A gene signature associated with PTEN activation defines good prognosis intermediate risk prostate cancer cases

Genomic classifiers and prognosis of localized prostate cancer: a systematic review

BACKGROUND

Refinement of the risk classification for localized prostate cancer is warranted to aid in clinical decision making. A systematic analysis was undertaken to evaluate the prognostic ability of three genomic classifiers, Decipher, GPS, and Prolaris, for biochemical recurrence, development of metastases and prostate cancer-specific mortality in patients with localized prostate cancer.

METHODS

Data sources: MEDLINE, Embase, and Web of Science were queried for reports published from January 2010 to April 2022.

STUDY SELECTION

prospective or retrospective studies reporting prognosis for patients with localized prostate cancer.

DATA EXTRACTION

relevant data were extracted into a customized database by one researcher with a second overreading. Risk of bias was assessed using a validated tool for prognostic studies, Quality in Prognosis Studies (QUIPS). Disagreements were resolved by consensus or by input from a third reviewer. We assessed the certainty of evidence by GRADE incorporating adaptation for prognostic studies.

RESULTS

Data synthesis: a total of 39 studies (37 retrospective) involving over 10,000 patients were identified. Twenty-two assessed Decipher, 5 GPS, and 14 Prolaris. Thirty-four studies included patients who underwent prostatectomy. Based on very low to low certainty of evidence, each of the three genomic classifiers modestly improved upon the prognostic ability for biochemical recurrence, development of metastases, and prostate cancer-specific mortality compared to standard clinical risk-classification schemes.

LIMITATIONS

downgrading of confidence in the evidence stemmed largely from bias due to the retrospective nature of the studies, heterogeneity in treatment received, and era in which patients were treated (i.e., prior to the 2000s).

CONCLUSIONS

Genomic classifiers provide a small but consistent improvement upon the prognostic ability of clinical classification schemes, which may be helpful when treatment decisions are uncertain. However, evidence from current management-era data and of the predictive ability of these tests is needed.

Systematic review and integrated analysis of prognostic gene signatures for prostate cancer patients

Prostate cancer (PC) is one of the most common cancers in men and becoming the second leading cause of cancer fatalities. At present, the lack of effective strategies for prognosis of PC patients is still a problem to be solved. Therefore, it is significant to identify potential gene signatures for PC patients' prognosis. Here, we summarized 71 different prognostic gene signatures for PC and concluded 3 strategies for signature construction after extensive investigation. In addition, 14 genes frequently appeared in 71 different gene signatures, which enriched in mitotic and cell cycle. This review provides extensive understanding and integrated analysis of current prognostic signatures of PC, which may help researchers to construct gene signatures of PC and guide future clinical treatment.

A Novel Signature Based on Anoikis Associated with BCR-Free Survival for Prostate Cancer

This study aimed to elucidate the role of anoikis in the progression of prostate cancer (PCa) and to develop a prognostic signature based on anoikis-related genes (ARGs). To achieve this, PCa cases were subjected to nonnegative matrix factorization (NMF) analysis, which allowed for the identification of distinct patterns of anoikis modification. Additionally, immune infiltration was evaluated using single-sample gene-set enrichment analysis (ssGSEA). Survival analysis was performed using the Kaplan-Meier method, and a risk score was generated based on the expression levels of ARGs to quantitatively assess the modification of anoikis in PCa. Using the Least Absolute Shrinkage and Selection Operator (LASSO) method, four hub-genes were identified, and patients were classified into different risk groups based on their individual scores. Importantly, the low-risk subtype was characterized by a significantly improved biochemical recurrence-free survival, underscoring the clinical relevance of the ARG-based prognostic signature. To further improve the prognostic accuracy of the signature, patient age, pathological T stage, Gleason score, and prostate-specific antigen level were incorporated into the analysis, yielding a comprehensive prognostic signature. The clinical relevance of this signature was illustrated through a nomogram, providing a visual representation of the prognostic implications of the ARG-based signature. Taken together, these findings highlight the potential of ARGs in predicting the clinical outcomes of PCa patients and provide a novel and clinically relevant prognostic signature based on the modification of anoikis in PCa.

