Although the accuracy of quantitative real-time polymerase chain reaction (qRT-PCR) is highly dependent on the reliable reference genes, many commonly used reference genes are not stably expressed and as such are not suitable for quantification and normalization of qRT-PCR data. The aim of this study was to identify novel reliable reference genes in lung squamous-cell carcinoma. We used RNA sequencing (RNA-Seq) to survey the whole genome expression in 5 lung normal samples and 44 lung squamous-cell carcinoma samples. We evaluated the expression profiles of 15 commonly used reference genes and identified five additional candidate reference genes. To validate the RNA-Seq dataset, we used qRT-PCR to verify the expression levels of these 20 genes in a separate set of 100 pairs of normal lung tissue and lung squamous-cell carcinoma samples, and then analyzed these results using geNorm and NormFinder. With respect to 14 of the 15 common reference genes (B2M, GAPDH, GUSB, HMBS, HPRT1, IPO8, PGK1, POLR2A, PPIA, RPLP0, TBP, TFRC, UBC, and YWHAZ), the expression levels were either too low to be easily detected, or exhibited a high degree of variability either between lung normal and squamous-cell carcinoma samples, or even among samples of the same tissue type. In contrast, 1 of the 15 common reference genes (ACTB) and the 5 additional candidate reference genes (EEF1A1, FAU, RPS9, RPS11, and RPS14) were stably and constitutively expressed at high levels in all the samples tested. ACTB, EEF1A1, FAU, RPS9, RPS11, and RPS14 are ideal reference genes for qRT-PCR analysis of lung squamous-cell carcinoma, while 14 commonly used qRT-PCR reference genes are less appropriate in this context.

Immune infiltration may predict survival and have clinical significance in lung cancer. However, immune signatures derived from immune profiling based on bulk tumor transcriptomes have not been systematically established in lung adenocarcinoma. We aimed to construct an immune cell infiltrating score, using a new algorithm for evaluating immune infiltration, to improve the prognostic model of lung adenocarcinoma. Public datasets of lung adenocarcinoma from the Gene Expression Omnibus and The Cancer Genome Atlas were adopted as the training and validation cohorts. Fractions of different immune cell subtypes in each sample were estimated using the CIBERSORT algorithm. The immune infiltrating score was further developed by a least absolute shrinkage and selection operator regression model. The prognostic value and clinical relationship of the model was then further explored. An immune infiltrating score model was established on the basis of the immune cells in the training cohort. A high score was associated with significantly worse survival in patients with lung adenocarcinoma (P < 0.001). The prognostic value of the score was confirmed in the validation cohort. The immune infiltrating score could improve the accuracy of predictions of survival when combined with the staging system. Furthermore, the score was potentially associated with patient smoking status and histologic subtype of lung adenocarcinoma. Its possible association with the efficacy of adjuvant chemotherapy was not statistically significant. The immune cell infiltrating score has prognostic significance in predicting overall survival in patients with lung adenocarcinoma.

Background. The tumor immune microenvironment (TIME) played an important role in immunotherapy prognosis and treatment response. Immune cells constitute a large part of the tumor microenvironment and regulate tumor progression. Our research is dedicated to studying the infiltrating immune cell in lung adenocarcinoma (LUAD) and seeking potential targets. Methods. The scRNA-seq data were collected from our FDZSH and two public datasets. The code for cell-type mapping algorithms was downloaded from the CIBERSORTx portal. The bioinformatics data of LUAD patients could be approached from The Cancer Genome Atlas (TCGA) portal. Weighted gene coexpression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) analyses were performed to construct a risk model. TIMER2 and TIDE helped with the immune infiltration estimation, while PROGENy helped the cancer-related pathways enrichment analysis. GSE31210 dataset and IMVigor ICB therapy cohort validated our findings as the external validation datasets. Results. We clustered the scRNA-seq dataset (integrating our FDZSH datasets and other public datasets) into 23 subpopulations. After curated cell annotation, we implemented Cibersort and WGCNA analysis to anchor the brown module and natural killer cell cluster1 due to the most relationship with tumor trait. The overlap of the brown module gene, natural killer cell signature, and DEGs of tumor and adjacent normal samples was screened by LASSO Cox regression. The obtained 5-gene risk model showed an excellent prognostic performance in the validation dataset. Furthermore, there was a correlation between risk score and tumor-infiltrating immune cells and tumor genomics abnormity. Patients with higher risk scores had a significantly lower immunotherapy response rate. Conclusion. Our observations implied that immune cells played a pivotal role in TIME and established a 5-gene signature (including IDH2, ADRB2, SFTPC, CCDC69, and CCND2) on the basement of nature killer markers targeted by WGCNA analysis. The significance of clinical outcome and immunotherapy response prediction was validated robustly.

The present study aimed to explore gene and microRNA (miRNA) expression differences between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified by analyzing mRNA and miRNA expression data in normal and cancerous lung tissues that were obtained from The Cancer Genome Atlas database. A total of 778 DEGs and 7 DEMs were identified. Altered gene functions and signaling pathways were investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, which revealed that DEGs were significantly enriched in extracellular matrix organization, cell differentiation, negative regulation of toll signaling pathway, and several other terms and pathways. Transcription factor (TF)‑miRNA‑gene networks in LUAD and LUSC were predicted using the TargetScan, Miranda, and TRANSFAC databases, which revealed the regulatory links among the TFs, DEMs, and DEGs. The central TFs, i.e., the TFs in the middle of the TF‑miRNA‑gene network, of LUAD and LUSC were similar. Although LUAD and LUSC shared similar miRNAs in the predicted networks, miR‑29b‑3p was demonstrated to be upregulated only in LUAD, whereas miR‑1, miR‑105‑5p, and miR‑193b‑5p were altered in LUSC. These findings may improve our understanding of the different molecular mechanisms in non‑small cell lung cancers and may promote new and accurate strategies for prevention, diagnosis, and treatment.