Transcription profiling of human lung cancer patients with sqamous cell or adenocarcinoma at different stages of disease to identify gene signatures to predict prognosis
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Raponi M
PROVIDER: S-ECPF-GEOD-4573 | biostudies-other | 2006 Aug
REPOSITORIES: biostudies-other
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Project description:Gene signatures were derived to separate high risk patients from low risk ones. Experiment Overall Design: 130 samples from 129 patients
2007-11-01 | E-GEOD-4573 | biostudies-arrayexpress
Project description:Malignant pleural mesothelioma (MPM) is a rare but lethal pleural cancer with high intratumor heterogeneity (ITH). A recent study in lung adenocarcinoma has developed a clonal gene signature (ORACLE) from multiregional transcriptomic data and demonstrated high prognostic values and reproducibility. However, such a strategy has not been tested in other types of cancer with high ITH. We aimed to identify biomarkers from multi-regional data to prognostically stratify MPM patients. We generated a multiregional RNA-seq dataset for 78 tumor samples obtained from 26 MPM patients, each with one sample collected from a superior, lateral, and inferior region of the tumor. By integrating this dataset with the Cancer Genome Atlas MPM RNA-seq data, we selected 29 prognostic genes displaying high variability across different tumors but low ITH, which named PRACME (Prognostic Risk Associated Clonal Mesothelioma Expression). We evaluated PRACME in two independent MPM datasets and demonstrated its prognostic values. Patients with high signature scores are associated with poor prognosis after adjusting established clinical factors. Interestingly, the PRACME and the ORACLE signatures defined respectively from MPM and lung adenocarcinoma cross-predict prognosis between the two cancer types. Further investigation indicated that the cross-prediction ability might be explained by the high similarity between the two cancer types in their genomic regions with copy number variation, which host many clonal genes. Overall, our clonal signature PRACME provided prognostic stratification in MPM and this study emphasized the importance of multi-regional transcriptomic data for prognostic stratification based on clonal genes.
| S-EPMC10891127 | biostudies-literature
Project description:We aimed to find some specific pathways that can be used to predict the stage of lung adenocarcinoma.RNA-Seq expression profile data and clinical data of lung adenocarcinoma (stage I [37], stage II 161], stage III [75], and stage IV [45]) were obtained from the TCGA dataset. The differentially expressed genes were merged, correlation coefficient matrix between genes was constructed with correlation analysis, and unsupervised clustering was carried out with hierarchical clustering method. The specific coexpression network in every stage was constructed with cytoscape software. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed with KOBAS database and Fisher exact test. Euclidean distance algorithm was used to calculate total deviation score. The diagnostic model was constructed with SVM algorithm.Eighteen specific genes were obtained by getting intersection of 4 group differentially expressed genes. Ten significantly enriched pathways were obtained. In the distribution map of 10 pathways score in different groups, degrees that sample groups deviated from the normal level were as follows: stage I < stage II < stage III < stage IV. The pathway score of 4 stages exhibited linear change in some pathways, and the score of 1 or 2 stages were significantly different from the rest stages in some pathways. There was significant difference between dead and alive for these pathways except thyroid hormone signaling pathway.Those 10 pathways are associated with the development of lung adenocarcinoma and may be able to predict different stages of it. Furthermore, these pathways except thyroid hormone signaling pathway may be able to predict the prognosis.