CACNA1D overexpression and voltage-gated calcium channels in prostate cancer during androgen deprivation

Prostate cancer is often treated by perturbing androgen receptor signalling. CACNA1D, encoding Ca_V1.3 ion channels is upregulated in prostate cancer. Here we show how hormone therapy affects CACNA1D expression and Ca_V1.3 function. Human prostate cells (LNCaP, VCaP, C4-2B, normal RWPE-1) and a tissue microarray were used. Cells were treated with anti-androgen drug, Enzalutamide (ENZ) or androgen-removal from media, mimicking androgen-deprivation therapy (ADT). Proliferation assays, qPCR, Western blot, immunofluorescence, Ca²⁺-imaging and patch-clamp electrophysiology were performed. Nifedipine, Bay K 8644 (Ca_V1.3 inhibitor, activator), mibefradil, Ni²⁺ (Ca_V3.2 inhibitors) and high K⁺ depolarising solution were employed. CACNA1D and Ca_V1.3 protein are overexpressed in prostate tumours and CACNA1D was overexpressed in androgen-sensitive prostate cancer cells. In LNCaP, ADT or ENZ increased CACNA1D time-dependently whereas total protein showed little change. Untreated LNCaP were unresponsive to depolarising high K⁺/Bay K (to activate Ca_V1.3); moreover, currents were rarely detected. ADT or ENZ-treated LNCaP exhibited nifedipine-sensitive Ca²⁺-transients; ADT-treated LNCaP exhibited mibefradil-sensitive or, occasionally, nifedipine-sensitive inward currents. CACNA1D knockdown reduced the subpopulation of treated-LNCaP with Ca_V1.3 activity. VCaP displayed nifedipine-sensitive high K⁺/Bay K transients (responding subpopulation was increased by ENZ), and Ni²⁺-sensitive currents. Hormone therapy enables depolarization/Bay K-evoked Ca²⁺-transients and detection of Ca_V1.3 and Ca_V3.2 currents. Physiological and genomic CACNA1D/Ca_V1.3 mechanisms are likely active during hormone therapy-their modulation may offer therapeutic advantage.

Prostate Cancer Secretome and Membrane Proteome from Pten Conditional Knockout Mice Identify Potential Biomarkers for Disease Progression

Prostate cancer (PCa) is the second most common cause of mortality among men. Tumor secretome is a promising strategy for understanding the biology of tumor cells and providing markers for disease progression and patient outcomes. Here, transcriptomic-based secretome analysis was performed on the PCa tumor transcriptome of Genetically Engineered Mouse Model (GEMM) Pb-Cre4/Pten^f/f mice to identify potentially secreted and membrane proteins—PSPs and PMPs. We combined a selection of transcripts from the GSE 94574 dataset and a list of protein-coding genes of the secretome and membrane proteome datasets using the Human Protein Atlas Secretome. Notably, nine deregulated PMPs and PSPs were identified in PCa (DMPK, PLN, KCNQ5, KCNQ4, MYOC, WIF1, BMP7, F3, and MUC1). We verified the gene expression patterns of Differentially Expressed Genes (DEGs) in normal and tumoral human samples using the GEPIA tool. DMPK, KCNQ4, and WIF1 targets were downregulated in PCa samples and in the GSE dataset. A significant association between shorter survival and KCNQ4, PLN, WIF1, and F3 expression was detected in the MSKCC dataset. We further identified six validated miRNAs (mmu-miR-6962-3p, mmu-miR- 6989-3p, mmu-miR-6998-3p, mmu-miR-5627-5p, mmu-miR-15a-3p, and mmu-miR-6922-3p) interactions that target MYOC, KCNQ5, MUC1, and F3. We have characterized the PCa secretome and membrane proteome and have spotted new dysregulated target candidates in PCa.

Gene Expression Analysis Reveals Prognostic Biomarkers of the Tyrosine Metabolism Reprogramming Pathway for Prostate Cancer

Background

Tyrosine metabolism pathway-related genes were related to prostate cancer progression, which may be used as potential prognostic markers.

Aims

To dissect the dysregulation of tyrosine metabolism in prostate cancer and build a prognostic signature based on tyrosine metabolism-related genes for prostate cancer. Materials and Method. Cross-platform gene expression data of prostate cancer cohorts were collected from both The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Based on the expression of tyrosine metabolism-related enzymes (TMREs), an unsupervised consensus clustering method was used to classify prostate cancer patients into different molecular subtypes. We employed the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to evaluate prognostic characteristics based on TMREs to obtain a prognostic effect. The nomogram model was established and used to synthesize molecular subtypes, prognostic characteristics, and clinicopathological features. Kaplan–Meier plots and logrank analysis were used to clarify survival differences between subtypes.

