Five-hub genes identify potential mechanisms for the progression of asthma to lung cancer.

High BRCA1 expression is an independent prognostic biomarker in LUAD and correlates with immune infiltration

G-protein coupled estrogen receptor 1 (GPER1) is involved in estrogen response and associated with tumorigenesis in several solid tumors, and we previously reported that its positive expression rate is more than 80% in lung cancer. However, GPER1 has been less studied during the tumorigenesis in non-small cell lung cancer (NSCLC). We used Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC) data from the Cancer Genome Atlas and Gene Expression Omnibus databases, and self-sequencing data of whole transcriptome of A549 cells for research. Firstly, the expression of GPER1 in adjacent tissues and cancer tissues was compared. Then, according to the median of GPER1, LUAD and LUSC samples in the data set were assigned into GPER1 high and low expression group, respectively. The hub genes were enriched and analyzed. Additionally, the association between GPER1 expression and immune related responses were explored. We obtained GPER1-inhibited and -activated differentially expressed genes (DEGs) from the self-sequencing data. Finally, the GPER1-related competitive endogenous RNA (ceRNA) network was constructed and verified by experiments. The expression of GPER1 in cancer tissues was decreased when compared with that in healthy tissues. The GPER1 gene has good diagnostic value as a differentiator between cancer and normal samples in LUAD- and LUSC-related datasets. Fourteen and thirteen hub genes were identified in LUAD and LUSC, respectively. They were all enriched in the pathways of actin cytoskeleton regulation, extracellular matrix assembly, PI3K-Akt signaling pathway. In addition, GPER1 was significantly associated with immune cells infiltration and expression of common immune checkpoints, and its low expression could predict benefit from immune checkpoint blockade (ICB) treatment in LUSC. The whole-transcriptome sequencing data of A549 cells were analyzed to obtain 132 GPER1 repression- and 39 GPER1 activation-related mRNA, and 13 hub genes were finally screened. Further, the GPER1-related transcription factor (TF)-miRNA–mRNA network and lncRNA/circRNA–miRNA–mRNA ceRNA networks were constructed. Finally, confirmatory studies demonstrated that the hub genes and MAPK signaling pathway were regulated by GPER1, and knockdown of GPER1 expression caused F-actin cytoskeleton rearrangement and promoted cell migration in A549 cells. The expression of GPER1 was decreased during the tumorigenesis in NSCLC. GPER1 was significantly associated with immune cells infiltration and immune checkpoints expression in both LUAD and LUSC, and its low expression could predict benefit from ICB treatment in LUSC. In addition, we report a ceRNA network that may provide new insight into the roles of GPER1 in NSCLC development.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-15405-x.

Identification of Novel Tumor-Microenvironment-Regulating Factor That Facilitates Tumor Immune Infiltration in Colon Cancer