| S-EPMC5413258 | biostudies-literature
Project description:Background: Lung adenocarcinoma (LUAD) possesses a poor prognosis with a low 5-year survival rate even for stages I-III resected patients, it is thus critical to understand the determinants that affect the survival and discover new potentially prognostic biomarkers. Somatic copy number alterations (CNAs) are major source of genomic variations driving tumor evolution, CNAs screening may identify prognostic biomarkers. Methods: Oncoscan MIP array was used to analyze the patterns of CNAs on formalin fixed paraffin embedded(FFPE) tumor specimens from 163 consecutive stage I-III resected LUAD patients, 145 out of which received platinum-based adjuvant chemotherapy. Results: Of the 163 patients, 91(55.8%) were recurred within 3 years after surgery. The most common aberrations in our cohort were 1q, 5p, 5q, 7p, 8q, 14p, 16p, 17q, 20q for copy number gains and 8p, 9p, 13p, 16q, 18q for losses. The GISTIC2 analysis produced 45 amplification peaks and 40 deletion peaks, involving some reported genes TERT, EGFR, MYC, CCND1, CDK4, MDM2, ERBB2, NKX2-1, CCNE1, and CDKN2A, most of which were consistent with TCGA database. The amplifications of 12p12.1 (CMAS, GOLT1B, YS2, LDHB, RECQL, ETNK1, IAPP, PYROXD1, KRAS) and KDM5A were correlated with worse prognosis in our cohort, this result was further validated in 506 LUAD patients from TCGA. In addition, 163 patients could be well-classified into five groups, and the clinical outcomes were significantly different based on threshold copy number at reoccurring alteration peaks. Among the 145 patients who received adjuvant chemotherapy, focal amplification of ERBB2 and deletion of 4q34.3 were found to be specific in relapsed patients, this result was validated in an independent group of Imielinski et al., demonstrating these two CNAs may contribute to resected LUAD recurrence after adjuvant chemotherapy. Conclusion: This study suggests that CNAs profiling may be a potential prognostic classifier in resected LAUD patients. Amplifications of 12p12.1 and KDM5A might be prognostic biomarkers for LUAD, and amplification of ERBB2 and deletion of 4q34.3 predicted early relapse after adjuvant chemotherapy. These novel findings may provide implication for better implementation of precision therapy for lung cancer patients.
| S-EPMC6691340 | biostudies-literature
Project description:Signatures of Oncogenic Pathway Deregulation in Human Cancers. The ability to define cancer subtypes, recurrence of disease, and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Such data is also of substantial importance to the analysis of cellular signaling pathways central to the oncogenic process. With this focus, we have developed a series of gene expression signatures that reliably reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumors, and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumor sub-types. Clustering tumors based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Furthermore, predictions of pathway deregulation in cancer cell lines are shown to coincide with sensitivity to therapeutic agents that target components of the pathway, underscoring the potential for such pathway prediction to guide the use of targeted therapeutics. Experiment Overall Design: RNA was extracted from frozen tissue of primary lung tumors for gene array analysis.
| S-ECPF-GEOD-3141 | biostudies-other
Project description:BackgroundPolyamines metabolism is closely related to tumor development and progression, as well as tumor microenvironment (TME). In this study, we focused on exploring whether polyamines metabolism-associated genes would provide prognosis and immunotherapy response prediction in lung adenocarcinoma (LUAD).MethodsThe expression profile data of polyamines metabolism-associated genes were acquired from the Cancer Genome Atlas (TCGA) database. Utilizing the least absolute shrinkage and selection operator (LASSO) algorithm, we created a risk score model according to polyamines metabolism-associated gene signatures. Meanwhile, an independent cohort (GSE72094) was employed to validate this model. Through the univariate and multivariate Cox regression analyses, the independent prognostic factors were identified. Subsequently, quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect their expression in LUAD cells. By consensus clustering analysis, polyamines metabolism-associated subgroups were determined in LUAD patients, with differential gene expression, prognosis, and immune characteristics analyses explored.ResultsA total of 59 polyamines metabolism genes were collected for this study, of which 14 genes were identified for the construction of risk score model using LASSO method. High- and low- risk groups of LUAD patients in TCGA cohort were distinguished via this model, and high-risk group presented dismal clinical outcomes. The same prognostic prediction of this model had been also validated in GSE72094 cohort. Meanwhile, three independent prognostic factors (PSMC6, SMOX, SMS) were determined for constructing the nomogram, and they were all upregulated in LUAD cells. In addition, two distinct subgroups (C1 and C2) were identified in LUAD patients. Comparing the two subgroups, 291 differentially expressed genes (DEGs) were acquired, mainly enriching in organelle fission, nuclear division, and cell cycle. Comparing to C1 subgroup, the patients in C2 subgroup had favorable clinical outcomes, increased immune cells infiltration, and effective immunotherapy response.ConclusionThis study identified polyamines metabolism-associated gene signatures for predicting the patients' survival, and they were also linked to immune cells infiltration and immunotherapy response in LUAD patients.