Results

Based on the hierarchical clustering method and the expression profiles of TMREs, prostate cancer samples were assigned into two subgroups (S1, subgroup 1; S2, subgroup 2), and the Kaplan–Meier plot and logrank analysis showed distinct survival outcomes between S1 and S2 subgroups. We further established a four-gene-based prognostic signature, and both in-group testing dataset and out-group testing dataset indicated the robustness of this model. By combining the four gene-based signatures and clinicopathological features, the nomogram model achieved better survival outcomes than any single classifier. Interestingly, we found that immune-related pathways were significantly concentrated on S1-upregulated genes, and the abundance of memory B cells, CD4+ resting memory T cells, M0 macrophages, resting dendritic cells, and resting mast cells were significantly different between S1 and S2 subgroups.

Conclusions

Our results indicate the prognostic value of genes related to tyrosine metabolism in prostate cancer and provide inspiration for treatment and prevention strategies.

Construction and validation of N6-methyladenosine long non-coding RNAs signature of prognostic value for early biochemical recurrence of prostate cancer

PURPOSE

Early biochemical recurrence (eBCR) indicated a high risk for potential recurrence and metastasis in prostate cancer. The N6-methyladenosine (m6A) methylation modification played an important role in prostate cancer progression. This study aimed to develop a m6A lncRNA signature to accurately predict eBCR in prostate cancer.

METHODS

Pearson correlation analysis was first conducted to explore m6A lncRNAs and univariate Cox regression analysis was further performed to identify m6A lncRNAs of prognostic roles for predicting eBCR in prostate cancer. The m6A lncRNA signature was constructed by least absolute shrinkage and selection operator analysis (LASSO) in training cohort and further validated in test cohort. Furthermore, half maximal inhibitory concentration (IC50) values were utilized to explore potential effective drugs for high-risk group in this study.

RESULTS

Five hundred and thirty-eighth m6A lncRNAs were searched out through Pearson correlation analysis and 25 out of 538 m6A lncRNAs were identified to pose prediction roles for eBCR in prostate cancers. An m6A lncRNA signature including 5 lncRNAs was successfully built in training cohort. The high-risk group derived from m6A lncRNA signature could efficiently predict eBCR occurrence in both training (p < 0.001) and test cohort (p = 0.002). ROC analysis also confirmed that lncRNA signature in this study posed more accurate prediction roles for eBCR occurrence when compared with PSA, TNM stages and Gleason scores. Drug sensitivity analysis further discovered that various drugs could be potentially utilized to treat high-risk samples in this study.

CONCLUSIONS

The m6A lncRNA signature in this study could be utilized to efficiently predict eBCR occurrence, various clinical characteristic and immune microenvironment for prostate cancer.

Attenuating Adaptive VEGF-A and IL8 Signaling Restores Durable Tumor Control in AR Antagonist-Treated Prostate Cancers

Inhibiting androgen signaling using androgen signaling inhibitors (ASI) remains the primary treatment for castrate-resistant prostate cancer. Acquired resistance to androgen receptor (AR)-targeted therapy represents a major impediment to durable clinical response. Understanding resistance mechanisms, including the role of AR expressed in other cell types within the tumor microenvironment, will extend the clinical benefit of AR-targeted therapy. Here, we show the ASI enzalutamide induces vascular catastrophe and promotes hypoxia and microenvironment adaptation. We characterize treatment-induced hypoxia, and subsequent induction of angiogenesis, as novel mechanisms of relapse to enzalutamide, highlighting the importance of two hypoxia-regulated cytokines in underpinning relapse. We confirmed AR expression in CD34+ vascular endothelium of biopsy tissue and human vascular endothelial cells (HVEC). Enzalutamide attenuated angiogenic tubule formation and induced cytotoxicity in HVECs in vitro, and rapidly induced sustained hypoxia in LNCaP xenografts. Subsequent reoxygenation, following prolonged enzalutamide treatment, was associated with increased tumor vessel density and accelerated tumor growth. Hypoxia increased AR expression and transcriptional activity in prostate cells in vitro. Coinhibition of IL8 and VEGF-A restored tumor response in the presence of enzalutamide, confirming the functional importance of their elevated expression in enzalutamide-resistant models. Moreover, coinhibition of IL8 and VEGF-A resulted in a durable, effective resolution of enzalutamide-sensitive prostate tumors. We conclude that concurrent inhibition of two hypoxia-induced factors, IL8 and VEGF-A, prolongs tumor sensitivity to enzalutamide in preclinical models and may delay the onset of enzalutamide resistance.