Interferon-induced protein 44-like (IFI44L), a type I interferon-stimulated gene (ISG), has been reported to be involved in innate immune processes and to act as a tumor suppressor in several cancers. However, its immune implication on lung cancer remains unclear. Here, we systemically analyzed the immune association of IFI44L with multiple tumor-infiltrating immune cells (TIICs) and immunomodulators through bioinformatics methods in The Cancer Genome Atlas (TCGA) lung cancer cohorts. Then, the IFI44L-related immunomodulators were selected to construct the prognostic signatures in the lung adenocarcinoma (LUAD) cohort and the lung squamous cell carcinoma (LUSC) cohort, respectively. Concordance index and time-dependent receiver operating characteristics (ROC) curves were applied to evaluate the prognostic signatures. GSE72094 and GSE50081 were used to validate the TCGA-LUAD signature and TCGA-LUSC signature, respectively. A nomogram was established by risk score and clinical features in the LUAD cohort. Finally, the prognostic value and biological function of IFI44L were verified in a real-world cohort and in vitro experiments. The results indicated that IFI44L showed significant correlation with TIICs in LUAD and LUSC samples. Functional enrichment analysis showed that IFI44L may participate in various cancer/immune-related pathways, including JAK/STAT signaling pathway and NF-κB signaling pathway. A total of 44 immunomodulators presented obvious association with IFI44L in the TCGA-LUAD cohort and a robust 10-immunomodulator signature was constructed. Patients in the higher-risk group presented worse prognosis than those in the lower-risk group. Notably, the risk signature was successfully validated in GSE72094. Multivariate Cox regression suggested that the risk signature could act as independent prognostic factors in both TCGA-LUAD and GSE72094 cohorts. Besides, a 17-immunomodulator signature was established in the TCGA-LUSC cohort and similar results were presented through analysis. The nomogram exhibited good accuracy in predicting overall survival (OS) outcome among TCGA-LUAD patients than the risk signature and other clinical features, with the area under curve values being 0.782 at 1 year, 0.825 at 3 years, and 0.792 at 5 years. Finally, tissue microarray analysis indicated that higher expression of IFI44L presented opposite relationship with pathological stage (p = 0.016) and a better outcome among lung cancer patients (p = 0.024). Functional experiments found that IFI44L overexpression significantly inhibited the proliferation, migration, and invasion in LUAD and LUSC cells; RT-qPCR experiments verified the correlation between the expression level of IFI44L with multiple immunomodulators in SPC-A-1 and NCI-H520 cells. In conclusion, our research highlighted that IFI44L is associated with tumor immune infiltration and provided information on IFI44L’s immune implication, which indicates that IFI44L has potential clinical immunotherapeutic value and the proposed nomogram is a promising biomarker for non-small cell lung cancer patients.

Accumulating evidence indicates that immunotherapy helped to improve survival and quality of life of patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC), besides chemotherapy and gene targeting treatment. This study aimed to develop immune-related gene signatures in LUAD and LUSC subtypes, respectively. LUAD and LUSC samples were divided into high- and low-abundance groups of immune cell infiltration (Immunity_H and Immunity_L) based on abundance of immune cell infiltrations. The distribution of immune cells was significantly different between high- and low-immunity subtypes in LUAD and LUSC samples. The differentially expressed genes (DEGs) between those two groups in LUAD and LUSC contain some key immune-related genes, such as PDL1, PD1, CTLA-4, and HLA families. The DEGs were enriched in multiple immune-related pathways. Furthermore, the seven-immune-related-gene-signature prognostic model (CD1B, CHRNA6, CLEC12B, CLEC17A, CLNK, INHA, and SLC14A2)-based high- and low-risk groups were significantly associated with LUAD overall survival and clinical characteristics. The eight-immune-related-gene-signature prognostic model (C4BPB, FCAMR, GRAPL, MAP1LC3C, MGC2889, TRIM55, UGT1A1, and VIPR2)-based high- and low-risk groups were significantly associated with LUSC overall survival and clinical characteristics. The prognostic models were tested as good ones by receiver operating characteristic (ROC), principal components analysis (PCA), univariate and multivariate analysis, and nomogram. The verifications of these two immune-related-gene-signature prognostic models showed consistency in train and test cohorts of LUAD and LUSC. In addition, patients with LUAD in the low-risk group responded better to immunotherapy than those in the high-risk group. This study revealed two reliable immune-related-gene signature models that were significantly associated with prognosis and tumor microenvironment cell infiltration in LUAD and LUSC, respectively. Evaluation of the integrated characterization of multiple immune-related-genes and pathways could help to predict the response to immunotherapy and monitor immunotherapy strategies.

