Identification of key genes and multiple molecular pathways of metastatic process in prostate cancer

Identification of cuproptosis-related genes for predicting the development of prostate cancer

Copper can be toxic at very high intracellular concentrations and can inhibit prostate cancer (PCa) progression. Recently, a study reported the mechanism of cuproptosis and the potentially associated genes. However, the function of these cuproptosis-related genes in PCa remains unknown. Based on the RNA sequence and clinical data from public databases, we analyzed the clinical value of cuproptosis-related genes in PCa. DLD, DLAT, PDHA1, and CDKN2A were expressed differently between normal and PCa tissues. The FDX1, LIAS, DLAT, GLS, and CDKN2A genes can affect PCa progression, while PDHA1 and CDKN2A influence the patients' disease-free survival (DFS) status. The expression of LIAS, LIPT1, DLAT, and PDHB did not alter upon the incidence of PCa in Chinese patients. A constructed regression model showed that FDX1, PDHA1, MTF1, and CDKN2A can be risk factors leading to PCa in both Western and Chinese patients with PCa. The lasso regression model reflected that these genes can affect the patients' DFS status. Additionally, the cuproptosis-related genes were associated with immune cell infiltration. We also verified the high expression of PDHA1 and CDKN2A, in clinical samples. In conclusion, we identified a novel cuproptosis-related gene signature for predicting the development of PCa.

Identification of hub genes predicting the development of prostate cancer from benign prostate hyperplasia and analyzing their clinical value in prostate cancer by bioinformatic analysis

Prostate cancer (PCa) and benign prostate hyperplasia (BPH) are commonly encountered diseases in males. Studies showed that genetic factors are responsible for the occurrences of both diseases. However, the genetic association between them is still unclear. Gene Expression Omnibus (GEO) database can help determine the differentially expressed genes (DEGs) between BPH and PCa. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were utilized to find pathways DEGs enriched. The STRING database can provide a protein-protein interaction (PPI) network, and find hub genes in PPI network. R software was used to analyze the clinical value of hub genes in PCa. Finally, the function of these hub genes was tested in different databases, clinical samples, and PCa cells. Fifteen up-regulated and forty-five down-regulated genes were found from GEO database. Seven hub genes were found in PPI network. The expression and clinical value of hub genes were analyzed by The Cancer Genome Atlas (TCGA) data. Except CXCR4, all hub genes expressed differently between tumor and normal samples. Exclude CXCR4, other hub genes have diagnostic value in predicting PCa and their mutations can cause PCa. The expression of CSRP1, MYL9 and SNAI2 changed in different tumor stage. CSRP1 and MYH11 could affect disease-free survival (DFS). Same results reflected in different databases. The expression and function of MYC, MYL9, and SNAI2, were validated in clinical samples and PCa cells. In conclusion, seven hub genes among sixty DEGs may be achievable targets for predicting which BPH patients may later develop PCa and they can influence the progression of 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.

Development and Verification of a Prostate Cancer Prognostic Signature Based on an Immunogenomic Landscape Analysis

Purpose

Prostate cancer (PCa) has a high incidence among older men. Until now, there are no immunological markers available to predict PCa patients’ survival. Therefore, it is necessary to explore the immunological characteristics of PCa.

Methods

First, we retrieved RNA-seq and clinical data of 499 PCa and 52 normal prostate tissue samples from the Cancer Genome Atlas (TCGA). We identified 193 differentially expressed immune-related genes (IRGs) between PCa and normal prostate tissues. Functional enrichment analyses showed that the immune system can participate in PCa initiation. Then, we constructed a correlation network between transcription factors (TFs) and IRGs. We performed univariate and multivariate Cox regression analyses and identified five key prognostic IRGs (S100A2, NOX1, IGHV7-81, AMH, and AGTR1). Finally, a predictive nomogram was established and verified by the C-index.

Results

We successfully constructed and validated an immune-related PCa prediction model. The signature could independently predict PCa patients’ survival. Results showed that high-immune-risk patients were correlated with advanced stage. We also validated the S100A2 expression in vitro using PCa and normal prostate tissues. We found that higher S100A2 expressions were related to lower biochemical recurrences. Additionally, higher AMH expressions were related to higher Gleason score, lymph node metastasis and positive rate, and tumor stages, and higher ATGR1 expressions were related to lower PSA value.

Conclusion

Overall, we detected five IRGs (S100A2, NOX1, IGHV7-81, AMH, and AGTR1) that can be used as independent PCa prognostic factors.