IMPLICATIONS

Targeting hypoxia-induced signaling may extend the therapeutic benefit of enzalutamide, providing an improved treatment strategy for patients with resistant disease.

Protein expression of the gp78 E3-ligase predicts poor breast cancer outcome based on race

Women of African ancestry suffer higher rates of breast cancer mortality compared to all other groups in the United States. Though the precise reasons for these disparities remain unclear, many recent studies have implicated a role for differences in tumor biology. Using an epitope-validated antibody against the endoplasmic reticulum-associated degradation (ERAD) E3 ubiquitin ligase, gp78, we show that elevated levels of gp78 in patient breast cancer cells predict poor survival. Moreover, high levels of gp78 are associated with poor outcomes in both ER-positive and ER-negative tumors, and breast cancers expressing elevated amounts of gp78 protein are enriched in gene expression pathways that influence cell cycle, metabolism, receptor-mediated signaling, and cell stress response pathways. In multivariate analysis adjusted for subtype and grade, gp78 protein is an independent predictor of poor outcomes in women of African ancestry. Furthermore, gene expression signatures, derived from patients stratified by gp78 protein expression, are strong predictors of recurrence and pathological complete response in retrospective clinical trial data and share many common features with gene sets previously identified to be overrepresented in breast cancers based on race. These findings implicate a prominent role for gp78 in tumor progression and offer new insights into our understanding of racial differences in breast cancer outcomes.

Establishment of an age- and tumor microenvironment-related gene signature for survival prediction in prostate cancer

Background

The incidence of prostate cancer (PCa) increases with age, and age and tumor microenvironment (TME) have important roles in the development of PCa, while the underlying mechanisms have not been fully elucidated.

Materials and method

The Cancer Genome Atlas‐Prostate Adenocarcinoma (TCGA‐PRAD) RNA‐Seq, the Surveillance, Epidemiology, and End Results (SEER‐PRAD), and ESTIMATE data were downloaded, and the clinical information of PRAD patients in our cohort was collected. The associations among age, TME, and PCa were analyzed. The age‐ and TME‐related risk score (ATRS) of each TCGA‐PRAD sample was calculated based on the identified age‐ and TME‐related differentially expressed genes (DEGs), and the correlation of ATRS with immune‐related characteristics of PCa patients was analyzed, and the ATRS‐based overall survival (OS)‐predicting nomogram was also established.

Results

Age was correlated with OS, PSA level, tumor stage, T stage, N stage, Gleason score, nerve invasion of PCa, and age was positively correlated with stromal, immune, and ESTIMATE scores. The compositions of immune cells of TCGA‐PRAD patients altered with age. Nine age‐ and TME‐related prognostic DEGs were identified, and the ATRS of each TCGA‐PRAD patient was calculated based on the identified nine DEGs. The ATRS was associated with the expression of immune checkpoints and intratumoral cytolytic activity, and the ATRS‐based nomogram performed well in predicting the outcomes of PCa patients.

Conclusions

Age and TME had crucial roles in PCa, and the ATRS gene signature was associated with the immune‐related characteristics of PCa patients, which showed good performance in predicting OS of PCa patients.

Transcriptional landscape of PTEN loss in primary prostate cancer

BACKGROUND

PTEN is the most frequently lost tumor suppressor in primary prostate cancer (PCa) and its loss is associated with aggressive disease. However, the transcriptional changes associated with PTEN loss in PCa have not been described in detail. In this study, we highlight the transcriptional changes associated with PTEN loss in PCa.

METHODS

Using a meta-analysis approach, we leveraged two large PCa cohorts with experimentally validated PTEN and ERG status by Immunohistochemistry (IHC), to derive a transcriptomic signature of PTEN loss, while also accounting for potential confounders due to ERG rearrangements. This signature was expanded to lncRNAs using the TCGA quantifications from the FC-R2 expression atlas.

RESULTS

The signatures indicate a strong activation of both innate and adaptive immune systems upon PTEN loss, as well as an expected activation of cell-cycle genes. Moreover, we made use of our recently developed FC-R2 expression atlas to expand this signature to include many non-coding RNAs recently annotated by the FANTOM consortium. Highlighting potential novel lncRNAs associated with PTEN loss and PCa progression.

