Four-gene signature predicting overall survival and immune infiltration in hepatocellular carcinoma by bioinformatics analysis with RT‒qPCR validation

Guan, Renguo; Zou, Jingwen; Mei, Jie; Deng, Min; Guo, Rong

doi:10.1186/s12885-022-09934-1

Cited by 2 publications

(1 citation statement)

References 32 publications

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“…Utilizing multiple independent datasets from diverse platforms for model construction and validation enhances the model’s generalization capability, leading to more compelling conclusions. This strategy is currently widely employed in the analysis of various diseases ( Li J. et al, 2022 ; Guan et al, 2022 ). We conducted differential gene expression analysis on patient data from the training set to identify DEGs.…”

Section: Discussionmentioning

confidence: 99%

Integrated transcriptomic and immunological profiling reveals new diagnostic and prognostic models for cutaneous melanoma

Li,

Wu,

Lin

et al. 2024

Front. Pharmacol.

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The mortality rate associated with cutaneous melanoma (SKCM) remains alarmingly high, highlighting the urgent need for a deeper understanding of its molecular underpinnings. In our study, we leveraged bulk transcriptome sequencing data from the SKCM cohort available in public databases such as TCGA and GEO. We utilized distinct datasets for training and validation purposes and also incorporated mutation and clinical data from TCGA, along with single-cell sequencing data from GEO. Through dimensionality reduction, we annotated cell subtypes within the single-cell data and analyzed the expression of tumor-related pathways across these subtypes. We identified differentially expressed genes (DEGs) in the training set, which were further refined using the Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithm, employing tenfold cross-validation. This enabled the construction of a prognostic model, whose diagnostic efficacy we subsequently validated. We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on the DEGs, and performed immunological profiling on two risk groups to elucidate the relationship between model genes and the immune responses relevant to SKCM diagnosis, treatment, and prognosis. We also knocked down the GMR6 expression level in the melanoma cells and verified its effect on cancer through multiple experiments. The results indicate that the GMR6 gene plays a role in promoting the proliferation, invasion, and migration of cancer cells in human melanoma. Our findings offer novel insights and a theoretical framework that could enhance prognosis, treatment, and drug development strategies for SKCM, potentially leading to more precise therapeutic interventions.

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Section: Discussionmentioning

confidence: 99%

Integrated transcriptomic and immunological profiling reveals new diagnostic and prognostic models for cutaneous melanoma

Li,

Wu,

Lin

et al. 2024

Front. Pharmacol.

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Predicting gene signature in breast cancer patients with multiple machine learning models

Zhu,

2024

Discov Onc

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Aims The aim of this study was to predict gene signatures in breast cancer patients using multiple machine learning models. Methods In this study, we first collated and merged the datasets GSE54002 and GSE22820, obtaining a gene expression matrix comprising 16,820 genes (including 593 breast cancer (BC) samples and 26 normal control (NC) samples). Subsequently, we performed enrichment analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO). Results We identified 177 differentially expressed genes (DEGs), including 40 up-regulated and 137 down-regulated genes, through differential expression analysis. The GO enrichment results indicated that these genes are primarily involved in extracellular matrix organization, positive regulation of nervous system development, collagen-containing extracellular matrix, heparin binding, glycosaminoglycan binding, and Wnt protein binding, among others. KEGG enrichment analysis revealed that the DEGs were primarily associated with pathways such as focal adhesion, the PI3K–Akt signaling pathway, and human papillomavirus infection. DO enrichment analysis showed that the DEGs play a significant role in regulating diseases such as intestinal disorders, nephritis, and dermatitis. Further, through LASSO regression analysis and SVM-RFE algorithm analysis, we identified 9 key feature DEGs (CF-DEGs): ANGPTL7, TSHZ2, SDPR, CLCA4, PAMR1, MME, CXCL2, ADAMTS5, and KIT. Additionally, ROC curve analysis demonstrated that these CF-DEGs serve as a reliable diagnostic index. Finally, using the CIBERSORT algorithm, we analyzed the infiltration of immune cells and the associations between CF-DEGs and immune cell infiltration across all samples. Conclusions Our findings provide new insights into the molecular functions and metabolic pathways involved in breast cancer, potentially aiding in the discovery of new diagnostic and immunotherapeutic biomarkers.

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Four-gene signature predicting overall survival and immune infiltration in hepatocellular carcinoma by bioinformatics analysis with RT‒qPCR validation

Cited by 2 publications

References 32 publications

Integrated transcriptomic and immunological profiling reveals new diagnostic and prognostic models for cutaneous melanoma

Integrated transcriptomic and immunological profiling reveals new diagnostic and prognostic models for cutaneous melanoma

Predicting gene signature in breast cancer patients with multiple machine learning models

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