Accumulating evidence indicates that immunotherapy helped to improve survival and quality of life of patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC), besides chemotherapy and gene targeting treatment. This study aimed to develop immune-related gene signatures in LUAD and LUSC subtypes, respectively. LUAD and LUSC samples were divided into high- and low-abundance groups of immune cell infiltration (Immunity_H and Immunity_L) based on abundance of immune cell infiltrations. The distribution of immune cells was significantly different between high- and low-immunity subtypes in LUAD and LUSC samples. The differentially expressed genes (DEGs) between those two groups in LUAD and LUSC contain some key immune-related genes, such as PDL1, PD1, CTLA-4, and HLA families. The DEGs were enriched in multiple immune-related pathways. Furthermore, the seven-immune-related-gene-signature prognostic model (CD1B, CHRNA6, CLEC12B, CLEC17A, CLNK, INHA, and SLC14A2)-based high- and low-risk groups were significantly associated with LUAD overall survival and clinical characteristics. The eight-immune-related-gene-signature prognostic model (C4BPB, FCAMR, GRAPL, MAP1LC3C, MGC2889, TRIM55, UGT1A1, and VIPR2)-based high- and low-risk groups were significantly associated with LUSC overall survival and clinical characteristics. The prognostic models were tested as good ones by receiver operating characteristic (ROC), principal components analysis (PCA), univariate and multivariate analysis, and nomogram. The verifications of these two immune-related-gene-signature prognostic models showed consistency in train and test cohorts of LUAD and LUSC. In addition, patients with LUAD in the low-risk group responded better to immunotherapy than those in the high-risk group. This study revealed two reliable immune-related-gene signature models that were significantly associated with prognosis and tumor microenvironment cell infiltration in LUAD and LUSC, respectively. Evaluation of the integrated characterization of multiple immune-related-genes and pathways could help to predict the response to immunotherapy and monitor immunotherapy strategies.

Accumulating evidence indicates that immunotherapy helped to improve the survival and quality-of-life of patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC) besides chemotherapy and gene targeting treatment. This study aimed to develop immune-related gene signatures in LUAD and LUSC subtypes, respectively. LUAD and LUSC samples were divided into high- and low-abundance groups of immune cell infiltration (Immunity_H and Immunity_L) based on the abundance of immune cell infiltrations. The distribution of immune cells was significantly different between the high- and low-immunity subtypes in LUAD and LUSC samples. The differentially expressed genes (DEGs) between those two groups in LUAD and LUSC contain some key immune-related genes, such as PDL1, PD1, CTLA-4, and HLA families. The DEGs were enriched in multiple immune-related pathways. Furthermore, the seven-immune-related-gene-signature (CD1B, CHRNA6, CLEC12B, CLEC17A, CLNK, INHA, and SLC14A2) prognostic model-based high- and low-risk groups were significantly associated with LUAD overall survival and clinical characteristics. The eight-immune-related-gene-signature (C4BPB, FCAMR, GRAPL, MAP1LC3C, MGC2889, TRIM55, UGT1A1, and VIPR2) prognostic model-based high- and low-risk groups were significantly associated with LUSC overall survival and clinical characteristics. The prognostic models were tested as good ones by receiver operating characteristic, principal component analysis, univariate and multivariate analysis, and nomogram. The verifications of these two immune-related-gene-signature prognostic models showed consistency in the train and test cohorts of LUAD and LUSC. In addition, patients with LUAD in the low-risk group responded better to immunotherapy than those in the high-risk group. This study revealed two reliable immune-related-gene-signature models that were significantly associated with prognosis and tumor microenvironment cell infiltration in LUAD and LUSC, respectively. Evaluation of the integrated characterization of multiple immune-related genes and pathways could help to predict the response to immunotherapy and monitor immunotherapy strategies.

