Development and validation of lncRNAs-based nomogram for prediction of biochemical recurrence in prostate cancer by bioinformatics analysis

Mutator-Derived lncRNA Landscape: A Novel Insight Into the Genomic Instability of Prostate Cancer

Background

Increasing evidence has emerged to reveal the correlation between genomic instability and long non-coding RNAs (lncRNAs). The genomic instability-derived lncRNA landscape of prostate cancer (PCa) and its critical clinical implications remain to be understood.

Methods

Patients diagnosed with PCa were recruited from The Cancer Genome Atlas (TCGA) program. Genomic instability-associated lncRNAs were identified by a mutator hypothesis-originated calculative approach. A signature (GILncSig) was derived from genomic instability-associated lncRNAs to classify PCa patients into high-risk and low-risk groups. The biochemical recurrence (BCR) model of a genomic instability-derived lncRNA signature (GILncSig) was established by Cox regression and stratified analysis in the train set. Then its prognostic value and association with clinical features were verified by Kaplan–Meier (K-M) analysis and receiver operating characteristic (ROC) curve in the test set and the total patient set. The regulatory network of transcription factors (TFs) and lncRNAs was established to evaluate TF–lncRNA interactions.

Results

A total of 95 genomic instability-associated lncRNAs of PCa were identified. We constructed the GILncSig based on 10 lncRNAs with independent prognostic value. GILncSig separated patients into the high-risk (n = 121) group and the low-risk (n = 121) group in the train set. Patients with high GILncSig score suffered from more frequent BCR than those with low GILncSig score. The results were further validated in the test set, the whole TCGA cohort, and different subgroups stratified by age and Gleason score (GS). A high GILncSig risk score was significantly associated with a high mutation burden and a low critical gene expression (PTEN and CDK12) in PCa. The predictive performance of our BCR model based on GILncSig outperformed other existing BCR models of PCa based on lncRNAs. The GILncSig also showed a remarkable ability to predict BCR in the subgroup of patients with TP53 mutation or wild type. Transcription factors, such as FOXA1, JUND, and SRF, were found to participate in the regulation of lncRNAs with prognostic value.

Conclusion

In summary, we developed a prognostic signature of BCR based on genomic instability-associated lncRNAs for PCa, which may provide new insights into the epigenetic mechanism of BCR.

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.

Should androgen deprivation therapy and other systemic treatments be used in men with prostate cancer and a rising PSA post-local treatments?

Biochemical recurrence is an evolving space in prostate cancer, with increasing multidisciplinary involvement. Androgen deprivation therapy has shown proof of its value in complementing salvage radiotherapy in high-risk biochemical relapsing patients; ongoing trials aim to further refine this treatment combination. As systemic treatments, and notably next-generation androgen receptor targeted agents, have moved towards early hormone-sensitive and non-metastatic stages, the prostate specific antigen (PSA)-relapse disease stage will be undoubtedly challenged by future evidence from such ongoing clinical trials. With the use of modern imaging and newer molecular technologies, including integration of tumoral genomic profiling and liquid biopsies in risk stratification, a path towards a precision oncology-focused approach will become a reality to guide in the future decisions for patients with a diagnosis of biochemical recurrence.

Identification and Validation of a Prognostic 5-Protein Signature for Biochemical Recurrence Following Radical Prostatectomy for Prostate Cancer

Background: Biochemical recurrence (BCR) is an indicator of prostate cancer (PCa)-specific recurrence and mortality. However, there is a lack of an effective prediction model that can be used to predict prognosis and to determine the optimal method of treatment for patients with BCR. Hence, the aim of this study was to construct a protein-based nomogram that could predict BCR in PCa.

Methods: Protein expression data of PCa patients was obtained from The Cancer Proteome Atlas (TCPA) database. Clinical data on the patients was downloaded from The Cancer Genome Atlas (TCGA) database. Lasso and Cox regression analyses were conducted to select the most significant prognostic proteins and formulate a protein signature that could predict BCR. Subsequently, Kaplan–Meier survival analysis and Cox regression analyses were conducted to evaluate the performance of the prognostic protein-based signature. Additionally, a nomogram was constructed using multivariate Cox regression analysis.