CONCLUSION

We created a PCa specific signature of the transcriptional landscape of PTEN loss that comprises both the coding and an extensive non-coding counterpart, highlighting potential new players in PCa progression. We also show that contrary to what is observed in other cancers, PTEN loss in PCa leads to increased activation of the immune system. These findings can help the development of new biomarkers and help guide therapy choices.

A novel immune-related gene-based prognostic signature to predict biochemical recurrence in patients with prostate cancer after radical prostatectomy

Accumulating evidences indicates that the immune landscape signature dramatically correlates with tumorigenesis and prognosis of prostate cancer (PCa). Here, we identified a novel immune-related gene-based prognostic signature (IRGPS) to predict biochemical recurrence (BCR) after radical prostatectomy. We also explored the correlation between IRGPS and tumor microenvironment. We identified an IRGPS consisting of seven immune-related genes (PPARGC1A, AKR1C2, COMP, EEF1A2, IRF5, NTM, and TPX2) that were related to the BCR-free survival of PCa patients. The high-risk patients exhibited a higher fraction of regulatory T cells and M2 macrophages than the low-risk BCR patients (P < 0.05) as well as a lower fraction of resting memory CD4 T cells and resting mast cells. These high-risk patients also had higher expression levels of CTLA4, TIGIT, PDCD1, LAG3, and TIM3. Finally, a strong correlation was detected between IRGPS and specific clinicopathological features, including Gleason scores and tumor stage. In conclusion, our study reveals the clinical significance and potential functions of the IRGPS, provides more data for predicting outcomes, and suggests more effective immunotherapeutic target strategies for PCa.

The PTEN Conundrum: How to Target PTEN-Deficient Prostate Cancer

Loss of the tumor suppressor phosphatase and tensin homologue deleted on chromosome 10 (PTEN), which negatively regulates the PI3K-AKT-mTOR pathway, is strongly linked to advanced prostate cancer progression and poor clinical outcome. Accordingly, several therapeutic approaches are currently being explored to combat PTEN-deficient tumors. These include classical inhibition of the PI3K-AKT-mTOR signaling network, as well as new approaches that restore PTEN function, or target PTEN regulation of chromosome stability, DNA damage repair and the tumor microenvironment. While targeting PTEN-deficient prostate cancer remains a clinical challenge, new advances in the field of precision medicine indicate that PTEN loss provides a valuable biomarker to stratify prostate cancer patients for treatments, which may improve overall outcome. Here, we discuss the clinical implications of PTEN loss in the management of prostate cancer and review recent therapeutic advances in targeting PTEN-deficient prostate cancer. Deepening our understanding of how PTEN loss contributes to prostate cancer growth and therapeutic resistance will inform the design of future clinical studies and precision-medicine strategies that will ultimately improve patient care.

PTEN: A Thrifty Gene That Causes Disease in Times of Plenty?

The modern obesity epidemic with associated disorders of metabolism and cancer has been attributed to the presence of "thrifty genes". In the distant past, these genes helped the organism to improve energy efficiency and store excess energy safely as fat to survive periods of famine, but in the present day obesogenic environment, have turned detrimental. I propose PTEN as the likely gene as it has functions that span metabolism, cancer and reproduction, all of which are deranged in obesity and insulin resistance. The activity of PTEN can be calibrated in utero by availability of nutrients by the methylation arm of the epigenetic pathway. Deficiency of protein and choline has been shown to upregulate DNA methyltransferases (DNMT), especially 1 and 3a; these can then methylate promoter region of PTEN and suppress its expression. Thus, the gene is tuned like a metabolic rheostat proportional to the availability of specific nutrients, and the resultant "dose" of the protein, which sits astride and negatively regulates the insulin-PI3K/AKT/mTOR pathway, decides energy usage and proliferation. This "fixes" the metabolic capacity of the organism periconceptionally to a specific postnatal level of nutrition, but when faced with a discordant environment, leads to obesity related diseases.

