Purpose: We aimed to identify new prognostic biomarkers of lung adenocarcinoma (LUAD) based on cancer stem cell theory. Materials and Methods: RNA-seq and microarray data were obtained with clinical information downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Weighted gene coexpression network analysis (WGCNA) was applied to identify significant module and hub genes. The hub genes were validated via microarray data from GEO, and a prognostic signature with prognostic hub genes was constructed. Results: LUAD patients enrolled from TCGA had a higher mRNA expression-based stemness index (mRNAsi) in tumor tissue than in adjacent normal tissue. Some clinical features and prognoses were found to be highly correlated with mRNAsi. WGCNA found that the green module and blue module were the most significant modules related to mRNAsi; 50 key genes were identified in the green module and were enriched mostly in the cell cycle, chromosome segregation, chromosomal region and microtubule binding. Six hub genes were revealed through the protein-protein interaction (PPI) network and Molecular Complex Detection (MCODE) plugin of Cytoscape software. Based on external verification with the GEO database, these six genes are not only expressed at different levels in LUAD and normal tissues but also associated with different clinical features. In addition, the construction of a prognostic signature with three hub genes showed high predictive value. Conclusion: mRNAsi-related biomarkers may suggest a new potential treatment strategy for LUAD.

The pathogenesis of small cell lung cancer (SCLC), a highly metastatic malignant tumor, remains unclear. In the present study, important genes and pathways that are involved in the pathogenesis of SCLC were identified. The following four datasets were downloaded from the Gene Expression Omnibus: GSE60052, GSE43346, GSE15240 and GSE6044. The differentially expressed genes (DEGs) between the SCLC samples and the normal samples were analyzed using R software. The limma package was used for every dataset. The RobustRankAggreg package was used to integrate the DEGs from the four datasets. Functional and pathway enrichment analyses were conducted using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases with FunRich software and R software, respectively. In addition, the protein‑protein interaction (PPI) network of the DEGs was constructed using the STRING database and Cytoscape software. Hub genes and significant modules were identified using Molecular Complex Detection in Cytoscape software. Finally, the expression values of hub genes were determined using the Oncomine online database. In total, 412 DEGs were identified following the integration of the four datasets, with 146 upregulated genes and 266 downregulated genes. The upregulated DEGs were primarily enriched in the cell cycle, cell division and microtubule binding. The downregulated DEGs were primarily enriched in the complement and coagulation cascades, the cytokine‑mediated signaling pathway and protein binding. Eight hub genes and 1 significant module correlated to the cell cycle pathway were identified based on a subset of the PPI network. Finally, five hub genes were identified as highly expressed in SCLC tissue compared with normal tissue. The cell cycle pathway may be the pathway most closely associated with the pathogenesis of SCLC. NDC80, BUB1B, PLK1, CDC20 and MAD2L1 should be the focus of follow‑up studies regarding the diagnosis and treatment of SCLC.

Background: An increasing number of studies have shown that the positive lymph node ratio (pLNR) can be used to evaluate the prognosis of non-small cell lung cancer (NSCLC) patients. To determine the predictive value of the pLNR, we collected data from the Surveillance, Epidemiology, and End Results (SEER) database and performed a retrospective analysis. Methods: We collected survival and clinical information on patients with T₁₋₄N₁₋₃M₀ NSCLC diagnosed between 2010 and 2016 from the SEER database and screened them according to inclusion and exclusion criteria. X-tile software was used to obtain the best cut-off value for the pLNR. Then, we randomly divided patients into a training set and a validation set at a ratio of 7:3. Pearson's correlation coefficient, tolerance and the variance inflation factor (VIF) were used to detect collinearity between variables. Univariate and multivariate Cox regression analyses were used to identify significant prognostic factors, and nomograms was constructed to visualize the results. The concordance index (C-index), calibration curves, and decision curve analysis (DCA) were used to assess the predictive ability of the nomogram. We divided the patient scores into four groups according to the interquartile interval and constructed a survival curve using Kaplan–Meier analysis. Results: A total of 6,245 patients were initially enrolled. The best cut-off value for the pLNR was determined to be 0.55. The nomogram contained 13 prognostic factors, including the pLNR. The pLNR was identified as an independent prognostic factor for both overall survival (OS) and cancer-specific survival (CSS). The C-index was 0.703 (95% CI, 0.695–0.711) in the training set and 0.711 (95% CI, 0.699–0.723) in the validation set. The calibration curves and DCA also indicated the good predictability of the nomogram. Risk stratification revealed a statistically significant difference among the four groups of patients divided according to quartiles of risk score. Conclusion: The nomogram containing the pLNR can accurately predict survival in patients with T₁₋₄N₁₋₃M₀ NSCLC.

