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Project description:Squamous cell lung carcinoma (SqCLC) is associated with high mortality and limited treatment options. Identification of therapeutic targets and prognostic biomarkers is still lacking. This research aims to analyze the transcriptomic profile of SqCLC samples and identify the key genes associated with tumorigenesis, overall survival (OS), and a profile of the tumor-infiltrating immune cells. Differential gene expression analysis, pathway enrichment analysis, and Gene Ontology analysis on RNA-seq data obtained from FFPE tumor samples (N = 23) and healthy tissues (N = 3) were performed (experimental cohort). Validation of the results was conducted on publicly available gene expression data using TCGA LUSC (N = 225) and GTEx healthy donors' cohorts (N = 288). We identified 1133 upregulated and 644 downregulated genes, common for both cohorts. The most prominent upregulated genes were involved in cell cycle and proliferation regulation pathways (MAGEA9B, MAGED4, KRT, MMT11/13), while downregulated genes predominately belonged to immune-related pathways (DEFA1B, DEFA1, DEFA3). Results of the survival analysis, conducted on the validation cohort and commonly deregulated genes, indicated that overexpression of HOXC4 (p < 0.001), LLGL1 (p = 0.0015), and SLC4A3 (p = 0.0034) is associated with worse OS in early-stage SqCLC patients. In contrast, overexpression of GSTZ1 (p = 0.0029) and LILRA5 (p = 0.0086) was protective, i.e., associated with better OS. By applying a single-sample gene-set enrichment analysis (ssGSEA), we identified four distinct immune subtypes. Immune cell distribution suggests that the memory T cells (central and effector) and follicular helper T cells could serve as important stratification parameters.
| S-EPMC10887138 | biostudies-literature
Project description:Squamous cell lung cancer (LUSC) is a frequently diagnosed histological subtype of lung malignancy associated with high mortality due to limited number of treatment options. Identification of potential targets suitable for drug development using high-throughput methods is still lacking. Therefore, the purpose of this research is to analyze expression profiles of mRNA and miRNA in LUSC, aiming to identify the key molecules associated with tumorigenesis and prognosis (overall survival; OS). We performed differential gene expression analysis, pathway enrichment analysis and gene ontology analysis on RNA-seq data obtained from 23 FFPE tumor samples and 3 FFPE healthy individuals. TCGA LUSC and GTEx healthy donors’ cohorts were used for validation. We identified 1133 up-regulated and 644 down-regulated genes, common for both cohorts. The most significant up-regulated genes were involved in cell-cycle regulation pathways, while down-regulated genes predominately belonged to immune-related pathways. Survival analysis performed on selected genes, commonly dysregulated in both cohorts and performed only on non-metastatic patients, identified novel prognostic biomarkers associated with OS in early-stage LUSC patients (HOXC4-worse OS, p=0.0001; LILRA5-better OS, p=0.0086). In total, 50 differentially expressed genes are correlated with patients’ OS. Aiming to get better insight into the profile of the tumor microenvironment, we estimated levels of immune-cells infiltration in LUSC and identified 4 subtypes based on immune-cell infiltration levels. Protein-protein interaction (PPI) network was built based on differentially expressed genes, and PPI modules and hub-genes were identified. Weighted gene co-expression network analysis was conducted to identify significant modules and hub-genes in correlation to clinical traits. Differential analysis on miRNA-Seq data, obtained from 16 tumor and 4 healthy tissue samples, was conducted as well. 75 up-regulated and 47 down-regulated miRNAs were identified. Further, targets of differentially expressed miRNA were identified, following gene ontology analysis and miRNA-mRNA networks construction.
