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Identification of Biomarkers for Cervical Cancer Radiotherapy Resistance Based on RNA Sequencing Data.

ABSTRACT: Cervical cancer as a common gynecological malignancy threatens the health and lives of women. Resistance to radiotherapy is the primary cause of treatment failure and is mainly related to difference in the inherent vulnerability of tumors after radiotherapy. Here, we investigated signature genes associated with poor response to radiotherapy by analyzing an independent cervical cancer dataset from the Gene Expression Omnibus, including pre-irradiation and mid-irradiation information. A total of 316 differentially expressed genes were significantly identified. The correlations between these genes were investigated through the Pearson correlation analysis. Subsequently, random forest model was used in determining cancer-related genes, and all genes were ranked by random forest scoring. The top 30 candidate genes were selected for uncovering their biological functions. Functional enrichment analysis revealed that the biological functions chiefly enriched in tumor immune responses, such as cellular defense response, negative regulation of immune system process, T cell activation, neutrophil activation involved in immune response, regulation of antigen processing and presentation, and peptidyl-tyrosine autophosphorylation. Finally, the top 30 genes were screened and analyzed through literature verification. After validation, 10 genes (KLRK1, LCK, KIF20A, CD247, FASLG, CD163, ZAP70, CD8B, ZNF683, and F10) were to our objective. Overall, the present research confirmed that integrated bioinformatics methods can contribute to the understanding of the molecular mechanisms and potential therapeutic targets underlying radiotherapy resistance in cervical cancer.

SUBMITTER: Feng Y

PROVIDER: S-EPMC8369412 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Ovarian aging leads to reproductive and endocrine dysfunction, causing the disorder of multiple organs in the body and even declined quality of offspring's health. However, few studies have investigated the changes in gene expression profile in the ovarian aging process. Here, we applied integrated bioinformatics to screen, identify, and validate the critical pathogenic genes involved in ovarian aging and uncover potential molecular mechanisms. The expression profiles of GSE84078 were downloaded from the Gene Expression Omnibus (GEO) database, which included the data from ovarian samples of 10 normal C57BL/6 mice, including old (21-22 months old, ovarian failure period) and young (5-6 months old, reproductive bloom period) ovaries. First, we filtered 931 differentially expressed genes (DEGs), including 876 upregulated and 55 downregulated genes through comparison between ovarian expression data from old and young mice. Functional enrichment analysis showed that biological functions of DEGs were primarily immune response regulation, cell-cell adhesion, and phagosome pathway. The most closely related genes among DEGs (Tyrobp, Rac2, Cd14, Zap70, Lcp2, Itgb2, H2-Ab1, and Fcer1g) were identified by constructing a protein-protein interaction (PPI) network and consequently verified using mRNA and protein quantitative detection. Finally, the immune cell infiltration in the ovarian aging process was also evaluated by applying CIBERSORT, and a correlation analysis between hub genes and immune cell type was also performed. The results suggested that plasma cells and naïve CD4+ T cells may participate in ovarian aging. The hub genes were positively correlated with memory B cells, plasma cells, M1 macrophages, Th17 cells, and immature dendritic cells. In conclusion, this study indicates that screening for DEGs and pathways in ovarian aging using bioinformatic analysis could provide potential clues for researchers to unveil the molecular mechanism underlying ovarian aging. These results could be of clinical significance and provide effective molecular targets for the treatment of ovarian aging.

Project description:BackgroundWhether keratoconus (KC) is an inflammatory disease is currently debated. Hence, we aimed to investigate the immune-related features of KC based on single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data.MethodsscRNA-seq data were obtained from the Genome Sequence Archive (GSA), bulk RNA-seq data were obtained from the Gene Expression Omnibus (GEO), and immune-associated genes(IAGs) were obtained from the ImmPort database. Cell clusters of KC were annotated, and different cell clusters were then selected. The IAG score of each cell was calculated using the AUCell package. Three bulk RNA-seq datasets were merged and used to identify the differentially expressed genes (DEGs), biological functions, and immune characteristics. Weighted gene coexpression network analysis (WGCNA) was used to select the IAG score-related hub genes. Based on scRNA-seq and bulk RNA-seq analyses, three machine learning algorithms, including random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO) regression analysis, were used to identify potential prognostic markers for KC. A predictive nomogram was developed based on prognostic markers.ResultsSix cell clusters were identified in KC, and decreased corneal stromal cell-5 (CSC-5) and increased CSC-6 were found in KC. CSC and immune cell clusters had the highest IAG scores. The bulk RNA-seq analysis identified 1362 DEGs (553 upregulated and 809 downregulated) in KC. We found different immune cell populations and differentially expressed cytokines in KC. More than three key IAG score-related modules and 367 genes were identified. By integrating the scRNA-seq and bulk RNA-seq analyses, 250 IAGs were selected and then incorporated into three machine learning models, and 10 IAGs (CEP112, FYN, IFITM1, IGFBP5, LPIN2, MAP1B, RNASE1, RUNX3, SMIM10, and SRGN) were identified as potential prognostic genes that were significantly associated with cytokine and matrix metalloproteinase(MMP)1-14 expression. Finally, a predictive nomogram was constructed and validated.ConclusionTaken together, our results identified CSCs and immune cell clusters that may play a key role during KC progression by regulating immunological features and maintaining cell stability.

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Identification of Biomarkers for Cervical Cancer Radiotherapy Resistance Based on RNA Sequencing Data.

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