ArticleFrontiers in endocrinology2021
Cancer Stemness-Based Prognostic Immune-Related Gene Signatures in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma.
Article in Frontiers in endocrinology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed, 32 citations in OpenAlex.
- Optimizing Surgery Strategies in Stage IB Lung Squamous Cell Carcinoma: Insights from Interpretable Machine Learning.Thoracic cancer · 2026Article
- Construction and validation of risk prediction model for glioblastoma associated with cancer stem cells and disulfidptosis.Translational cancer research · 2026Article
- MiR-21-5p regulates biological malignancy in esophageal squamous cell carcinoma via targeting CNTFR.BMC gastroenterology · 2026Article
- Development and validation of a hypoxia-immune-based microenvironment gene signature for predicting survival in non-small cell lung cancer.Discover oncology · 2025Article
- Construction of a prognostic model for lung adenocarcinoma based on disulfidptosis-related lncRNAs.Translational cancer research · 2025Article
- Integrative machine learning model for subtype identification and prognostic prediction in lung squamous cell carcinoma.Discover oncology · 2025Article
- Application of artificial intelligence-based stemness index in cancer.Frontiers in oncology · 2025Review
- Construction of a Prognostic Model based on CSC-related Genes in Patients with Colorectal Cancer.Journal of Cancer · 2025Article
- Analysis and identification of mRNAsi‑related expression signatures via RNA sequencing in lung cancer.Oncology letters · 2024Article
- Histological transformation in lung adenocarcinoma: Insights of mechanisms and therapeutic windows.Journal of translational internal medicine · 2024Article
- Identification of a biomarker to predict doxorubicin/cisplatin chemotherapy efficacy in osteosarcoma patients using primary, recurrent and metastatic specimens.Translational oncology · 2024Article
- CTSG may inhibit disease progression in HIV-related lung cancer patients by affecting immunosuppression.Infectious agents and cancer · 2024Article
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- Exercise potentially prevents colorectal cancer liver metastases by suppressing tumor epithelial cell stemnessHeliyon · 2024Article
- The endoplasmic reticulum stress-related genes and molecular typing predicts prognosis and reveals characterization of tumor immune microenvironment in lung squamous cell carcinoma.Discover oncology · 2024Article
- Article
- Development and validation of a novel stemness-related prognostic model for neuroblastoma using integrated machine learning and bioinformatics analyses.Translational pediatrics · 2024Article
- Article
- Identification of cancer stemness and M2 macrophage-associated biomarkers in lung adenocarcinoma.Heliyon · 2023Article
- A Comprehensive Pan-Cancer Analysis of the Regulation and Prognostic Effect of Coat Complex Subunit Zeta 1.Genes · 2023Article
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4 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Cancer stem cells (CSCs) refer to cells with self-renewal capability in tumors. CSCs play important roles in proliferation, metastasis, recurrence, and tumor heterogeneity. This study aimed to identify immune-related gene-prognostic models based on stemness index (mRNAsi) in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), respectively. Methods: X-tile software was used to determine the best cutoff value of survival data in LUAD and LUSC based on mRNAsi. Tumor purity and the scores of infiltrating stromal and immune cells in lung cancer tissues were predicted with ESTIMATE R package. Differentially expressed immune-related genes (DEIRGs) between higher- and lower-mRNAsi subtypes were used to construct prognostic models. Results: mRNAsi was negatively associated with StromalScore, ImmuneScore, and ESTIMATEScore, and was positively associated with tumor purity. LUAD and LUSC samples were divided into higher- and lower-mRNAsi groups with X-title software. The distribution of immune cells was significantly different between higher- and lower-mRNAsi groups in LUAD and LUSC. DEIRGs between those two groups in LUAD and LUSC were enriched in multiple cancer- or immune-related pathways. The network between transcriptional factors (TFs) and DEIRGs revealed potential mechanisms of DEIRGs in LUAD and LUSC. The eight-gene-signature prognostic model (ANGPTL5, CD1B, CD1E, CNTFR, CTSG, EDN3, IL12B, and IL2)-based high- and low-risk groups were significantly related to overall survival (OS), tumor microenvironment (TME) immune cells, and clinical characteristics in LUAD. The five-gene-signature prognostic model (CCL1, KLRC3, KLRC4, CCL23, and KLRC1)-based high- and low-risk groups were significantly related to OS, TME immune cells, and clinical characteristics in LUSC. These two prognostic models were tested as good ones with principal components analysis (PCA) and univariate and multivariate analyses. Tumor T stage, pathological stage, or metastasis status were significantly correlated with DEIRGs contained in prognostic models of LUAD and LUSC. Conclusion: Cancer stemness was not only an important biological process in cancer progression but also might affect TME immune cell infiltration in LUAD and LUSC. The mRNAsi-related immune genes could be potential biomarkers of LUAD and LUSC. Evaluation of integrative characterization of multiple immune-related genes and pathways could help to understand the association between cancer stemness and tumor microenvironment in lung cancer.
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