Feature selection with ensemble learning for prostate cancer diagnosis from microarray gene expression

Cancer diagnosis using machine learning algorithms is one of the main topics of research in computer-based medical science. Prostate cancer is considered one of the reasons that are leading to deaths worldwide. Data analysis of gene expression from microarray using machine learning and soft computing algorithms is a useful tool for detecting prostate cancer in medical diagnosis. Even though traditional machine learning methods have been successfully applied for detecting prostate cancer, the large number of attributes with a small sample size of microarray data is still a challenge that limits their ability for effective medical diagnosis. Selecting a subset of relevant features from all features and choosing an appropriate machine learning method can exploit the information of microarray data to improve the accuracy rate of detection. In this paper, we propose to use a correlation feature selection (CFS) method with random committee (RC) ensemble learning to detect prostate cancer from microarray data of gene expression. A set of experiments are conducted on a public benchmark dataset using 10-fold cross-validation technique to evaluate the proposed approach. The experimental results revealed that the proposed approach attains 95.098% accuracy, which is higher than related work methods on the same dataset.

Site-Specific and Common Prostate Cancer Metastasis Genes as Suggested by Meta-Analysis of Gene Expression Data

Anticancer therapies mainly target primary tumor growth and little attention is given to the events driving metastasis formation. Metastatic prostate cancer, in comparison to localized disease, has a much worse prognosis. In the work presented here, groups of genes that are common to prostate cancer metastatic cells from bones, lymph nodes, and liver and those that are site-specific were delineated. The purpose of the study was to dissect potential markers and targets of anticancer therapies considering the common characteristics and differences in transcriptional programs of metastatic cells from different secondary sites. To that end, a meta-analysis of gene expression data of prostate cancer datasets from the GEO database was conducted. Genes with differential expression in all metastatic sites analyzed belong to the class of filaments, focal adhesion, and androgen receptor signaling. Bone metastases undergo the largest transcriptional changes that are highly enriched for the term of the chemokine signaling pathway, while lymph node metastasis show perturbation in signaling cascades. Liver metastases change the expression of genes in a way that is reminiscent of processes that take place in the target organ. Survival analysis for the common hub genes revealed involvements in prostate cancer prognosis and suggested potential biomarkers.

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.

Genomic pan-cancer classification using image-based deep learning

Accurate cancer type classification based on genetic mutation can significantly facilitate cancer-related diagnosis. However, existing methods usually use feature selection combined with simple classifiers to quantify key mutated genes, resulting in poor classification performance. To circumvent this problem, a novel image-based deep learning strategy is employed to distinguish different types of cancer. Unlike conventional methods, we first convert gene mutation data containing single nucleotide polymorphisms, insertions and deletions into a genetic mutation map, and then apply the deep learning networks to classify different cancer types based on the mutation map. We outline these methods and present results obtained in training VGG-16, Inception-v3, ResNet-50 and Inception-ResNet-v2 neural networks to classify 36 types of cancer from 9047 patient samples. Our approach achieves overall higher accuracy (over 95%) compared with other widely adopted classification methods. Furthermore, we demonstrate the application of a Guided Grad-CAM visualization to generate heatmaps and identify the top-ranked tumor-type-specific genes and pathways. Experimental results on prostate and breast cancer demonstrate our method can be applied to various types of cancer. Powered by the deep learning, this approach can potentially provide a new solution for pan-cancer classification and cancer driver gene discovery. The source code and datasets supporting the study is available at https://github.com/yetaoyu/Genomic-pan-cancer-classification.

Role of methyltransferase-like enzyme 3 and methyltransferase-like enzyme 14 in urological cancers

N6-methyladenosine (m6A) modifications can be found in eukaryotic messenger RNA (mRNA), long non-coding RNA (lncRNA), and microRNA (miRNA). Several studies have demonstrated a close relationship between m6A modifications and cancer cells. Methyltransferase-like enzyme 3 (METTL3) and methyltransferase-like enzyme 14 (METTL14) are two major enzymes involved in m6A modifications that play vital roles in various cancers. However, the roles and regulatory mechanisms of METTL3 and METTL14 in urological cancers are largely unknown. In this review, we summarize the current research results for METTL3 and METTL14 and identify potential pathways involving these enzymes in kidney, bladder, prostate, and testicular cancer. We found that METTL3 and METTL14 have different expression patterns in four types of urological cancers. METTL3 is highly expressed in bladder and prostate cancer and plays an oncogenic role on cancer cells; however, its expression and role are opposite in kidney cancer. METTL14 is expressed at low levels in kidney and bladder cancer, where it has a tumor suppressive role. Low METTL3 or METTL14 expression in cancer cells negatively regulates cell growth-related pathways (e.g., mTOR, EMT, and P2XR6) but positively regulates cell death-related pathways (e.g., P53, PTEN, and Notch1). When METTL3 is highly expressed, it positively regulates the NF-kB and SHH-GL1pathways but negatively regulates PTEN. These results suggest that although METTL3 and METTL14 have different expression levels and regulatory mechanisms in urological cancers, they control cancer cell fate via cell growth- and cell death-related pathways. These findings suggest that m6A modification may be a potential new therapeutic target in urological cancer.