Integrated bioinformatics analyses identifying key transcriptomes correlated with prognosis and immune infiltrations in lung squamous cell carcinoma

Alghamdi, Rana A.; Al-Zahrani, Maryam H.

doi:10.1016/j.sjbs.2023.103596

Cited by 4 publications

(3 citation statements)

References 64 publications

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“…In conclusion, the present study identified 2 novel biomarkers. And, one of the biomarkers, PLAU was also identified in parallel studies, [ 38 , 39 ] which demonstrating the accuracy of the method used in this study. More importantly is that we constructed miRNA-mRNA- TFs networks, which is conducive to the subsequent exploration of the function of biomarkers in the development and progression of LUAD.…”

Section: Discussionsupporting

confidence: 62%

PLAU and GREM1 are prognostic biomarkers for predicting immune response in lung adenocarcinoma

Fu,

Hu,

et al. 2024

Medicine

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Lung adenocarcinoma (LUAD) is a common malignant tumor. Identification of biomarkers and understanding their potential functions will facilitate the treatment and diagnosis in LUAD patients. The yellow module (cor = 0.31, P = 2e-6) was selected as the core module based on weighted gene co-expression network analysis (WGCNA) by integrating RNA-seq data and tumor stage. Two upregulated genes (PLAU and GREM1) in yellow module were identified to be biomarkers. Kaplan–Meier curve analysis displayed that high expression levels of them had a poor overall survival (OS). And, their high expression levels revealed higher tumor stage and relapse possibility in LUAD patients, and could be a prognostic parameter. Both biomarkers showed similar immune cell expression profiles in low- and high-expression groups. Strongly positive correlation between both biomarkers and biomarkers of tumor-infiltrating lymphocytes were also clarified in TCGA-LUAD cohort. Importantly, single gene GSEA showed that transcriptional mis-regulation in cancer and microRNAs in cancer were enriched in LUAD patients. Therefore, a miRNA-mRNA-transcription factors (TFs) co-expression regulatory networks was constructed for each biomarker, various miRNAs and TFs were related to PLAU and GREM1. Among which, 6 downstream TFs were overlapped genes for both biomarkers. Notably, 2 of these TFs (FOXF1 and TFAP2A) exhibited significantly abnormal expression levels. Among which, FOXF1 was downregulated and TFAP2A was upregulated in TCGA-LUAD cohort. Both TFs showed a significantly positive correlation with the expression level of PLAU. In conclusion, we identified 2 biomarkers related to immune response and achieved a good accuracy in predicting OS in patients with LUAD.

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Section: Discussionsupporting

confidence: 62%

PLAU and GREM1 are prognostic biomarkers for predicting immune response in lung adenocarcinoma

Fu,

Hu,

et al. 2024

Medicine

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“…The challenges posed by current therapeutic approaches, the high mortality rates, and the inherent molecular heterogeneity of LUSC underscore the need to employ bioinformatics and machine learning methodologies to explore the molecular data and guide decision-making for LUSC treatment [9]. These methodologies can also be used to obtain molecular signatures that characterize patients with the disease, and studies indicate that the aberrantly expressed gene signatures are critically associated with the pathogenesis of LUSC [11]. Most studies looking for gene signatures in LUSC used regression, survival analysis, and carried out more specific searches in pathways associated with immune processes [12], cell death pathways [13] using microarray data [11], and a mixture of RNAseq and microarray data [14].…”

Section: Introductionmentioning

confidence: 99%

“…These methodologies can also be used to obtain molecular signatures that characterize patients with the disease, and studies indicate that the aberrantly expressed gene signatures are critically associated with the pathogenesis of LUSC [11]. Most studies looking for gene signatures in LUSC used regression, survival analysis, and carried out more specific searches in pathways associated with immune processes [12], cell death pathways [13] using microarray data [11], and a mixture of RNAseq and microarray data [14]. Recently, studies based on Machine Learning (ML) techniques, i.e., algorithms that use data for learning purposes [15], [16], have been developed to improve the understanding of many diseases.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Data Analysis Using Bioinformatics and Random Forest to Predict Prognosis of Patients With Squamous Cell Lung Cancer

Lima,

Terrematte,

Stransky

et al. 2024

IEEE Access

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Lung cancer is the leading cause of cancer death worldwide, regardless of gender. Among the types of lung cancer, Lung Squamous Cell Carcinoma (LUSC) is the second most common type, characterized by a diagnosis in advanced stages, a poor prognosis, and a high association with smoking. Due to the severity of lung cancer, it is essential to understand its molecular mechanisms. In this context, this study uses transcriptomic and clinical data to implement bioinformatics pipelines, and machine learning, through random forest models to predict patients' overall survival and obtain a gene signature of LUSC for tumor progression. We analyzed clinical and molecular data from the project LUSC-TCGA, and we performed differential expression analyses (DEA) comparing normal tissues against tumor tissues. Based on DEA-selected genes, the patients were divided into three clusters, followed by a feature selection and classification. Finally, it was possible to obtain classifications results close to 70% of accuracy for the three clusters. Finally, we also performed a functional enrichment analysis. The clustering analysis revealed in cluster 2, enriched genes such as CDT1, CENPI, and NLGN1, associated with the molecular EMT (epithelialto-mesenchymal transition) process. Our approach facilitated the identification of genes that are biologically relevant to the LUSC development process, holding significant genes for predicting patient survival, such as gene ALDH3B1, C7, FAM83A, FOSB, GCGR, BMP7, PPP1R27 and AQP1, and putative therapeutic targets for LUSC such as gene FAM83A, CAV1, TNS4, EIF4G1, TFAP2A, GCGR and PPP1R27.

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Synergistic interaction between Chlorella vulgaris extract and Origanum elongatum essential oil against methicillin-resistant Staphylococcus aureus

Ouzakar,

Senhaji,

Harsal

et al. 2024

Int Microbiol

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Integrated bioinformatics analyses identifying key transcriptomes correlated with prognosis and immune infiltrations in lung squamous cell carcinoma

Cited by 4 publications

References 64 publications

PLAU and GREM1 are prognostic biomarkers for predicting immune response in lung adenocarcinoma

PLAU and GREM1 are prognostic biomarkers for predicting immune response in lung adenocarcinoma

An Integrated Data Analysis Using Bioinformatics and Random Forest to Predict Prognosis of Patients With Squamous Cell Lung Cancer

Synergistic interaction between Chlorella vulgaris extract and Origanum elongatum essential oil against methicillin-resistant Staphylococcus aureus

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