A novel gene signature to predict immune infiltration and outcome in patients with prostate cancer

Prostate cancer (PCa) is one of the most common malignancies in male. We aim to establish a novel gene signature for immune infiltration and outcome (biochemical recurrence (BCR) and overall survival (OS)) of patients with prostate cancer (PCa) to augment Gleason patterns for evaluating prognosis and managing patients undergoing radical prostatectomy (RP). Combined with our microarray data and the Cancer Genome Atlas Project (TCGA) database (discovery set), we identified a six-gene signature. The Gene Expression Omnibus (GEO) database served as the test set. The databases of Fudan University Shanghai Cancer Center (FUSCC) and Third Affiliated Hospital of Nantong University (TAHNU) served as an external validation set. Immunohistochemistry was used to investigate the relationship between risk groups and the immune infiltrate. We identified a six-gene signature to predict immune cell infiltration and outcome of PCa patients. The AUC values used to predict early BCR in the discovery, test, FUSCC, and TAHNU sets were 0.73, 0.76, 0.72, and 0.81, respectively. Low-risk score patients in each dataset experienced significantly longer OS (P = .01, 0.04, 0.02, respectively). The signature also predicted high regulatory T cells (Tregs) and M2-polarized macrophages infiltration in high-risk score patients with PCa. Additionally, high mutation load, related signal pathways, and sensitivity to anticancer drugs that correlated with high-risk score of cancer progression and death were also identified. The six-gene signature may improve prognostic information, serve as a prognostic tool to manage patients after RP, and advance basic studies of PCa.

Detailed Molecular and Immune Marker Profiling of Archival Prostate Cancer Samples Reveals an Inverse Association between TMPRSS2:ERG Fusion Status and Immune Cell Infiltration

Prostate cancer is a significant global health issue, and limitations to current patient management pathways often result in overtreatment or undertreatment. New ways to stratify patients are urgently needed. We conducted a feasibility study of such novel assessments, looking for associations between genomic changes and lymphocyte infiltration. An innovative workflow using an in-house targeted sequencing panel, immune cell profiling using an image analysis pipeline, RNA sequencing, and exome sequencing in select cases was tested. Gene fusions were profiled by RNA sequencing in 27 of 27 cases, and a significantly higher tumor-infiltrating lymphocyte (TIL) count was noted in tumors without a TMPRSS2:ERG fusion compared with those with the fusion (P = 0.01). Although this finding was not replicated in a larger validation set (n = 436) of The Cancer Genome Atlas images, there was a trend in the same direction. Differential expression analysis of TIL-high and TIL-low tumors revealed the enrichment of both innate and adaptive immune response pathways. Mutations in mismatch repair genes (MLH1 and MSH6 mutations in 1 of 27 cases) were identified. We describe a potential immune escape mechanism in TMPRSS2:ERG fusion-positive tumors. Detailed profiling, as shown herein, can provide novel insights into tumor biology. Likely differences with findings with other cohorts are related to methods used to define region of interest, but this warrants further study in a larger cohort.

Cancer taxonomy: pathology beyond pathology

The way we categorise and classify cancer types dictates not only the way we diagnose and treat patients but also many of our decisions on biomarker and drug development. In addition, cancer taxonomy proves the ground truth for future discoveries in the area of computational pathology and artificial intelligence. This editorial comment illustrates the relevance of cancer taxonomy in clinical and morphomolecular diagnosis, prognosis and therapeutic prediction; it shows its importance in identifying the epidemiology, aetiology and pathogenesis in oncology and explains its determinant role in computational tissue-based cancer diagnosis.

Analysis of microRNA expression profiles reveals a 5‑microRNA prognostic signature for predicting overall survival time in patients with gastric adenocarcinoma

There is growing evidence supporting dysregulated microRNAs (miRNAs) as potential prognostic biomarkers in cancer. The present study aimed to identify an miRNA model set with prognostic power for patients with gastric adenocarcinoma. miRNA‑seq data from 155 patients and 37 controls were downloaded from The Cancer Genome Atlas (TCGA) database for a comprehensive analysis of miRNA expression profiles and were used as training data. A total of 5 prognostic miRNAs, which have not been previously reported, were identified using univariate and multivariate Cox regression analyses. A separate 155‑patient TCGA cohort was used as a validation set for evaluation of the risk model. Patients in the training set were assigned into high‑ and low‑risk groups according to the 5‑miRNA signature risk scores. Kaplan‑Meier survival analyses demonstrated that patients with high risk scores had significantly shorter survival times than those with low risk scores. The risk model validation confirmed the prognostic ability of this 5‑miRNA signature in predicting the risk status of patients. Stratification analysis for clinical prognostic variables demonstrated recurrence and age were significant prognostic factors in the low‑ and high‑risk groups, respectively. In conclusion, the present 5‑miRNA signature is a potential independent risk factor for patient outcomes. The risk model based on the 5‑miRNA signature performed well in predicting overall survival time in patients with gastric adenocarcinoma.