Molecular characterization of early-stage multi-primary lung adenocarcinoma by transcriptome sequencing—a retrospective study

Overall, 194 differentially expressed genes were identified (<i>P</i> < 0.05), including 22 up-regulated and 172 down-regulated genes. Two up-regulated (<i>DUOX1</i> and <i>CACNA2D2</i>) and three down-regulated (<i>GPX8</i>, <i>COL1A2</i>, and <i>COL1A1</i>) genes were selected for validation by qRT-PCR analysis. The qRT-PCR results showed that the expression of <i>DUOX1</i> mRNA in the sMP-LUAD group was significantly higher (<i>P</i> < 0.05) than that in the SP-LUAD group; mRNA <i>CACNA2D2</i>, <i>GPX8</i>, <i>COL1A2</i>, and <i>COL1A1</i> expression in the sMP-LUAD group was not statistically different from that in the SP-LUAD group (<i>P</i> > 0.05). Gene ontology (GO) enrichment analysis showed that <i>DUOX1</i> mRNA was mainly enriched in the entries of positive regulation of wound healing and oxidation-reduction processes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that DUOX1 can promote reactive oxygen species (ROS) production and be related to thyroid hormone production. Furthermore, based on the TCGA database, we analyzed the biological behavior and clinical significance of DUOX1 in lung adenocarcinoma using bioinformatics technology. <i>DUOX1</i> mRNA expression was decreased in all stages and pathological subtypes of lung adenocarcinoma (<i>P</i> < 0.05). Immune infiltration analysis showed that DUOX1 with mast cells and eosinophils was positively correlated (<i>P</i> < 0.05), and Th2 cells were negatively correlated (<i>P</i> < 0.05). Logistic regression analysis showed that the expression of <i>DUOX1</i> mRNA was significantly correlated with the patient's age, lymph node metastasis, and pathologic stage (<i>P</i> < 0.05). Kaplan-Meier survival plots showed that low DUOX1 expression was not significantly correlated with OS, DSS, and PFI (<i>P</i> > 0.05). Univariate and multivariate COX regression analysis revealed that <i>DUOX1</i> mRNA could not be used as an independent factor for predicting prognosis (<i>P</i> > 0.05). Therefore, we developed a predictive nomogram model combining clinicopathological variables and <i>DUOX1</i> mRNA to predict the survival of patients with lung adenocarcinoma.