Results: We constructed a 5-protein-based prognostic prediction signature that could be used to identify high-risk and low-risk groups of PCa patients. The survival analysis demonstrated that patients with a higher BCR showed significantly worse survival than those with a lower BCR (p < 0.0001). The time-dependent receiver operating characteristic curve showed that the signature had an excellent prognostic efficiency for 1, 3, and 5-year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariate and multivariate analyses indicated that this 5-protein signature could be used as independent prognosis marker for PCa patients. Moreover, the concordance index (C-index) confirmed the predictive value of this 5-protein signature in 3, 5, and 10-year BCR overall survival (C-index: 0.764, 95% confidence interval: 0.701–0.827). Finally, we constructed a nomogram to predict BCR of PCa.

Conclusions: Our study identified a 5-protein-based signature and constructed a nomogram that could reliably predict BCR. The findings might be of paramount importance for the prediction of PCa prognosis and medical decision-making.

Subjects: Bioinformatics, oncology, urology.

Development and validation of a set of novel and robust 4-lncRNA-based nomogram predicting prostate cancer survival by bioinformatics analysis

Background and objective

Accumulating evidence shows that long noncoding RNAs (lncRNAs) possess great potential in the diagnosis and prognosis of prostate cancer (PCa). Therefore, this study aimed to construct an lncRNA-based signature to more accurately predict the prognosis of different PCa patients, so as to improve patient management and prognosis.

Methods

Through univariate and multivariate Cox regression analysis, this study constructed a 4 lncRNAs-based prognosis nomogram for the classification and prediction of survival risk in patients with PCa based on TCGA data. Then we used the data of TCGA and ICGC to verify the performance of our prediction model. The receiver operating characteristic curve was plotted for detecting and validating our prediction model sensitivity and specificity. In addition, Cox regression analysis was conducted to examine whether the signature’s prediction ability was independent of additional clinicopathological variables. Possible biological functions for those prognostic lncRNAs were predicted on those 4 protein-coding genes (PCGs) related to lncRNAs.

Results

Four lncRNAs (HOXB-AS3, YEATS2-AS1, LINC01679, PRRT3-AS1) were extracted after COX regression analysis for classifying patients into high and low-risk groups by different OS rates. As suggested by ROC analysis, our proposed model showed high sensitivity and specificity. Independent prognostic capability of the model from other clinicopathological factors was indicated through further analysis. Based on functional enrichment, those action sites for prognostic lncRNAs were mostly located in the extracellular matrix and cell membrane, and their functions are mainly associated with the adhesion, activation and transport of the components across the extracellular matrix or cell membrane.

Conclusion

Our current study successfully identifies a novel candidate, which can provide more convincing evidence for prognosis in addition to the traditional clinicopathological indicators to predict the PCa survival, and laying the foundation for offering potentially novel therapeutic treatment. Additionally, this study sheds more lights on the PCa-related molecular mechanisms.

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.

Identification and validation of a six immune-related gene signature for prediction of biochemical recurrence in localized prostate cancer following radical prostatectomy

Background

Prostate cancer (PCa) is the second lethal heterogeneous cancer among males worldwide, and approximately 20% of PCa patients following radical prostatectomy (RP) will undergo biochemical recurrence (BCR). This study is aimed to identify the immune-related gene signature that can predict BCR in localized PCa following RP.

Methods

Expression profile of genes together with clinical parameters from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database (GEO) and the immune-related genes from the Molecular Signatures Database v4.0 were applied to construct and validate the gene signature. The Cox regression analyses were conducted to identify the candidate genes and establish the gene signature. To estimate the prognostic power of the risk score, the time-dependent receiver operating characteristic (ROC) analysis and Harrell's index of concordance (C-index) were utilized. We also established a nomogram to forecast the probability of patients' survival.

