite landscape of approaches within PCa data analysis

(combination of coding and non-coding genes) improving the patient management in a near future. CONCLUSIONS Evidence based on clinical studies that focuses on finding new biomarkers suggests

that there is a wide molecular field that lies unexplored and that could be the key for many clinical challenges nowadays. These markers, such as the coding (_AR, BRCA2, PTEN_, MLH1, CUL3,

_SPEN_) and non-coding genes (PCA3, SChLAP1, _HERV-K_ and miR-21), the _TMPRSS2-ERG_ gene fusion including their derivatives, and the androgen receptor variant 7 can be found using genomic,

transcriptomics and AI approaches. However, these are not the only alterations that can be used for the diagnostic, prognostic and prediction in the management of PCa patients. The clinical

evidence mentioned in this review suggests the importance of these types of molecular markers (coding and non-coding genes) and their roles in the decision-making process for establishing

the most adequate treatment for patients suffering from this disease. Thus, it is important to establish the types of actionable mutations, or their combinations, in patients with advanced

PCa (locally advanced and metastatic), allowing us to determine the type of treatment that will provide a positive response in the PCa patient. In this area, genomic analysis, transcriptome

sequencing, and new approaches like spatial transcriptomics, along with the clinical-pathological information, could provide the necessary information. Likewise, the application of ML

algorithms will accelerate the identification and discovery of novel molecular biomarkers and it will lead biomedical investigation towards artificial-intelligence-based precision medicine,

so it can improve patient management as well as their quality of life, and, in the near future, allow a scientific revolution in medicine for the management of the PCa patient. The new

molecular biomarkers mentioned here along with the novel bioinformatic approaches of AI and sequencing techniques will improve biomedical research by complementing PSA test for screening,

stratifying patients, and identifying new molecular biomarkers for differentiation of indolent and aggressive disease, prognostic, predictive and surrogate biomarkers with clinical utility

(Fig. 4B). Finally, the fusion between sequencing and AI is established with the aim of creating synergies that allow the development of more specific and advanced systems that facilitate

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ACKNOWLEDGEMENTS We thank to National Cancer Institute of Mexico (INCan) for their support. Rogelio Montiel-Manriquez is a PhD student in the “Programa de Doctorado en Ciencias Biomédicas,

UNAM and received a fellowship from CONACYT with Currículum Vitae Único (CVU)- 581151. Figures were created with BioRender.com. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Unidad de

Investigación Biomédica en Cáncer, Instituto Nacional de Cancerología-Instituto de Investigaciones Biomédicas, UNAM, Avenida San Fernando No. 22 Col. Sección XVI, Tlalpan. C.P., 14080, CDMX,

México Ana Paula Alarcón-Zendejas, Rogelio Montiel-Manríquez, Clementina Castro-Hernández, Rodrigo González-Barrios, Cristian Arriaga-Canon & Luis A. Herrera * Departamento de Urología,

Instituto Nacional de Cancerología, Avenida San Fernando No. 22 Col. Sección XVI, Tlalpan. C.P., 14080, CDMX, México Anna Scavuzzo, Miguel A. Jiménez-Ríos & Miguel A. Jiménez-Dávila *

Clínica de Cáncer Hereditario, Instituto Nacional de Cancerología, Avenida San Fernando No. 22 Col. Sección XVI, Tlalpan. C.P., 14080, CDMX, México Rosa M. Álvarez-Gómez * Departamento de

Patología, Instituto Nacional de Cancerología, Avenida San Fernando No. 22 Col. Sección XVI, Tlalpan. C.P., 14080, CDMX, México Delia Pérez-Montiel * Instituto Nacional de Pediatría,

Insurgentes Sur No. 3700-C. Coyoacán. C.P., 04530, CDMX, México Francisco Jiménez-Trejo * Instituto Nacional de Medicina Genómica, Periférico Sur 4809. Arenal Tepepan, Tlalpan. C.P., 14610,

Mexico City, Mexico Luis A. Herrera Authors * Ana Paula Alarcón-Zendejas View author publications You can also search for this author inPubMed Google Scholar * Anna Scavuzzo View author

publications You can also search for this author inPubMed Google Scholar * Miguel A. Jiménez-Ríos View author publications You can also search for this author inPubMed Google Scholar * Rosa

M. Álvarez-Gómez View author publications You can also search for this author inPubMed Google Scholar * Rogelio Montiel-Manríquez View author publications You can also search for this author

inPubMed Google Scholar * Clementina Castro-Hernández View author publications You can also search for this author inPubMed Google Scholar * Miguel A. Jiménez-Dávila View author

publications You can also search for this author inPubMed Google Scholar * Delia Pérez-Montiel View author publications You can also search for this author inPubMed Google Scholar * Rodrigo

González-Barrios View author publications You can also search for this author inPubMed Google Scholar * Francisco Jiménez-Trejo View author publications You can also search for this author

inPubMed Google Scholar * Cristian Arriaga-Canon View author publications You can also search for this author inPubMed Google Scholar * Luis A. Herrera View author publications You can also

search for this author inPubMed Google Scholar CONTRIBUTIONS Project development: APAZ, AS, CAC, and LAH. Clinical Information: AS, MAJR, RMAG, MAJD and DPM. Basic research Information:

APAZ, RMM, CCH, RGB, FJT, CAC, and LAH. Manuscript writing/editing: APAZ, AS, MAJR, RMAG, RMM, CCH, MAJD, DPM, RGB, FJT, CAC, and LAH. CORRESPONDING AUTHORS Correspondence to Cristian

Arriaga-Canon or Luis A. Herrera. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral

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THIS ARTICLE CITE THIS ARTICLE Alarcón-Zendejas, A.P., Scavuzzo, A., Jiménez-Ríos, M.A. _et al._ The promising role of new molecular biomarkers in prostate cancer: from coding and non-coding

genes to artificial intelligence approaches. _Prostate Cancer Prostatic Dis_ 25, 431–443 (2022). https://doi.org/10.1038/s41391-022-00537-2 Download citation * Received: 11 November 2021 *

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