Identification of crucial genes associated with lung adenocarcinoma by bioinformatic analysis

Identification of LGR4 as a prognostic biomarker in KRAS-mutant lung adenocarcinoma: Evidence from integrated bioinformatics analysis

Globally, lung cancer is the leading cause of cancer-related deaths, primarily non-small cell lung cancer. Kirsten Rat Sarcoma Oncogene Homolog (KRAS) mutations are common in non-small cell lung cancer and linked to a poor prognosis. Covalent inhibitors targeting KRAS-G12C mutation have improved treatment for some patients, but most KRAS-mutant lung adenocarcinoma (KRAS-MT LUAD) cases lack targeted therapies. This gap in treatment options underscores a significant challenge in the field. Our study aimed to identify hub/key genes specifically associated with KRAS-MT LUAD. These hub genes hold the potential to serve as therapeutic targets or biomarkers, providing insights into the pathogenesis and prognosis of lung cancer. We performed a comprehensive analysis on KRAS-MT LUAD samples using diverse data sources. This included TCGA project data for RNA-seq, clinical information, and somatic mutations, along with RNA-seq data for adjacent normal tissues. DESeq2 identified differentially expressed genes (DEGs), while weighted gene co-expression network analysis revealed co-expression modules. Overlapping genes between DEGs and co-expression module with the highest significance were analyzed using gene set enrichment analysis and protein-protein interaction network analysis. Hub genes were identified with the Maximal Clique Centrality algorithm in Cytoscape. Prognostic significance was assessed through survival analysis and validated using the GSE72094 dataset from Gene Expression Omnibus (GEO) database. In KRAS-MT LUAD, 3122 DEGs were found (2131 up-regulated, 985 down-regulated). The blue module, among 25 co-expression modules from weighted gene co-expression network analysis, had the strongest correlation. 804 genes overlapped between DEGs and the blue module. Among 20 hub genes in the blue module, leucine-rich repeats containing G protein-coupled receptor 4 (LGR4) overexpression correlated with worse overall survival. The prognostic significance of LGR4 was confirmed using GSE72094, but surprisingly, the direction of the association was opposite to what was expected. LGR4 stands as a promising biomarker in KRAS-MT LUAD prognosis. Contrasting associations in TCGA and GSE72094 datasets reveal the intricate nature of KRAS-MT LUAD. Additional explorations are imperative to grasp the precise involvement of LGR4 in lung adenocarcinoma prognosis, particularly concerning KRAS mutations. These insights could potentially pave the way for targeted therapeutic interventions, addressing the existing unmet demands in this specific subgroup.

AURKA, TOP2A and MELK are the key genes identified by WGCNA for the pathogenesis of lung adenocarcinoma

The comprehensive analysis of single or multiple microarray datasets is currently available in Gene Expression Omnibus (GEO) databases, with several studies having identified genes strongly associated with the development of lung adenocarcinoma (LUAD). However, the mechanisms of LUAD development remain largely unknown and has not yet been systematically studied; thus, further studies are required in this field. In the present study, weighted gene co-expression network analysis (WGCNA) was used for the evaluation of key genes with potential high risk of LUAD, and to provide more reliable evidence concerning its pathogenesis. The GSE140797 dataset from the high-throughput GEO database was downloaded and was first analyzed using the Limma package in the R language in order to determine the differentially expressed genes. The dataset was then analyzed using the WGCNA package to analyze the co-expressed genes, and the modular genes with the highest correlation with the clinical phenotype were identified. Subsequently, the pathogenic genes shared in common between the result of the two analyses were imported into the STRING database for protein-protein interaction network analysis. The hub genes were screened out using Cytoscape, and then The Cancer Genome Atlas analysis, receiver operating characteristic analysis and survival analysis were subsequently performed. Finally, the key genes were evaluated using reverse transcription-quantitative PCR and western blot analysis. Bioinformatics analysis of the GSE140797 dataset revealed eight key genes: AURKA, BUB1, CCNB1, CDK1, MELK, NUSAP1, TOP2A and PBK. Finally, the AURKA, TOP2A and MELK genes were evaluated in samples from patients with lung cancer using WGCNA and RT-qPCR, western blot analysis experiments, providing basis for further research on the mechanisms of LUAD development and targeted therapy.

Circular RNA-mediated ceRNA network was identified in human lung adenocarcinoma by high-throughput sequencing

OBJECTIVES

Aberrantly expressed circular RNAs (circRNAs) have been detected in many types of tumors. Hence, they are currently investigated as candidate biomarkers for diagnostic and potential targets for therapy in cancers. The objective of this study was to assess the expression profile of circRNA in lung adenocarcinoma (LUAD).

METHODS

This study included 14 pairs of postoperative lung adenocarcinoma specimens, including cancer tissues and matched adjacent tissues. Second-generation sequencing was applied to the specimens to determine the circRNA expression in them among the 5242 distinct circRNAs detected.

RESULTS

We identified a total of 18 significantly dysregulated circRNAs in the LUAD tissues: upregulation in four and downregulation in 14. ROC (The receiver operating characteristic curve) further suggested that hsa_circ_0120106, has_circ_0007342, has_circ_0005937, and circRNA_0000826 could potentially be used as biomarkers in the diagnosis of LUAD. Furthermore, study of the circRNA-microRNA (miRNA)-messenger RNA (mRNA) revealed interactions between the 18 dysregulated circRNA and several cancer-related miRNAs. Finally, a further Kyoto Encyclopedia of Genes and Genomes analysis showed that the cell cycle phase transition, p53 signaling pathway, AMP-activated protein kinase (AMPK) relative signaling pathway, and so on were key putative pathways in the process of LUAD.