Accumulating evidence indicates that immunotherapy helped to improve survival and quality of life of patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC), besides chemotherapy and gene targeting treatment. This study aimed to develop immune-related gene signatures in LUAD and LUSC subtypes, respectively. LUAD and LUSC samples were divided into high- and low-abundance groups of immune cell infiltration (Immunity_H and Immunity_L) based on abundance of immune cell infiltrations. The distribution of immune cells was significantly different between high- and low-immunity subtypes in LUAD and LUSC samples. The differentially expressed genes (DEGs) between those two groups in LUAD and LUSC contain some key immune-related genes, such as PDL1, PD1, CTLA-4, and HLA families. The DEGs were enriched in multiple immune-related pathways. Furthermore, the seven-immune-related-gene-signature prognostic model (CD1B, CHRNA6, CLEC12B, CLEC17A, CLNK, INHA, and SLC14A2)-based high- and low-risk groups were significantly associated with LUAD overall survival and clinical characteristics. The eight-immune-related-gene-signature prognostic model (C4BPB, FCAMR, GRAPL, MAP1LC3C, MGC2889, TRIM55, UGT1A1, and VIPR2)-based high- and low-risk groups were significantly associated with LUSC overall survival and clinical characteristics. The prognostic models were tested as good ones by receiver operating characteristic (ROC), principal components analysis (PCA), univariate and multivariate analysis, and nomogram. The verifications of these two immune-related-gene-signature prognostic models showed consistency in train and test cohorts of LUAD and LUSC. In addition, patients with LUAD in the low-risk group responded better to immunotherapy than those in the high-risk group. This study revealed two reliable immune-related-gene signature models that were significantly associated with prognosis and tumor microenvironment cell infiltration in LUAD and LUSC, respectively. Evaluation of the integrated characterization of multiple immune-related-genes and pathways could help to predict the response to immunotherapy and monitor immunotherapy strategies.

Accumulating evidence indicates that immunotherapy helped to improve survival and quality of life of patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC), besides chemotherapy and gene targeting treatment. This study aimed to develop immune-related gene signatures in LUAD and LUSC subtypes, respectively. LUAD and LUSC samples were divided into high- and low-abundance groups of immune cell infiltration (Immunity_H and Immunity_L) based on abundance of immune cell infiltrations. The distribution of immune cells was significantly different between high- and low-immunity subtypes in LUAD and LUSC samples. The differentially expressed genes (DEGs) between those two groups in LUAD and LUSC contain some key immune-related genes, such as PDL1, PD1, CTLA-4, and HLA families. The DEGs were enriched in multiple immune-related pathways. Furthermore, the seven-immune-related-gene-signature prognostic model (CD1B, CHRNA6, CLEC12B, CLEC17A, CLNK, INHA, and SLC14A2)-based high- and low-risk groups were significantly associated with LUAD overall survival and clinical characteristics. The eight-immune-related-gene-signature prognostic model (C4BPB, FCAMR, GRAPL, MAP1LC3C, MGC2889, TRIM55, UGT1A1, and VIPR2)-based high- and low-risk groups were significantly associated with LUSC overall survival and clinical characteristics. The prognostic models were tested as good ones by receiver operating characteristic (ROC), principal components analysis (PCA), univariate and multivariate analysis, and nomogram. The verifications of these two immune-related-gene-signature prognostic models showed consistency in train and test cohorts of LUAD and LUSC. In addition, patients with LUAD in the low-risk group responded better to immunotherapy than those in the high-risk group. This study revealed two reliable immune-related-gene signature models that were significantly associated with prognosis and tumor microenvironment cell infiltration in LUAD and LUSC, respectively. Evaluation of the integrated characterization of multiple immune-related-genes and pathways could help to predict the response to immunotherapy and monitor immunotherapy strategies.

Accumulating evidence indicates that immunotherapy helped to improve survival and quality of life of patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC), besides chemotherapy and gene targeting treatment. This study aimed to develop immune-related gene signatures in LUAD and LUSC subtypes, respectively. LUAD and LUSC samples were divided into high- and low-abundance groups of immune cell infiltration (Immunity_H and Immunity_L) based on abundance of immune cell infiltrations. The distribution of immune cells was significantly different between high- and low-immunity subtypes in LUAD and LUSC samples. The differentially expressed genes (DEGs) between those two groups in LUAD and LUSC contain some key immune-related genes, such as PDL1, PD1, CTLA-4, and HLA families. The DEGs were enriched in multiple immune-related pathways. Furthermore, the seven-immune-related-gene-signature prognostic model (CD1B, CHRNA6, CLEC12B, CLEC17A, CLNK, INHA, and SLC14A2)-based high- and low-risk groups were significantly associated with LUAD overall survival and clinical characteristics. The eight-immune-related-gene-signature prognostic model (C4BPB, FCAMR, GRAPL, MAP1LC3C, MGC2889, TRIM55, UGT1A1, and VIPR2)-based high- and low-risk groups were significantly associated with LUSC overall survival and clinical characteristics. The prognostic models were tested as good ones by receiver operating characteristic (ROC), principal components analysis (PCA), univariate and multivariate analysis, and nomogram. The verifications of these two immune-related-gene-signature prognostic models showed consistency in train and test cohorts of LUAD and LUSC. In addition, patients with LUAD in the low-risk group responded better to immunotherapy than those in the high-risk group. This study revealed two reliable immune-related-gene signature models that were significantly associated with prognosis and tumor microenvironment cell infiltration in LUAD and LUSC, respectively. Evaluation of the integrated characterization of multiple immune-related-genes and pathways could help to predict the response to immunotherapy and monitor immunotherapy strategies.