Results

A total of 268 patients from the TCGA and 77 patients from GSE70770 and six immune-related genes (SCIN, THY1, TBX1, NOTCH4, MAL, BNIP3L) were eventually selected. The Kaplan-Meier analysis demonstrated that patients in the low-risk group had a significantly longer recurrence-free survival (RFS) compared to those in the high-risk group. In the multivariate Cox model, the signature was identified as an independent prognostic factor, which was significantly associated with RFS (TCGA: HR =5.232, 95% CI: 1.762-15.538, P=0.003; GSE70770: HR =2.158, 95% CI: 1.051-4.432, P=0.036). Moreover, the C-index got improved after incorporating the risk score into original clinicopathological parameters. In addition, the novel nomogram was constructed to better predict the 1-, 3- and 5-year RFS.

Conclusions

This signature could serve as an independent prognostic factor for BCR. Incorporation of our signature into traditional risk classification might further stratify patients with different prognosis, which could assist practitioners in developing clinical decision-making.

Using machine learning method to identify MYLK as a novel marker to predict biochemical recurrence in prostate cancer

Aim: This study aims to identify novel marker to predict biochemical recurrence (BCR) in prostate cancer patients after radical prostatectomy with negative surgical margin. Materials & methods: The Cancer Genome Atlas database, Gene Expression Omnibus database and Cancer Cell Line Encyclopedia database were employed. The ensemble support vector machine-recursive feature elimination method was performed to select crucial gene for BCR. Results: We identified MYLK as a novel and independent biomarker for BCR in The Cancer Genome Atlas training cohort and confirmed in four independent Gene Expression Omnibus validation cohorts. Multi-omic analysis suggested that MYLK was a DNA methylation-driven gene. Additionally, MYLK had significant positive correlations with immune infiltrations. Conclusion: MYLK was identified and validated as a novel, robust and independent biomarker for BCR in prostate cancer.

A new risk stratification system of prostate cancer to identify high-risk biochemical recurrence patients

Background

Biochemical recurrence (BCR) is considered a decisive risk factor for clinical recurrence and the metastasis of prostate cancer (PCa). Therefore, we developed and validated a signature which could be used to accurately predict BCR risk and aid in the selection of PCa treatments.

Methods

A comprehensive genome-wide analysis of data concerning PCa from previous datasets of the Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO) was performed. Lasso and Cox regression analyses were performed to develop and validate a novel signature to help predict BCR risk. Moreover, a nomogram was constructed by combining the signature and clinical variables.

Results

A total of 977 patients were involved in the study. This consisted of patients from the TCGA (n=405), GSE21034 (n=131), GSE70770 (n=193) and GSE116918 (n=248) datasets. A 9-mRNA signature was identified in the TCGA dataset (composed of C9orf152, EPHX2, ASPM, MMP11, CENPF, KIF4A, COL1A1, ASPN, and FANCI) which was significantly associated with BCR (HR =3.72, 95% CI: 2.30-6.00, P<0.0001). This signature was validated in the GSE21034 (HR =7.54, 95% CI: 3.15-18.06, P=0.019), GSE70770 (HR =2.52, 95% CI: 1.50-4.22, P=0.0025) and GSE116918 datasets (HR =4.75, 95% CI: 2.51-9.02, P=0.0035). Multivariate Cox regression and stratified analysis showed that the 9-mRNA signature was a clinical factor independent of prostate-specific antigen (PSA), Gleason score (GS), or AJCC T staging. The mean AUC for 5-year BCR-free survival predictions of the 9-mRNA signature (0.81) was higher than the AUC for PSA, GS, or AJCC T staging (0.52-0.73). Furthermore, we combined the 9-mRNA signature with PSA, GS, or AJCC T staging and demonstrated that this could enhance prognostic accuracy.

Conclusions

The proposed 9-mRNA signature is a promising biomarker for predicting BCR-free survival in PCa. However, further controlled trials are needed to validate our results and explore a role in individualized management of PCa.