CONCLUSIONS

These findings demonstrated the correlation between abnormality in circRNA expression and LUAD, which lays the foundation of making CircRNAs candidate biomarkers in the diagnosis of LUAD.

Network module function enrichment analysis of lung squamous cell carcinoma and lung adenocarcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) are the two major subtypes of non-small cell lung cancer that pose a serious threat to human health. However, both subtypes currently lack effective indicators for early diagnosis.

METHODS

To identify tumor-specific indicators and predict cancer-related signaling pathways, LUSC and LUAD gene weighted co-expression networks were constructed. Combined with clinical data, core genes in LUSC and LUAD modules were then screened using protein-protein interaction networks and their functions and pathways were analyzed. Finally, the effect of core genes on survival of LUSC and LUAD patients was evaluated.

RESULTS

We identified 12 network modules in LUSC and LUAD, respectively. LUSC modules "purple" and "green" and LUAD modules "brown" and "pink" are significantly associated with overall survival and clinical traits of tumor node metastasis, respectively. Eleven genes from LUSC and eight genes from LUAD were identified as candidate core genes, respectively. Survival analysis showed that high expression of SLIT3, ABI3BP, MYOCD, PGM5, TNXB, and DNAH9 are associated with decreased survival in LUSC patients. Furthermore, high expression of BUB1, BUB1B, TTK, and UBE2C are associated with lower patient survival.

CONCLUSIONS

We found biomarker genes and biological pathways for LUSC and LUAD. These network hub genes are associated with clinical characteristics and patient outcomes and they may play important roles in LUSC and LUAD.

Hematogenesis Adaptation to Long-Term Hypoxia Acclimation in Zebrafish (Danio rerio)

When fish live in the wild or are cultured artificially, they will inevitably suffer from hypoxia. At the same time, blood physiological indexes represent the physiological state of fish. In order to study the effect of long-term hypoxia acclimation on fish hematogenesis, we cultured zebrafish embryos into adulthood in a hypoxia incubator (1.5 ± 0.2 mg/L). Then we compared the hematological parameters of zebrafish cultured in normoxia and hypoxia conditions. Transcriptome sequencing analysis of the main hematopoietic tissue, the head kidney, was also compared between the two groups. Results showed that the number of erythrocytes increased significantly in the long-term hypoxia acclimated group, while the size of several cell types, such as red blood cells, eosinophils, basophils, small lymphocytes and thrombocytes, decreased significantly. The transcriptomic comparisons revealed that there were 6475 differentially expressed genes (DEGs) between the two groups. A Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that hematopoiesis and cell proliferation signaling were the most significantly enriched pathways in the head kidney of hypoxia acclimated zebrafish. In addition, many genes involved in the hematopoietic process showed significantly higher levels of expression in the hypoxia acclimated zebrafish, when compared to the normoxia zebrafish. When considered together, these data allowed us to conclude that long-term hypoxia can promote the hematopoiesis process and cell proliferation signaling in the zebrafish head kidney, which resulted in higher red blood cell production. Higher numbers of red blood cells allow for better adaptation to the hypoxic environment. In conclusion, this study provides a basis for the in-depth understanding of the effects of hypoxia on hematogenesis in fish species.

Identification of a Four-Gene Signature With Prognostic Significance in Endometrial Cancer Using Weighted-Gene Correlation Network Analysis

Endometrial hyperplasia (EH) is a precursor for endometrial cancer (EC). However, biomarkers for the progression from EH to EC and standard prognostic biomarkers for EC have not been identified. In this study, we aimed to identify key genes with prognostic significance for the progression from EH to EC. Weighted-gene correlation network analysis (WGCNA) was used to identify hub genes utilizing microarray data (GSE106191) downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified from the Uterine Corpus Endometrial Carcinoma (UCEC) dataset of The Cancer Genome Atlas database. The Limma-Voom R package was applied to detect differentially expressed genes (DEGs; mRNAs) between cancer and normal samples. Genes with |log2 (fold change [FC])| > 1.0 and p < 0.05 were considered as DEGs. Univariate and multivariate Cox regression and survival analyses were performed to identify potential prognostic genes using hub genes overlapping in the two datasets. All analyses were conducted using R Bioconductor and related packages. Through WGCNA and overlapping genes in hub modules with DEGs in the UCEC dataset, we identified 42 hub genes. The results of the univariate and multivariate Cox regression analyses revealed that four hub genes, BUB1B, NDC80, TPX2, and TTK, were independently associated with the prognosis of EC (Hazard ratio [95% confidence interval]: 0.591 [0.382–0.912], p = 0.017; 0.605 [0.371–0.986], p = 0.044; 1.678 [1.132–2.488], p = 0.01; 2.428 [1.372–4.29], p = 0.02, respectively). A nomogram was established with a risk score calculated using the four genes’ coefficients in the multivariate analysis, and tumor grade and stage had a favorable predictive value for the prognosis of EC. The survival analysis showed that the high-risk group had an unfavorable prognosis compared with the low-risk group (p < 0.0001). The receiver operating characteristic curves also indicated that the risk model had a potential predictive value of prognosis with area under the curve 0.807 at 2 years, 0.783 at 3 years, and 0.786 at 5 years. We established a four-gene signature with prognostic significance in EC using WGCNA and established a nomogram to predict the prognosis of EC.