Accumulating evidence indicates that immunotherapy helped to improve survival and quality of life of patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC), besides chemotherapy and gene targeting treatment. This study aimed to develop immune-related gene signatures in LUAD and LUSC subtypes, respectively. LUAD and LUSC samples were divided into high- and low-abundance groups of immune cell infiltration (Immunity_H and Immunity_L) based on abundance of immune cell infiltrations. The distribution of immune cells was significantly different between high- and low-immunity subtypes in LUAD and LUSC samples. The differentially expressed genes (DEGs) between those two groups in LUAD and LUSC contain some key immune-related genes, such as PDL1, PD1, CTLA-4, and HLA families. The DEGs were enriched in multiple immune-related pathways. Furthermore, the seven-immune-related-gene-signature prognostic model (CD1B, CHRNA6, CLEC12B, CLEC17A, CLNK, INHA, and SLC14A2)-based high- and low-risk groups were significantly associated with LUAD overall survival and clinical characteristics. The eight-immune-related-gene-signature prognostic model (C4BPB, FCAMR, GRAPL, MAP1LC3C, MGC2889, TRIM55, UGT1A1, and VIPR2)-based high- and low-risk groups were significantly associated with LUSC overall survival and clinical characteristics. The prognostic models were tested as good ones by receiver operating characteristic (ROC), principal components analysis (PCA), univariate and multivariate analysis, and nomogram. The verifications of these two immune-related-gene-signature prognostic models showed consistency in train and test cohorts of LUAD and LUSC. In addition, patients with LUAD in the low-risk group responded better to immunotherapy than those in the high-risk group. This study revealed two reliable immune-related-gene signature models that were significantly associated with prognosis and tumor microenvironment cell infiltration in LUAD and LUSC, respectively. Evaluation of the integrated characterization of multiple immune-related-genes and pathways could help to predict the response to immunotherapy and monitor immunotherapy strategies.

