The Prognostic Value and Potential Mechanism of Tumor-Nutrition-Inflammation Index and Genes in Patients with Advanced Lung Cancer.

Background: Lung cancer (LC) has the highest mortality rate all over the world. It is necessary to search for novel potential biomarkers that are easily accessible and inexpensive in identifying patients with LC at early stage. Methods: A total of 195 patients with advanced LC who have received first-line chemotherapy were involved in this study. The optimized cut-off values of AGR and SIRI (AGR = albumin/globulin; SIRI = neutrophil ∗ monocyte/lymphocyte) were determined by survival function analysis based on R software. COX regression analysis was performed to obtain the independent factors for establishing the nomogram model. A nomogram model comprising these independent prognostic parameters was built for the TNI (tumor-nutrition-inflammation index) score calculation. The predictive accuracy was demonstrated through ROC curve and calibration curves after index concordance. Results: The optimized cut-off values of AGR and SIRI were 1.22 and 1.60, respectively. It was revealed that liver metastasis, SCC, AGR, and SIRI were independent prognostic factors in advanced lung cancer by Cox analysis. Afterwards, the nomogram model comprised of these independent prognostic parameters was built for TNI scores calculation. Based on the TNI quartile values, patients were divided into four groups. And it was indicated that higher TNI had worse OS (P < 0.05) via Kaplan-Meier analysis and log-rank test. Moreover, the C-index and 1-year AUC area were 0.756 (0.723-0.788) and 75.62, respectively. There was high consistency shown in the calibration curves between predicted and actual survival proportions in the TNI model. In addition, tumor-nutrition-inflammation index and genes play an important role in LC development that might affect some pathways related to tumor development including cell cycle, homologous recombination, and P53 signaling pathway from a molecular level. Conclusion: TNI might be an analytical tool which was practical and precise for survival prediction of patients with advanced LC. Tumor-nutrition-inflammation index and genes play an important role in LC development. A preprint has previously been published [1].

Identification of a Four-Gene Signature Associated with the Prognosis Prediction of Lung Adenocarcinoma Based on Integrated Bioinformatics Analysis.

Lung adenocarcinoma (LUAD) is often diagnosed at an advanced stage, so it is necessary to identify potential biomarkers for the early diagnosis and prognosis of LUAD. In our study, a gene co-expression network was constructed using weighted gene co-expression network analysis (WGCNA) in order to obtain the key modules and genes correlated with LUAD prognosis. Four hub genes (HLF, CHRDL1, SELENBP1, and TMEM163) were screened out using least absolute shrinkage and selection operator (LASSO)-Cox regression analysis; then, a prognostic model was established for predicting overall survival (OS) based on these four hub genes..Furthermore, the prognostic values of this four-gene signature were verified in four validation sets (GSE26939, GSE31210, GSE72094, and TCGA-LUAD) as well as in the GEPIA database. To assess the prognostic values of hub genes, receiver operating characteristic (ROC) curves were constructed and a nomogram was created. We found that a higher expression of four hub genes was associated with a lower risk of patient death. In a training set, it was demonstrated that this four-gene signature was a better prognostic factor than clinical factors such as age and stage of disease. Moreover, our results revealed that these four genes were suppressor factors of LUAD and that their high expression was associated with a lower risk of death. In summary, we demonstrated that this four-gene signature could be a potential prognostic factor for LUAD patients. These findings provide a theoretical basis for exploring potential biomarkers for LUAD prognosis prediction in the future.