Construction and Validation of a Robust Cancer Stem Cell-Associated Gene Set-Based Signature to Predict Early Biochemical Recurrence in Prostate Cancer

Background

Postoperative early biochemical recurrence (BCR) was an essential indicator for recurrence and distant metastasis of prostate cancer (PCa). The aim of this study was to construct a cancer stem cell- (CSC-) associated gene set-based signature to identify a subgroup of PCa patients who are at high risk of early BCR.

Methods

The PCa dataset from The Cancer Genome Atlas (TCGA) was randomly separated into discovery and validation set. Patients in discovery set were divided into early BCR group and long-term survival group. Propensity score matching analysis and differentially expressed gene selection were used to identify candidate CSC-associated genes. The LASSO Cox regression model was finally performed to filter the most useful prognostic CSC-associated genes for predicting early BCR.

Results

By applying the LASSO Cox regression model, we built a thirteen-CSC-associated gene-based early BCR-predicting signature. In the discovery set, patients in high-risk group showed significantly poorer BCR free survival than that patients in low-risk group (HR: 4.91, 95% CI: 2.75–8.76, P < 0.001). The results were further validated in the internal validation set (HR: 2.99, 95% CI: 1.34–6.70, P = 0.005). Time-dependent ROC at 1 year suggested that the CSC gene signature (AUC = 0.800) possessed better predictive value than any other clinicopathological features in the entire TCGA cohort. Additionally, survival decision curve analysis revealed a considerable clinical usefulness of the CSC gene signature.

Conclusions

We successfully developed a CSC-associated gene set-based signature that can accurately predict early BCR in PCa cancer.

A three-lncRNA signature of pretreatment biopsies predicts pathological response and outcome in esophageal squamous cell carcinoma with neoadjuvant chemoradiotherapy

BACKGROUND

Current strategies are insufficient to predict pathologically complete response (pCR) for esophageal squamous cell carcinomas (ESCCs) before treatment. Here, we aim to develop a novel long noncoding RNA (lncRNA) signature for pCR and outcome prediction of ESCCs through a multicenter analysis for a Chinese population.

METHODS

Differentially expressed lncRNAs (DELs) between pCRs and less than pCR (

RESULTS

Twelve DELs were identified from Guangzhou cohort and six lncRNAs were verified. Then, a classifier of three lncRNAs (SCAT1, PRKAG2-AS1, and FLG-AS1) was established and achieved a high accuracy with an area under the receiver operating characteristic curve (AUC) of 0.952 in the training cohort, which was well validated in the internal validation cohort and external cohort with the AUCs of 0.856 and 0.817, respectively. Furthermore, the predictive score was identified as the only independent predictor for pCR. Patients with high discriminant score showed a significantly longer overall and relapse-free survival (P < .05).

CONCLUSIONS

We developed the first and applicable three-lncRNA signature of pCR and outcome prediction, which is robust and reproducible in multicenter cohorts for ESCCs with nCRT.

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Key Words

• esophageal squamous cell carcinoma
• individualized medicine
• lncRNAs
• neoadjuvant chemoradiotherapy
• pathologically complete response

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MESH Headings Collapse

Grants

• 2017-I2M-1-005 CAMS Innovation Fund for Medical Sciences
• 2016-I2M-1-001 CAMS Innovation Fund for Medical Sciences
• 2016YFC1303201 National Key R&D Program of China
• 81802299 National Natural Science Foundation of China
• 81502514 National Natural Science Foundation of China
• 3332018070 Fundamental Research Funds for the Central Universities
• 2018YFC1312105 National Key Basic Research Development Plan
• 7204291 Beijing Natural Science Foundation
• LC2019B18 Beijing Hope Run Special Fund of Cancer Foundation of China
• National Natural Science Foundation of China
• Fundamental Research Funds for the Central Universities
• Beijing Natural Science Foundation

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Affiliation(s)

