Multi-Omics Integrative Bioinformatics Analyses Reveal Long Non-coding RNA Modulates Genomic Integrity via Competing Endogenous RNA Mechanism and Serves as Novel Biomarkers for Overall Survival in Lung Adenocarcinoma

Exploring Novel Genome Instability-associated lncRNAs and their Potential Function in Pan-Renal Cell Carcinoma

OBJECTIVE

Genomic instability can drive clonal evolution, continuous modification of tumor genomes, and tumor genomic heterogeneity. The molecular mechanism of genomic instability still needs further investigation. This study aims to identify novel genome instabilityassociated lncRNAs (GI-lncRNAs) and investigate the role of genome instability in pan-Renal cell carcinoma (RCC).

MATERIALS AND METHODS

A mutator hypothesis was employed, combining the TCGA database of somatic mutation (SM) information, to identify GI-lncRNAs. Subsequently, a training cohort (n = 442) and a testing cohort (n = 439) were formed by randomly dividing all RCC patients. Based on the training cohort dataset, a multivariate Cox regression analysis lncRNAs risk model was created. Further validations were performed in the testing cohort, TCGA cohort, and different RCC subtypes. To confirm the relative expression levels of lncRNAs in HK-2, 786-O, and 769-P cells, qPCR was carried out. Functional pathway enrichment analyses were performed for further investigation.

RESULTS

A total of 170 novel GI-lncRNAs were identified. The lncRNA prognostic risk model was constructed based on LINC00460, AC073218.1, AC010789.1, and COLCA1. This risk model successfully differentiated patients into distinct risk groups with significantly different clinical outcomes. The model was further validated in multiple independent patient cohorts. Additionally, functional and pathway enrichment analyses revealed that GI-lncRNAs play a crucial role in GI. Furthermore, the assessments of immune response, drug sensitivity, and cancer stemness revealed a significant relationship between GI-lncRNAs and tumor microenvironment infiltration, mutational burden, microsatellite instability, and drug resistance.

CONCLUSIONS

In this study, we discovered four novel GI-lncRNAs and developed a novel signature that effectively predicted clinical outcomes in pan-RCC. The findings provide valuable insights for pan-RCC immunotherapy and shed light on potential underlying mechanisms.

Significance of macrophage infiltration in the prognosis of lung adenocarcinoma patients evaluated by scRNA and bulkRNA analysis

Purpose

To investigate the significance of macrophage infiltration to the prognosis of lung adenocarcinoma.

Methods

R language bioinformatics analysis technology, was used to obtain macrophage infiltration-related module genes through WGCNA (Weighted Gene Co-Expression Network Analysis). Marker genes of macrophage subtypes were identified using single-cell sequencing of lung adenocarcinoma tissue. Risk score models were constructed and validated using external data cohorts and clinical samples.

Results

Analysis of cohorts TCGA-LUAD, GSE11969, GSE31210, GSE50081, GSE72094 and GSE8894, revealed a negative correlation between macrophage infiltration and survival. Immunohistochemical analyses of clinical samples were consistent with these data. Based on cell-cluster-markers and TAMs-related-genes, TOP8 genes were obtained (C1QTNF6, CCNB1, FSCN1, HMMR, KPNA2, PRC1, RRM2, and TK1) with a significant association to prognosis. Risk score models including 9 factors (C1QTNF6, FSCN1, KPNA2, GLI2, TYMS, BIRC3, RBBP7, KRT8, GPR65) for prognosis were constructed. The efficacy, stability and generalizability of the risk score models were validated using multiple data cohorts (GSE19188, GSE26939, GSE31210, GSE50081, GSE42127, and GSE72094).

Conclusions

Macrophage infiltration negatively correlates with prognosis in patients with lung adenocarcinoma. Based on cell-cluster-markers and TAMs-related-genes, both TOP8 genes (C1QTNF6, CCNB1, FSCN1, HMMR, KPNA2, PRC1, RRM2, TK1) and risk score models using C1QTNF6, FSCN1, KPNA2, GLI2, TYMS, BIRC3, RBBP7, KRT8, GPR65 could predict disease prognosis.