BackgroundPrognostic genes in the tumor microenvironment play an important role in immune biological processes and the response of cancer to immunotherapy. Thus, we aimed to assess new biomarkers that are associated with immune/stromal cells in lung adenocarcinomas (LUAD) using the ESTIMATE algorithm, which also significantly affects the prognosis of cancer.MethodsThe RNA sequencing (RNA-Seq) and clinical data of LUAD were downloaded from the the Cancer Genome Atlas (TCGA ). The immune and stromal scores were calculated for each sample using the ESTIMATE algorithm. The LUAD gene chip expression profile data and the clinical data (GSE37745, GSE11969, and GSE50081) were downloaded from the Gene Expression Omnibus (GEO) for subsequent validation analysis. Differentially expressed genes were calculated between high and low score groups. Univariate Cox regression analysis was performed on differentially expressed genes (DEGs) between the two groups to obtain initial prognosis genes. These were verified by three independent LUAD cohorts from the GEO database. Multivariate Cox regression was used to identify overall survival-related DEGs. UALCAN and the Human Protein Atlas were used to analyze the mRNA /protein expression levels of the target genes. Immune cell infiltration was evaluated using the Tumor Immune Estimation Resource (TIMER) and CIBERSORT methods, and stromal cell infiltration was assessed using xCell.ResultsIn this study, immune scores and stromal scores are significantly associated with the clinical characteristics of LUAD, including T stage, M stage, pathological stage, and overall survival time. 530 DEGs (18 upregulated and 512 downregulated) were found to coexist in the difference analysis with the immune scores and stromal scores subgroup. Univariate Cox regression analysis showed that 286 of the 530 DEGs were survival-related genes (p < 0.05). Of the 286 genes initially identified, nine prognosis-related genes (CSF2RB, ITK, FLT3, CD79A, CCR4, CCR6, DOK2, AMPD1, and IGJ) were validated from three separate LUAD cohorts. In addition, functional analysis of DEGs also showed that various immunoregulatory molecular pathways, including regulation of immune response and the chemokine signaling pathways, were involved. Five genes (CCR6, ITK, CCR4, DOK2, and AMPD1) were identified as independent prognostic indicators of LUAD in specific data sets. The relationship between the expression levels of these genes and immune genes was assessed. We found that CCR6 mRNA and protein expression levels of LUAD were greater than in normal tissues. We evaluated the infiltration of immune cells and stromal cells in groups with high and low levels of expression of CCR6 in the TCGA LUAD cohort. In summary, we found a series of prognosis-related genes that were associated with the LUAD tumor microenvironment.

Background: Lung cancer is a leading cause of death. Despite advancements in early diagnosis and treatment, most patients are diagnosed at later stages, and the average 5-year survival rate is &amp;lt; 30%. Lung adenocarcinoma (LUAD) is the most common non-small cell lung cancer (NSCLC), representing about 40% of cases, followed by lung squamous cell carcinoma (LUSC) at 25%. The biological patterns and molecular characteristics of LUAD and LUSC exhibit differences. Changes in DNA methylation levels of various genes have been observed in lung cancer subtypes, yet research on differences in DNA methylation patterns between LUAD and LUSC is limited. Methods: The methylation microarray dataset (GSE39279, Illumina HumanMethylation450 BeadChip) from Gene Expression Omnibus at the National Center for Biotechnology Information was used. The dataset comprised 444 NSCLC samples derived from tumor tissues, of which 322 were LUAD and 122 were LUSC. We used the Champ (Chip Analysis Methylation Pipeline) package to identify differentially methylated probes (DMPs, Benjamini-Hochberg adjusted p-value &amp;lt; 0.05) and genes representing specific biological pathways between LUAD and LUSC. Singular Value Decomposition regression analysis was used to estimate the impact of age, sex, and smoking status and then adjusted for the covariates with p-values &amp;lt; 0.05. We used the eFORGE TF database to calculate the Find Individual Motif Occurrences (FIMO) p-values for the overlap with transcription factor binding sites. The Gene Expression Profiling Interactive Analysis database was used to measure the gene expression levels of LUAD and LUSC samples in the Genotype-Tissue Expression and the Cancer Genome Atlas Program portals. Results: In total, 223,007 DMPs were identified by comparing LUAD and LUAC, with 130,610 hypomethylated and 92,467 hypermethylated. Using the absolute difference of β values between groups ≥ 0.3, we identified 15 DMPs, 11 hypomethylated and four hypermethylated. The DMPs on promoter regions were all hypomethylated in LUSC compared to LUAD (cg00415665 and cg22997040 in ZHX2, cg27649037 in ST18, cg20691436 in CALML3, and cg24580076 in C7orf20). We also identified DMPs based on the importance scores (&amp;gt; 0.1) from a 5-fold cross-validation random forest analysis. The two DMPs in the ZHX2 gene had the highest importance scores (&amp;gt; 8.0). Among those, cg00415665 was covered by the Zfp161_secondary motif (FIMO p-value: &amp;lt; 10-5). The average expression level of ZHX2 in LUSC (transcripts per million, TPM=9.7) was higher than in LUAD (TPM=8.2). Conclusions: We characterized the methylation landscape of NSCLCs by histological subtypes and identified that the methylation pattern of cg00415665 in ZHX2, a tumor suppressor gene, was significantly associated with the histological subtype of NSCLC. Further investigation of these findings will provide additional insight into the biology and genetics of LUAD and LUSC. Citation Format: Hyeyeun Lim, Christopher I. Amos, Jinyoung Byun, Aaron P. Aaron. Comparing the methylation profiles between lung adenocarcinoma and squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2106.

