Transcriptomic Profiling for Prognostic Biomarkers in Early-stage Squamous Cell Lung Cancer (SqCLC)
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA954816 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR24145400_1.fastq.gz | Fastqsanger.gz | |||
| SRR24145400_2.fastq.gz | Fastqsanger.gz | |||
| SRR24145401_1.fastq.gz | Fastqsanger.gz | |||
| SRR24145401_2.fastq.gz | Fastqsanger.gz | |||
| SRR24145402_1.fastq.gz | Fastqsanger.gz |
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Project description:Transcriptomic Profiling for Prognostic Biomarkers in Early-stage Squamous Cell Lung Cancer (SqCLC)
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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:Identification of prognostic biomarkers in early stage squamous cell lung cancer (LUSC) by transcriptomic and miRNA profiling [sRNA-Seq]
| PRJNA957440 | 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:Multi-institutional prospective validation of prognostic mRNA signatures in early stage squamous lung cancer (Alliance)
| PRJNA659802 | ENA
Project description:Purpose: The primary objective of the current study was to validate biomarkers to identify the 10% to 27% of patients with stage I and 35% of patients with stage IIA squamous cell carcinoma of lung (SC) who are likely to recur following surgical resection, so that these patients may be offered enrollment in clinical trials evaluating directed ACT. A secondary objective was to identify patients with stage IIB SC who are unlikely to develop recurrences and might thereby be spared the potential significant toxicity and expense of ACT. Methods: Two-stage validation used independent core laboratories, objective quality control standards, locked test parameters, and large multi-institutional specimen/data sets. First stage validation confirmed a signature’s ability to stratify patient survival. Second stage validation determined which signature(s) optimally improved risk discrimination when added to baseline clinical predictors. Participants were prospectively enrolled on institutional (Cohort I) or cooperative group (Cohort II) biospecimen/data collection protocols. All cases underwent 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 3-years survival after surgery 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. Results: Male sex, advanced age, and higher stage were associated with shorter survival in Cohort I and established a baseline clinical model. Of three signatures validated in Cohort I, one signature was validated in Cohort II and statistically significantly enhanced prognosis relative to the baseline model (C-index difference 0.122; p<0.05). Conclusions: These results represent the first rigorous validation of a test appropriate to direct adjuvant treatment or clinical trials for patients with lung squamous cell carcinoma.
2020-08-28 | GSE157010 | GEO
Project description:Purpose: The primary objective of the current study was to validate biomarkers to identify the 10% to 27% of patients with stage I and 35% of patients with stage IIA squamous cell carcinoma of lung (SC) who are likely to recur following surgical resection, so that these patients may be offered enrollment in clinical trials evaluating directed ACT. A secondary objective was to identify patients with stage IIB SC who are unlikely to develop recurrences and might thereby be spared the potential significant toxicity and expense of ACT. Methods: Two-stage validation used independent core laboratories, objective quality control standards, locked test parameters, and large multi-institutional specimen/data sets. First stage validation confirmed a signature’s ability to stratify patient survival. Second stage validation determined which signature(s) optimally improved risk discrimination when added to baseline clinical predictors. Participants were prospectively enrolled on institutional (Cohort I) or cooperative group (Cohort II) biospecimen/data collection protocols. All cases underwent 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 3-years survival after surgery 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. Results: Male sex, advanced age, and higher stage were associated with shorter survival in Cohort I and established a baseline clinical model. Of three signatures validated in Cohort I, one signature was validated in Cohort II and statistically significantly enhanced prognosis relative to the baseline model (C-index difference 0.122; p<0.05). Conclusions: These results represent the first rigorous validation of a test appropriate to direct adjuvant treatment or clinical trials for patients with lung squamous cell carcinoma.
