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Four Novel Prognostic Genes Related to Prostate Cancer Identified Using Co-expression Structure Network Analysis.

ABSTRACT: Prostate cancer (PCa) is one of the most common malignancies for males, but very little is known about its pathogenesis. This study aimed to identify novel biomarkers associated with PCa prognosis and elucidate the underlying molecular mechanism. First, The Cancer Genome Atlas (TCGA) RNA-sequencing data were utilized to identify differentially expressed genes (DEGs) between tumor and normal samples. The DEGs were then applied to construct a co-expression and mined using structure network analysis. The magenta module that was highly related to the Gleason score (r = 0.46, p = 3e-26) and tumor stage (r = 0.38, p = 2e-17) was screened. Subsequently, all genes of the magenta module underwent function annotation. From the key module, CCNA2, CKAP2L, NCAPG, and NUSAP1 were chosen as the four candidate genes. Finally, internal (TCGA) and external data sets (GSE32571, GSE70770, and GSE141551) were combined to validate and predict the value of real hub genes. The results show that the above genes are up-regulated in PCa samples, and higher expression levels show significant association with higher Gleason scores and tumor T stage. Moreover, receiver operating characteristic curve and survival analysis validate the excellent value of hub genes in PCa progression and prognosis. In addition, the protein levels of these four genes also remain higher in tumor tissues when compared with normal tissues. Gene set enrichment analysis and gene set variation analysis for a single gene reveal the close relation with cell proliferation. Meanwhile, 11 small molecular drugs that have the potential to treat PCa were also screened. In conclusion, our research identified four potential prognostic genes and several candidate molecular drugs for treating PCa.

SUBMITTER: Feng T

PROVIDER: S-EPMC8078837 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundColorectal cancer (CRC) is one of the most prevalent malignancies and the third most lethal cancer globally. The most reported histological subtype of CRC is colon adenocarcinoma (COAD). The zinc transport pathway is critically involved in various tumors, and its anti-tumor effect may be through improving immune function. However, the Zn transport pathway in COAD has not been reported.MethodsThe determination of Zn transport-related genes in COAD was carried out through single-cell analysis of the GSE 161277 obtained from the GEO dataset. Subsequently, a weighted co-expression network analysis of the TCGA cohort was performed. Then, the prognostic model was conducted utilizing univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Functional enrichment, immune microenvironment, and survival analyses were also carried out. Consensus clustering analysis was utilized to verify the validity of the prognostic model and explore the immune microenvironment. Ultimately, cell experiments, including CCK-8,transwell and scratch assays, were performed to identify the function of LRRC59 in COAD.ResultsAccording to the Zn transport-related prognostic model, the individuals with COAD in TCGA and GEO databases were classified into high- and low-risk groups. The group with low risk had a comparatively more favorable prognosis. Two groups had significant variations in the immune infiltration, MHC, and the expression of genes related to the immune checkpoint. The cell experiments indicated that the proliferation, migration, and invasion of the HCT-116, DLD-1, and RKO cell lines were considerably increased after LRRC59 knockdown. It proved that LRRC59 was indeed a protective factor for COAD.ConclusionA prognostic model for COAD was developed using zinc transport-related genes. This model can efficiently assess the immune microenvironment and prognosis of individuals with COAD. Subsequently, the function of LRRC59 in COAD was validated via cell experiments, highlighting its potential as a biomarker.

Project description:BackgroundAs a severe hematological malignancy in adults, acute myeloid leukemia (AML) is characterized by high heterogeneity and complexity. Emerging evidence highlights the importance of the tumor immune microenvironment and lipid metabolism in cancer progression. In this study, we comprehensively evaluated the expression profiles of genes related to lipid metabolism and immune modifications to develop a prognostic risk signature for AML.MethodsFirst, we extracted the mRNA expression profiles of bone marrow samples from an AML cohort from The Cancer Genome Atlas database and employed Cox regression analysis to select prognostic hub genes associated with lipid metabolism and immunity. We then constructed a prognostic signature with hub genes significantly related to survival and validated the stability and robustness of the prognostic signature using three external datasets. Gene Set Enrichment Analysis was implemented to explore the underlying biological pathways related to the risk signature. Finally, the correlation between signature, immunity, and drug sensitivity was explored.ResultsEight genes were identified from the analysis and verified in the clinical samples, including APOBEC3C, MSMO1, ATP13A2, SMPDL3B, PLA2G4A, TNFSF15, IL2RA, and HGF, to develop a risk-scoring model that effectively stratified patients with AML into low- and high-risk groups, demonstrating significant differences in survival time. The risk signature was negatively related to immune cell infiltration. Samples with AML in the low-risk group, as defined by the risk signature, were more likely to be responsive to immunotherapy, whereas those at high risk responded better to specific targeted drugs.ConclusionsThis study reveals the significant role of lipid metabolism- and immune-related genes in prognosis and demonstrated the utility of these signature genes as reliable bioinformatic indicators for predicting survival in patients with AML. The risk-scoring model based on these prognostic signature genes holds promise as a valuable tool for individualized treatment decision-making, providing valuable insights for improving patient prognosis and treatment outcomes in AML.

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Four Novel Prognostic Genes Related to Prostate Cancer Identified Using Co-expression Structure Network Analysis.

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