2024-02-28 | GSE229509 | GEO
Project description:Transcriptomic Profiling for Prognostic Biomarkers in Early-stage Squamous Cell Lung Cancer (SqCLC)
| PRJNA957436 | ENA
Project description:Transcriptomic Profiling for Prognostic Biomarkers in Early-stage Squamous Cell Lung Cancer (SqCLC)
| PRJNA954816 | ENA
Project description:Squamous cell lung cancer (LUSC) is a frequently diagnosed histological subtype of lung malignancy associated with high mortality due to limited number of treatment options. Identification of potential targets suitable for drug development using high-throughput methods is still lacking. Therefore, the purpose of this research is to analyze expression profiles of mRNA and miRNA in LUSC, aiming to identify the key molecules associated with tumorigenesis and prognosis (overall survival; OS). We performed differential gene expression analysis, pathway enrichment analysis and gene ontology analysis on RNA-seq data obtained from 23 FFPE tumor samples and 3 FFPE healthy individuals. TCGA LUSC and GTEx healthy donors’ cohorts were used for validation. We identified 1133 up-regulated and 644 down-regulated genes, common for both cohorts. The most significant up-regulated genes were involved in cell-cycle regulation pathways, while down-regulated genes predominately belonged to immune-related pathways. Survival analysis performed on selected genes, commonly dysregulated in both cohorts and performed only on non-metastatic patients, identified novel prognostic biomarkers associated with OS in early-stage LUSC patients (HOXC4-worse OS, p=0.0001; LILRA5-better OS, p=0.0086). In total, 50 differentially expressed genes are correlated with patients’ OS. Aiming to get better insight into the profile of the tumor microenvironment, we estimated levels of immune-cells infiltration in LUSC and identified 4 subtypes based on immune-cell infiltration levels. Protein-protein interaction (PPI) network was built based on differentially expressed genes, and PPI modules and hub-genes were identified. Weighted gene co-expression network analysis was conducted to identify significant modules and hub-genes in correlation to clinical traits. Differential analysis on miRNA-Seq data, obtained from 16 tumor and 4 healthy tissue samples, was conducted as well. 75 up-regulated and 47 down-regulated miRNAs were identified. Further, targets of differentially expressed miRNA were identified, following gene ontology analysis and miRNA-mRNA networks construction.
2024-03-01 | GSE230087 | GEO
Project description:We are investigating the molecular development of squamous cell lung carcinoma based upon analysis of global gene expression profiles representing progressive stages of cancer development, consisting of precancerous, carcinoma-in-situ, and invasive cancer. Keywords: global gene expression analysis, lung cancer development In this study, we have generated 13 SAGE libraries, consisting of five carcinoma-in-situ libraries, six early invasive squamous cell carcinoma libraries, and two reference libraries representing precancerous squamous development.
| S-ECPF-GEOD-7898 | biostudies-other
Project description:This SuperSeries is composed of the SubSeries listed below.
2020-08-28 | GSE157011 | GEO
Project description:Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival. Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs. Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians' capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease's mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.
| S-EPMC7947407 | biostudies-literature
Project description:IntroductionSurgical resection is curative for some patients with early lung squamous cell carcinoma. Staging and clinical factors do not adequately predict recurrence risk. We sought to validate the discriminative performance of proposed prognostic gene expression signatures at a level of rigor sufficient to support clinical use.MethodsThe two-stage validation used independent core laboratories, objective quality control standards, locked test parameters, and large multi-institutional specimen and data sets. The first stage validation confirmed a signature's ability to stratify patient survival. The second-stage validation determined which signature(s) optimally improved risk discrimination when added to baseline clinical predictors. Participants were prospectively enrolled in institutional (cohort I) or cooperative group (cohort II) biospecimen and data collection protocols. All cases underwent a central review of clinical, pathologic, and biospecimen parameters using objective criteria to determine final inclusion (cohort I: n = 249; cohort II: n = 234). Primary selection required that a signature significantly predict a 3-year survival after surgical resection in cohort I. Signatures meeting this criterion were further tested in cohort II, comparing risk prediction using baseline risk factors alone versus in combination with the signature.ResultsMale sex, advanced age, and higher stage were associated with shorter survival in cohort I and established a baseline clinical model. Of the three signatures validated in cohort I, one signature was validated in cohort II and statistically significantly enhanced the prognosis relative to the baseline model (C-index difference 0.122; p < 0.05).ConclusionsThese results represent the first rigorous validation of a test appropriate to direct adjuvant treatment or clinical trials for patients with lung squamous cell carcinoma.
| S-EPMC8278226 | biostudies-literature
Project description:Identification of prognostic biomarkers in early stage squamous cell lung cancer (LUSC) by transcriptomic and miRNA profiling [sRNA-Seq]
| PRJNA957440 | ENA