2020-08-28 | GSE157009 | GEO
Project description:Background: Patients with early stage non-small cell lung carcinoma (NSCLC) may benefit from treatments based on more accurate prognosis. A 15-gene prognostic classifier for NSCLC was identified from mRNA expression profiling of tumor samples from the NCIC CTG JBR.10 trial. Here, we assessed its value in an independent set of cases. Methods: Expression profiling was performed on RNA from frozen, resected tumor tissues corresponding to 181 Stage I and II NSCLC cases collected at University Health Network (UHN181). Kaplan-Meier methodology was used to estimate three year overall survival probabilities and the prognostic effect of the classifier was assessed using log-rank testing. Cox proportional hazards model evaluated the signature's effect adjusting for clinical prognostic factors. Results: Expression data of the 15-gene classifier stratified UHN181 cases into high and low-risk subgroups with significantly different overall survival (HR=1.92, 95% CI: 1.15-3.23, p=0.012). Its strength as a prognostic classifier was superior to stage alone (HR=1.52, 95% CI: 0.90-2.55, p-value=0.11). In subgroup analysis, this classifier predicted survival in 127 Stage I patients (HR=2.17, 95% CI: 1.12-4.20, p=0.018) and the smaller subgroup of 48 Stage IA patients (HR=5.61, 95% CI: 1.19-26.45, p=0.014. The signature was prognostic for both adenocarcinoma and squamous cell carcinoma cases (HR= 1.76, p-value=0.058; HR= 4.19, p-value=0.045, respectively). Conclusions: The prognostic accuracy of a 15-gene classifier was validated in an independent cohort of 181 early stage NSCLC samples including Stage IA cases and in different NSCLC histologic subtypes.
2013-12-22 | GSE50081 | GEO
Project description:Microarray was used to find out the differentially expressed in tumor sites of early-stage oral squamous cell carcinoma compared with Normal parts. Furthermore, we compared cases of early-stage oral squamous cell carcinoma with lymph node metastasis with cases without lymph node metastasis. The miRNAs obtained may not only serve as predictive biomarkers for lymph node metastasis, but may also be used further to understand disease.
2023-08-22 | GSE241289 | GEO
Project description:Background: Patients with early stage non-small cell lung carcinoma (NSCLC) may benefit from treatments based on more accurate prognosis. A 15-gene prognostic classifier for NSCLC was identified from mRNA expression profiling of tumor samples from the NCIC CTG JBR.10 trial. Here, we assessed its value in an independent set of cases. Methods: Expression profiling was performed on RNA from frozen, resected tumor tissues corresponding to 181 Stage I and II NSCLC cases collected at University Health Network (UHN181). Kaplan-Meier methodology was used to estimate three year overall survival probabilities and the prognostic effect of the classifier was assessed using log-rank testing. Cox proportional hazards model evaluated the signature's effect adjusting for clinical prognostic factors. Results: Expression data of the 15-gene classifier stratified UHN181 cases into high and low-risk subgroups with significantly different overall survival (HR=1.92, 95% CI: 1.15-3.23, p=0.012). Its strength as a prognostic classifier was superior to stage alone (HR=1.52, 95% CI: 0.90-2.55, p-value=0.11). In subgroup analysis, this classifier predicted survival in 127 Stage I patients (HR=2.17, 95% CI: 1.12-4.20, p=0.018) and the smaller subgroup of 48 Stage IA patients (HR=5.61, 95% CI: 1.19-26.45, p=0.014. The signature was prognostic for both adenocarcinoma and squamous cell carcinoma cases (HR= 1.76, p-value=0.058; HR= 4.19, p-value=0.045, respectively). Conclusions: The prognostic accuracy of a 15-gene classifier was validated in an independent cohort of 181 early stage NSCLC samples including Stage IA cases and in different NSCLC histologic subtypes. Expression profiling was performed on RNA from frozen, resected tumor tissues corresponding to 181 Stage I and II NSCLC cases collected at University Health Network (UHN181). !Series_contributor = Sandy,D,Der
2013-12-22 | E-GEOD-50081 | biostudies-arrayexpress