| S-EPMC10272553 | biostudies-literature
Project description:Cervical cancer is the second most common cancer among women worldwide, with developing countries accounting for >80% of the disease burden. Although in the West, active screening has been instrumental in reducing the incidence of cervical cancer, disease management is hampered due to lack of biomarkers for disease progression and defined therapeutic targets. Here we carried out gene expression profiling of 29 cervical cancer tissues from Indian women, spanning International Federation of Gynaecology and Obstetrics (FIGO) stages of the disease from early lesion (IA and IIA) to progressive stages (IIB and IIIA-B), and identified distinct gene expression signatures. Overall, metabolic pathways, pathways in cancer and signaling pathways were found to be significantly upregulated, while focal adhesion, cytokine-cytokine receptor interaction and WNT signaling were downregulated. Additionally, we identified candidate biomarkers of disease progression such as SPP1, proliferating cell nuclear antigen (PCNA), STK17A, and DUSP1 among others that were validated by quantitative real-time polymerase chain reaction (qRT-PCR) in the samples used for microarray studies as well in an independent set of 34 additional samples. Integrative analysis of our results with other cervical cancer profiling studies could facilitate the development of multiplex diagnostic markers of cervical cancer progression.
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Project description:BackgroundLung adenocarcinoma (LUAD) accounts for 50% of lung cancers, with high mortality and poor prognosis. Long non-coding RNA (lncRNA) plays a vital role in the progression of tumors. Cuproptosis is a newly discovered form of cell death that is highly investigated. Therefore, in the present study, we aimed to investigate the role of cuproptosis-related lncRNA signature in clinical prognosis prediction and immunotherapy and the relationship with drug sensitivity.Material and methodsGenomic and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and cuproptosis-related genes were obtained from cuproptosis-related studies. The prognostic signature was constructed by co-expression analysis and Cox regression analysis. Patients were divided into high and low risk groups, and then, a further series of model validations were carried out to assess the prognostic value of the signature. Subsequently, lncRNAs were analyzed for gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes Enrichment (KEGG), immune-related functions, and tumor mutation burden (TMB). Finally, we used tumor immune dysfunction and exclusion (TIDE) algorithms on immune escape and immunotherapy of cuproptosis-related lncRNAs, thereby identifying its sensitivity toward potential drugs for LUAD.ResultsA total of 16 cuproptosis-related lncRNAs were obtained, and a prognostic signature was developed. We found that high-risk patients had worse overall survival (OS) and progression-free survival (PFS) and higher mortality. Independent prognostic analyses, ROC, C-index, and nomogram showed that the cuproptosis-related lncRNAs can accurately predict the prognosis of patients. The nomogram and heatmap showed a distinct distribution of the high- and low-risk cuproptosis-related lncRNAs. Enrichment analysis showed that the biological functions of lncRNAs are associated with tumor development. We also found that immune-related functions, such as antiviral activity, were suppressed in high-risk patients who had mutations in oncogenes. OS was poorer in patients with high TMB. TIDE algorithms showed that high-risk patients have a greater potential for immune escape and less effective immunotherapy.ConclusionTo conclude, the 16 cuproptosis-related lncRNAs can accurately predict the prognosis of patients with LUAD and may provide new insights into clinical applications and immunotherapy.
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Project description: Not available
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Project description:Identify Wnt3A responsive signature in lung adenocarcinoma cells Experiment Overall Design: 4 different cell lines +/- Wnt3A; replicate samples
| S-ECPF-GEOD-14107 | biostudies-other