Chaoqi Zhang Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Zhihui Zhang Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Guochao Zhang Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Liyan Xue Department of PathologyNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Haijun Yang Department of PathologyAnyang Cancer HospitalThe Fourth Affiliated Hospital of Henan University of Science and TechnologyAnyangHenanChina
Yuejun Luo Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Xiaoli Zheng Department of radiotherapyThe Affiliated Cancer hospital of Zhengzhou UniversityZhengzhouHenanChina
Yonglei Zhang Department of General SurgeryThe Affiliated Cancer Hospital of Zhengzhou UniversityZhengzhouHenanChina
Yufen Yuan Department of PathologyAnyang Cancer HospitalThe Fourth Affiliated Hospital of Henan University of Science and TechnologyAnyangHenanChina
Ruixue Lei Department of PathologyAnyang Cancer HospitalThe Fourth Affiliated Hospital of Henan University of Science and TechnologyAnyangHenanChina
Zhaoyang Yang Department of PathologyNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Bo Zheng Department of PathologyNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Zhen Zhang Biotherapy CenterThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
Le Wang Department of OtologyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
Yun Che Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Sihui Wang Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Feng Wang Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Lingling Fang Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Qingpeng Zeng Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Jiagen Li Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Shugeng Gao Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Qi Xue Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Nan Sun Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
Jie He Department of Thoracic SurgeryNational Cancer CenterNational Clinical Research Center for CancerCancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina

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A risk signature-based on metastasis-associated genes to predict survival of patients with osteosarcoma

Osteosarcoma (OS) is the most common primary solid malignant bone tumor, and its metastasis is a prominent cause of high mortality in patients. In this study, a prognosis risk signature was constructed based on metastasis-associated genes. Four microarrays datasets with clinical information were downloaded from Gene Expression Omnibus, and 256 metastasis-associated genes were identified by limma package. Further, a protein-protein interaction network was constructed, and survival analysis was performed using data from the Therapeutically Applicable Research to Generate Effective Treatments data matrix, identifying 19 genes correlated with prognosis. Six genes were selected by the least absolute shrinkage and selection operator regression for multivariate cox analysis. Finally, a three-gene (MYC, CPE, and LY86) risk signature was constructed, and datasets GSE21257 and GSE16091 were used to validate the prediction efficiency of the signature. The survival times of low- and high-risk groups were significantly different in the training set and validation set. Additionally, gene set enrichment analysis revealed that the genes in the signature may affect the cell cycle, gap junctions, and interleukin-6 production. Therefore, the three-gene survival risk signature could potentially predict the prognosis of patients with OS. Further, proteins encoded by CPE and LY86 may provide novel insights into the prediction of OS prognosis and therapeutic targets.

Assessment of biochemical recurrence of prostate cancer (Review)

The assessment of the risk of biochemical recurrence (BCR) is critical in the management of males with prostate cancer (PC). Over the past decades, a comprehensive effort has been focusing on improving risk stratification; a variety of models have been constructed using PC-associated pathological features and molecular alterations occurring at the genome, protein and RNA level. Alterations in RNA expression (lncRNA, miRNA and mRNA) constitute the largest proportion of the biomarkers of BCR. In this article, we systemically review RNA-based BCR biomarkers reported in PubMed according to the PRISMA guidelines. Individual miRNAs, mRNAs, lncRNAs and multi-gene panels, including the commercially available signatures, Oncotype DX and Prolaris, will be discussed; details related to cohort size, hazard ratio and 95% confidence intervals will be provided. Mechanistically, these individual biomarkers affect multiple pathways critical to tumorigenesis and progression, including epithelial-mesenchymal transition (EMT), phosphatase and tensin homolog (PTEN), Wnt, growth factor receptor, cell proliferation, immune checkpoints and others. This variety in the mechanisms involved not only validates their associations with BCR, but also highlights the need for the coverage of multiple pathways in order to effectively stratify the risk of BCR. Updates of novel biomarkers and their mechanistic insights are considered, which suggests new avenues to pursue in the prediction of BCR. Additionally, the management of patients with BCR and the potential utility of the stratification of the risk of BCR in salvage treatment decision making for these patients are briefly covered. Limitations will also be discussed.