Background: Tumor stem cells play important roles in the survival, proliferation, metastasis and recurrence of tumors. We aimed to identify new prognostic biomarkers for lung squamous cell carcinoma (LUSC) based on the cancer stem cell theory. Methods: RNA-seq data and relevant clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Weighted gene coexpression network analysis (WGCNA) was applied to identify significant modules and hub genes, and prognostic signatures were constructed with the prognostic hub genes. Results: LUSC patients in the TCGA database have higher mRNA expression-based stemness index (mRNAsi) in tumor tissue than in adjacent normal tissue. In addition, some clinical features and outcomes were highly correlated with the mRNAsi. WGCNA revealed that the pink and yellow modules were the most significant modules related to the mRNAsi; the top 10 hub genes in the pink module were enriched mostly in epidermal development, the secretory granule membrane, receptor regulator activity and the cytokine-cytokine receptor interaction. The protein–protein interaction (PPI) network revealed that the top 10 hub genes were significantly correlated with each other at the transcriptional level. In addition, the top 10 hub genes were all highly expressed in LUSC, and some were differentially expressed in different TNM stages. Regarding the survival analysis, the nomogram of a prognostic signature with three hub genes showed high predictive value. Conclusion: mRNAsi-related hub genes could be a potential biomarker of LUSC.

Idiopathic pulmonary fibrosis (IPF), a chronic, progressive, fibrosing interstitial lung disease, is associated with extremely poor prognosis, and lacks effective treatment. The frequently used immunosuppressive therapies such as dexamethasone (DEX) are often associated with side effects. Recently, combination of two Chinese herbal medicine preparations, Salvia miltiorrhiza and ligustrazine (SML), serves as an alternative medicine for treatment of IPF in clinical practices in China. The aim of this study is to compare the anti-fibrotic effect of SML with that of DEX and to investigate the underlying mechanisms. A rat model of bleomycin (BLM) induced pulmonary fibrosis was used in this study. Ninety rats were assigned to six groups: control group; BLM-group; BLM and dexamethasone group (BLM + DEX); BLM + low-dose SML; BLM + medium-dose SML and BLM + high-dose SML. Rats were sacrificed on day 7, 14 and 28 after treatment. The extent of alveolitis and fibrosis was observed by H&E and Masson’s trichrome staining. The expressions of TNF-α, TGF-β1 and SMAD4 were determined and quantified by immunohistochemical analysis. The serum levels of TNF-α and TGF-β1 were further quantified by ELISA kits. Both DEX and SML treatment attenuated BLM-induced lung injury and pathological collagen deposition in rats, showing improved alveolitis and fibrosis scores on day 7, 14, 28, compared to the BLM group (p < 0.05). The anti-fibrotic effect of SML was in a dose-dependent manner, and the medium- and high-dose SML showed comparable effect with DEX on day 14 and 28. Expressions of TNF-α, TGF-β1 and SMAD4 were significantly decreased in the DEX- and SML-treated groups compared with BLM groups (p < 0.05). Medium- and high-dose SML showed better repression of TNF-α, TGF-β1 and SMAD4 expression compared to DEX at all time points (p < 0.05). Notably, SML at different dosages did not affect serum levels of alanine aminotransferase, aspartate aminotransferase and creatinine. SML is safe and effective in repressing BLM-induced pulmonary fibrosis, which might be through modulating the expression of TNF-α and TGF-β1. Our findings advocate the use of SML for IPF, which might serve as a better treatment option over DEX.