Prognostic value of potential biomarkers in prostate cancer via bioinformatic analysis

Background:The Genotype-Tissue Expression was used to expanded normal tissue of the Cancer Genome Atlas database.This study aimed to investigate genes associated with the pathogenesis and prognosis of prostate cancer.Methods:We conducted prognostic related genes for prostate cancer by using transcriptome data from the Genotype-Tissue Expression Project and the Cancer Genome Atlas data sources,which were analyzed using an integrated bioinformatics strategy.Clinically significant modules were distinguished,and GO and KEGG analysis were used to Database for Annotation,Visualization and Integrated Discovery.Further annotation was performed through Gene set enrichment analysis.Logistic regression was carried out to analyze the associations between clinicopathologic characteristics and the hub genes.Logistic regression model and survival analysis were performed.Results:By using data available from the Cancer Genome Atlas and the Genotype-Tissue Expression databases,we here show that 53 differential expression genes were identified.Through GO and KEGG analysis a prognostic related gene signature consisted of GOLM1,EIF4A1,ABCC4,RPL7P16,NPIPB12 and PCA3 was constructed with a good performance in predicting overall survivals.The majority of the six hub genes were associated with clinical characteristics of prostate cancer.Conclusion:These genes might be considered as new targets for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy since they showed differently expressed in prostate cancer and correlate with overall survival prognosis.

New perspectives in prognostication of hepatocellular carcinoma:The role and clinical implications of transient receptor potential family genes

The study titled“Transient receptor potential-related risk model predicts prognosis of hepatocellular carcinoma patients”is a significant contribution to hepatocellular carcinoma(HCC)research,highlighting the role of transient receptor potential(TRP)family genes in the disease’s progression and prognosis.Utilizing data from The Cancer Genome Atlas database,it establishes a new risk assessment model,emphasizing the interaction of TRP genes with tumor proliferation pathways,key metabolic reactions like retinol metabolism,and the tumor immune microenvironment.Notably,the overexpression of the TRPC1 gene in HCC correlates with poorer patient survival outcomes,suggesting its potential as a prognostic biomarker and a target for personalized therapy,particularly in strategies combining immunotherapy and anti-TRP agents.

Comparative Analysis of Protein-Protein Interactions in Cancer-Associated Genes

Protein-protein interactions （PPIs） have been widely studied to understand the biological processes or molecular functions associated with different disease systems like cancer. While focused studies on individual cancers have generated valuable information, global and comparative analysis of datasets from different cancer types has not been done. In this work, we carried out bioinformatic analysis of PPIs corresponding to differentially expressed genes from microarrays of various tumor tissues （belonging to bladder, colon, kidney and thyroid cancers） and compared their associated biological processes and molecular functions （based on Gene Ontology terms）. We identified a set of processes or functions that are common to all these cancers, as well as those that are specific to only one or partial cancer types. Similarly, protein interaction networks in nucleic acid metabolism were compared to identify the common/specific clusters of proteins across different cancer types. Our results provide a basis for further experimental investigations to study protein interaction networks associated with cancer. The methodology developed in this work can also be applied to study similar disease systems.

Genome-wide identification of cancer-related polyadenylated and non-polyadenylated RNAs in human breast and lung cell lines

Eukaryotic mRNAs consist of two forms of transcripts:poly(A)+ and poly(A),based on the presence or absence of poly(A) tails at the 3 end.Poly(A)+ mRNAs are mainly protein coding mRNAs,whereas the functions of poly(A) mRNA are largely unknown.Previous studies have shown that a significant proportion of gene transcripts are poly(A) or bimorphic(containing both poly(A)+ and poly(A) transcripts).We compared the expression levels of poly(A) and poly(A)+ RNA mRNAs in normal and cancer cell lines.We also investigated the potential functions of these RNA transcripts using an integrative workflow to explore poly(A)+ and poly(A) transcriptome sequences between a normal human mammary gland cell line(HMEC) and a breast cancer cell line(MCF-7),as well as between a normal human lung cell line(NHLF) and a lung cancer cell line(A549).The data showed that normal and cancer cell lines differentially express these two forms of mRNA.Gene ontology(GO) annotation analyses hinted at the functions of these two groups of transcripts and grouped the differentially expressed genes according to the form of their transcript.The data showed that cell cycle-,apoptosis-,and cell death-related functions corresponded to most of the differentially expressed genes in these two forms of transcripts,which were also associated with the cancers.Furthermore,translational elongation and translation functions were also found for the poly(A) protein-coding genes in cancer cell lines.We demonstrate that poly(A) transcripts play an important role in cancer development.