Emerging evidence suggests that the prognosis of patients with lung adenocarcinoma can be determined from germline variants and transcript levels in non-tumoral lung tissue. Gene expression data from non-involved lung tissue of 483 lung adenocarcinoma patients were tested for correlation with overall survival using multivariable Cox proportional hazard and multivariate machine learning models. For genes whose transcript levels associated with survival, we used genotype data from 414 patients to identify germline variants acting as cis-expression quantitative trait loci (eQTLs). Associations of eQTL variant genotypes with gene expression and survival were tested. Levels of four transcripts were inversely associated with survival by Cox analysis (CLCF1, hazard ratio [HR] = 1.53; CNTNAP1, HR = 2.17; and DUSP14, HR = 1.78; and MT1F: HR = 1.40). Machine learning analysis identified a signature of transcripts associated with lung adenocarcinoma outcome that was largely overlapping with the transcripts identified by Cox analysis, including the three most significant genes (CLCF1, CNTNAP1 and DUSP14). Pathway analysis indicated that the signature is enriched for extracellular matrix components. We identified 32 cis-eQTLs for CNTNAP1, including six with an inverse correlation and 26 with a direct correlation between the number of minor alleles and transcript levels. Of these, all but one were prognostic: the six with an inverse correlation were associated with better prognosis (HR <1) while the others were associated with worse prognosis. Our findings provide supportive evidence that genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients' non-involved lung tissue.

Genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients’ non‐involved lung tissue

Marco Chierici;Giuseppe Jurman;
2022

Abstract

Emerging evidence suggests that the prognosis of patients with lung adenocarcinoma can be determined from germline variants and transcript levels in non-tumoral lung tissue. Gene expression data from non-involved lung tissue of 483 lung adenocarcinoma patients were tested for correlation with overall survival using multivariable Cox proportional hazard and multivariate machine learning models. For genes whose transcript levels associated with survival, we used genotype data from 414 patients to identify germline variants acting as cis-expression quantitative trait loci (eQTLs). Associations of eQTL variant genotypes with gene expression and survival were tested. Levels of four transcripts were inversely associated with survival by Cox analysis (CLCF1, hazard ratio [HR] = 1.53; CNTNAP1, HR = 2.17; and DUSP14, HR = 1.78; and MT1F: HR = 1.40). Machine learning analysis identified a signature of transcripts associated with lung adenocarcinoma outcome that was largely overlapping with the transcripts identified by Cox analysis, including the three most significant genes (CLCF1, CNTNAP1 and DUSP14). Pathway analysis indicated that the signature is enriched for extracellular matrix components. We identified 32 cis-eQTLs for CNTNAP1, including six with an inverse correlation and 26 with a direct correlation between the number of minor alleles and transcript levels. Of these, all but one were prognostic: the six with an inverse correlation were associated with better prognosis (HR <1) while the others were associated with worse prognosis. Our findings provide supportive evidence that genetic predisposition to lung adenocarcinoma outcome is a feature already present in patients' non-involved lung tissue.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/333967
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