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Emerging evidence suggests that the prognosis of patients with lung adenocarcinoma can be determined from germline variants and transcript levels in nontumoral lung tissue. Gene expression data from noninvolved 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 are 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; 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 ECM components. We identified 32 cis-eQTLs for CNTNAP1, including 6 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' noninvolved lung tissue., (© 2022 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.)

Transcripts originating from the transcriptional read through of two adjacent, similarly oriented genes have been identified in normal and neoplastic tissues, but their functional role and the mechanisms that regulate their expression are mostly unknown. Here, we investigated whether the expression of read-through transcripts previously identified in the non-involved lung tissue of lung adenocarcinoma patients was genetically regulated. Data on genome-wide single nucleotide variant genotypes and expression levels of 10 read-through transcripts in 201 samples of lung tissue were combined to identify expression quantitative trait loci (eQTLs). Then, to identify genes whose expression levels correlated with the 10 read-through transcripts, we used whole transcriptome profiles available for 154 patients. For 8 read-though transcripts, we identified 60 eQTLs (false discovery rate <0.05), including 17 cis-eQTLs and 43 trans-eQTLs. These eQTLs did not maintain their behavior on the 'parental' genes involved in the read-through transcriptional event. The expression levels of 7 read-through transcripts were found to correlate with the expression of other genes: CHIA-PIFO and CTSC-RAB38 correlated with CHIA and RAB38, respectively, while 5 other read-through transcripts correlated with 43 unique non-parental transcripts; thus offering indications about the molecular processes in which these chimeric transcripts may be involved. We confirmed 9 eQTLs (for 4 transcripts) in the non-involved lung tissue from an independent series of 188 lung adenocarcinoma patients. Therefore, this study indicates that the expression of four read-through transcripts in normal lung tissue is under germline genetic regulation, and that this regulation is independent of that of the genes involved in the read-through event., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Alterations in the gene expression of organs in contact with the environment may signal exposure to toxins. To identify genes in lung tissue whose expression levels are altered by cigarette smoking, we compared the transcriptomes of lung tissue between 118 ever smokers and 58 never smokers. In all cases, the tissue studied was non-involved lung tissue obtained at lobectomy from patients with lung adenocarcinoma. Of the 17,097 genes analyzed, 357 were differentially expressed between ever smokers and never smokers (FDR < 0.05), including 290 genes that were up-regulated and 67 down-regulated in ever smokers. For 85 genes, the absolute value of the fold change was ≥2. The gene with the smallest FDR was MYO1A (FDR = 6.9 × 10 -4 ) while the gene with the largest difference between groups was FGG (fold change = 31.60). Overall, 100 of the genes identified in this study (38.6%) had previously been found to associate with smoking in at least one of four previously reported datasets of non-involved lung tissue. Seven genes (KMO, CD1A, SPINK5, TREM2, CYBB, DNASE2B, FGG) were differentially expressed between ever and never smokers in all five datasets, with concordant higher expression in ever smokers. Smoking-induced up-regulation of six of these genes was also observed in a transcription dataset from lung tissue of non-cancer patients. Among the three most significant gene networks, two are involved in immunity and inflammation and one in cell death. Overall, this study shows that the lung parenchyma transcriptome of smokers has altered gene expression and that these alterations are reproducible in different series of smokers across countries. Moreover, this study identified a seven-gene panel that reflects lung tissue exposure to cigarette smoke.