Screening and identification of key biomarkers in prostate cancer using bioinformatics

Identification of bicalutamide resistance-related genes and prognosis prediction in patients with prostate cancer

Background

Prostate cancer (PCa) is the second most common type of cancer and the fifth leading cause of cancer-related death in men. Androgen deprivation therapy (ADT) has become the first-line therapy for inhibiting PCa progression; however, nearly all patients receiving ADT eventually progress to castrate-resistant prostate cancer. Therefore, this study aimed to identify hub genes related to bicalutamide resistance in PCa and provide new insights into endocrine therapy resistance.

Methods

The data were obtained from public databases. Weighted correlation network analysis was used to identify the gene modules related to bicalutamide resistance, and the relationship between the samples and disease-free survival was analyzed. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed, and hub genes were identified. The LASSO algorithm was used to develop a bicalutamide resistance prognostic model in patients with PCa, which was then verified. Finally, we analyzed the tumor mutational heterogeneity and immune microenvironment in both groups.

Results

Two drug resistance gene modules were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that both modules are involved in RNA splicing. The protein-protein interaction network identified 10 hub genes in the brown module LUC7L3, SNRNP70, PRPF3, LUC7L, CLASRP, CLK1, CLK2, U2AF1L4, NXF1, and THOC1) and 13 in the yellow module (PNN, PPWD1, SRRM2, DHX35, DMTF1, SALL4, MTA1, HDAC7, PHC1, ACIN1, HNRNPH1, DDX17, and HDAC6). The prognostic model composed of RNF207, REC8, DFNB59, HOXA2, EPOR, PILRB, LSMEM1, TCIRG1, ABTB1, ZNF276, ZNF540, and DPY19L2 could effectively predict patient prognosis. Genomic analysis revealed that the high- and low-risk groups had different mutation maps. Immune infiltration analysis showed a statistically significant difference in immune infiltration between the high- and low-risk groups, and that the high-risk group may benefit from immunotherapy.

Conclusion

In this study, bicalutamide resistance genes and hub genes were identified in PCa, a risk model for predicting the prognosis of patients with PCa was constructed, and the tumor mutation heterogeneity and immune infiltration in high- and low-risk groups were analyzed. These findings offer new insights into ADT resistance targets and prognostic prediction in patients with PCa.

Screening a novel six critical gene-based system of diagnostic and prognostic biomarkers in prostate adenocarcinoma patients with different clinical variables

The mechanisms behind prostate adenocarcinoma (PRAD) pathogenicity remain to be understood due to tumor heterogeneity. In the current study, we identified by microarray technology six eligible real hub genes from already identified hub genes through a systematic in silico approach that could be useful to lower the heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment. For this purpose, microarray technology-based, already-identified PRAD-associated hub genes were initially explored through extensive literature mining; then, a protein-protein interaction (PPI) network construction of those hub genes and its analysis helped us to identify six most critical genes (real hub genes). Various online available expression databases were then used to explore the tumor driving, diagnostic, and prognostic roles of real hub genes in PRAD patients with different clinicopathologic variables. In total, 124 hub genes were extracted from the literature, and among those genes, six, including CDC20, HMMR, AURKA, CDK1, ASF1B, and CCNB1 were identified as real hub genes by the degree method. Further expression analysis revealed the significant up-regulation of real hub genes in PRAD patients of different races, age groups, and nodal metastasis status relative to controls. Moreover, through correlational analyses, different valuable correlations between treal hub genes expression and different other data (promoter methylation status, genetic alterations, overall survival (OS), tumor purity, CD4+ T, CD8+ T immune cells infiltration, and different other mutant genes and a few more) across PRAD samples were also documented. Ultimately, from this study, a few important transcription factors (TFS), miRNAs, and chemotherapeutic drugs showing a great therapeutic potential were also identified. In conclusion, we have discovered a set of six real hub genes that might be utilized as new biomarkers for lowering heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment.

Association of STAT3, PTPRT, TNK2-AS1, LINC-ROR Genes Expression Level with Prostate Cancer and Benign Prostatic Hyperplasia

Background: Prostate cancer (PCa) and benign prostate hyperplasia (BPH) are highly prevalent heterogeneous disorders among men. Since angiogenesis is the key step in cancer progression, the deregulation of genes involved in this process may play a role in cancer development. Objectives: We evaluated the expression level of 4 angiogenesis-related genes including signal transducer and activator of transcription 3 (STAT3), protein tyrosine phosphatase receptor type T (PTPRT), TNK2 antisense RNA 1 (TNK2-AS1), and long intergenic non-protein coding rna-regulator of reprogramming (LINC-ROR) in patients with PCa and BPH. Methods: The expression level of STAT3, PTPRT, TNK2-AS1, and LINC-ROR genes in tumoral and adjacent non-cancerous tissue (ANCT) samples of 50 PCa patients and tissue samples from 50 BPH patients were evaluated, using the real-time PCR method. The statistical analysis was performed to evaluate the association between genes expression and clinicopathological characteristics of patients with PCa. Results: The expression level of STAT3 and LINC-ROR was upregulated in tumoral tissues compared to ANCTs (P < 0.0001 for both). Only the expression level of STAT3 in PCa was higher than in BPH tissues (P = 0.001). The elevated expression of STAT3 was associated with the higher grade group of the tumor (P = 0.03). Also, the high expression level of PTPRT and LINC-ROR genes was associated with a higher stage of cancer in patients with PCa (P = 0.002, P = 0.0001 respectively). The STAT3 gene transcript level had an excellent diagnostic power for discrimination between tumoral tissue and the ANCTs with an area under the curve (AUC) of 0.93. Conclusions: The higher expression of STAT3 and LINC-ROR suggested a role in the pathogenesis of PCa in higher stages. Also, STAT3 expression level could be suggested as a potential biomarker for PCa in combination with PSA level.

Bioinformatic analysis of dysregulated proteins in prostate cancer patients reveals putative urinary biomarkers and key biological pathways

Prostate cancer (PCa) is one of the most common cancer types among men. The quantification of prostate-specific antigen used for PCa detection has revealed limited applicability. Thus, it is crucial to identify new minimally invasive biomarkers for PCa. It is believed that the integration of proteomics data from different studies is vital for identifying new biomarkers for PCa, but studies carried out in this regard have few converging results. Using a different approach, this study aimed to unveil molecular features consistently dysregulated in PCa and potential urinary biomarkers for PCa. The novelty of this analysis relies on the comparison of urinary and tissue proteomes from PCa patients and consequent exclusion of kidney and bladder cancer interference. The conducted bioinformatic analysis revealed molecular processes dysregulated in urine from PCa patients that mirror the alterations in prostate tumor tissue. To identify putative urinary biomarkers, proteins previously detected in kidney and bladder tissues were eliminated from the final list of potential urinary biomarkers for PCa. After a detailed analysis, MSMB, KLK3, ITIH4, ITIH2, HPX, GP2, APOA2 and AZU1 proteins stood out as candidate urinary biomarkers for PCa.