The present study aimed to identify the differentially expressed genes (DEGs) between lung adenocarcinoma and normal lung tissues, and between lung squamous cell carcinoma and normal lung tissues, with the purpose of identifying potential biomarkers for the treatment of lung cancer. The gene expression profile (GSE6044) was downloaded from the Gene Expression Omnibus database, which included data from 10 lung adenocarcinoma samples, 10 lung squamous cell carcinoma samples, and five matched normal lung tissue samples. After data processing, DEGs were identified using the Student's t-test adjusted via the Benjamini-Hochberg method. Subsequently, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the DEGs was performed using the Database for Annotation, Visualization and Integrated Discovery, and a global network was constructed. A total of 95 upregulated and 241 downregulated DEGs were detected in lung adenocarcinoma samples, and 204 upregulated and 285 downregulated DEGs were detected in lung squamous cell carcinoma samples, as compared with the normal lung tissue samples. The DEGs in the lung squamous cell carcinoma group were enriched in the following three pathways: Hsa04110, Cell cycle; hsa03030, DNA replication; and hsa03430, mismatch repair. However, the DEGs in the lung adenocarcinoma group were not significantly enriched in any specific pathway. Subsequently, a global network of lung cancer was constructed, which consisted of 341 genes and 1,569 edges, of which the top five genes were HSP90AA1, BCL2, CDK2, KIT and HDAC2. The expression trends of the above genes were different in lung adenocarcinoma and lung squamous cell carcinoma when compared with normal tissues. Therefore, these genes were suggested to be crucial genes for differentiating lung adenocarcinoma and lung squamous cell carcinoma.

Lung cancer is the second most frequently diagnosed cancer type and responsible for the highest number of cancer deaths worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are subtypes of non-small-cell lung cancer which has the highest frequency of lung cancer cases. We aimed to analyze genomic and transcriptomic variations including simple nucleotide variations (SNVs), copy number variations (CNVs) and differential expressed genes (DEGs) in order to find key genes and pathways for diagnostic and prognostic prediction for lung adenocarcinoma and lung squamous cell carcinoma. We performed a univariate Cox model and then lasso-regularized Cox model with leave-one-out cross-validation using The Cancer Genome Atlas (TCGA) gene expression data in tumor samples. We generated 35- and 33-gene signatures for prognostic risk prediction based on the overall survival time of the patients with LUAD and LUSC, respectively. When we clustered patients into high- and low-risk groups, the survival analysis showed highly significant results with high prediction power for both training and test datasets. Then, we characterized the differences including significant SNVs, CNVs, DEGs, active subnetworks, and the pathways. We described the results for the risk groups and cancer subtypes separately to identify specific genomic alterations between both high-risk groups and cancer subtypes. Both LUAD and LUSC high-risk groups have more downregulated immune pathways and upregulated metabolic pathways. On the other hand, low-risk groups have both up- and downregulated genes on cancer-related pathways. Both LUAD and LUSC have important gene alterations such as CDKN2A and CDKN2B deletions with different frequencies. SOX2 amplification occurs in LUSC and PSMD4 amplification in LUAD. EGFR and KRAS mutations are mutually exclusive in LUAD samples. EGFR, MGA, SMARCA4, ATM, RBM10, and KDM5C genes are mutated only in LUAD but not in LUSC. CDKN2A, PTEN, and HRAS genes are mutated only in LUSC samples. The low-risk groups of both LUAD and LUSC tend to have a higher number of SNVs, CNVs, and DEGs. The signature genes and altered genes have the potential to be used as diagnostic and prognostic biomarkers for personalized oncology.