Analysis of the lncRNA–miRNA–mRNA Network Reveals a Potential Regulatory Mechanism of EGFR-TKI Resistance in NSCLC

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are widely used for patients with EGFR-mutated lung cancer. Despite its initial therapeutic efficacy, most patients eventually develop drug resistance, which leads to a poor prognosis in lung cancer patients. Previous investigations have proved that non-coding RNAs including long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and microRNAs (miRNAs) contribute to drug resistance by various biological functions, whereas how they regulate EGFR-TKI resistance remains unclear. In this study, we examined gene expression using the microarray technology on gefitinib-resistant NSCLC cells to obtain differentially expressed (DE) lncRNAs and mRNAs. A total of 45 DE-lncRNAs associated with overall survival and 1799 target DE-mRNAs were employed to construct a core lncRNA–miRNA–mRNA network to illustrate underlying molecular mechanisms of how EGFR-TKI resistance occurs in NSCLC. We found that target DE-mRNAs were mainly enriched in pathways involved in EGFR-TKI resistance, especially the target DE-mRNAs regulated by LINC01128 were significantly enriched in the PI3K/Akt signaling pathway, where the synergy of these target DE-mRNAs may play a key role in EGFR-TKI resistance. In addition, downregulated LINC01128, acting as a specific miRNA sponge, decreases PTEN via sponging miR-25-3p. Furthermore, signaling reactions caused by the downregulation of PTEN would activate the PI3K/Akt signaling pathway, which may lead to EGFR-TKI resistance. In addition, a survival analysis indicated the low expression of LINC01128, and PTEN is closely related to poor prognosis in lung adenocarcinoma (LUAD). Therefore, the LINC01128/miR-25-3p/PTEN axis may promote EGFR-TKI resistance via the PI3K/Akt signaling pathway, which provides new insights into the underlying molecular mechanisms of drug resistance to EGFR-TKIs in NSCLC. In addition, our study sheds light on developing novel therapeutic approaches to overcome EGFR-TKI resistance in NSCLC.

New Insights into LINC00346 and its Role in Disease

Accumulating evidence has shown that long intergenic non-protein-coding RNA 346 (LINC00346) functions as an oncogene in the tumorigenesis of several cancers. The expression level of LINC00346 has been shown to be obviously correlated with prognosis, lymphoma metastasis, histological grade, TNM stage, tumor size and pathologic stage. LINC00346 has been found to regulate specific cellular functions by interacting with several molecules and signaling pathways. In this review, we summarize recent evidence concerning the role of LINC00346 in the occurrence and development of diseases. We also discuss the potential clinical utility of LINC00346, thereby providing new insight into the diagnosis and treatment of diseases. In addition, we further discuss the potential clinical utility of LINC00346 in the diagnosis, prognostication, and treatment of diseases.

Comprehensive Analysis to Identify Key Genes Involved in Advanced Atherosclerosis

Background

The study was aimed at finding accurate and effective therapeutic targets and deepening our understanding of the mechanisms of advanced atherosclerosis (AA).

Methods

We downloaded the gene expression datasets GSE28829, GSE120521, and GSE43292 from Gene Expression Omnibus. Weighted gene coexpression network analysis (WGCNA) was performed for GSE28829, and functional enrichment analysis and protein–protein interaction network analysis were conducted on the key module. Significant genes in the key module were analyzed by molecular complex detection, and genes in the most important subnetwork were defined as hub genes. Multiple dataset analyses for hub genes were conducted. Genes that overlapped between hub genes and differentially expressed genes (DEGs) of GSE28829 and GSE120521 were defined as key genes. Further validation for key genes was performed using GSE28829 and GSE43292. Gene set enrichment analysis (GSEA) was applied to key genes.

Results

A total of 77 significant genes in the key module of GSE28829 were screened out that were mainly associated with inflammation and immunity. The subnetwork was obtained from significant genes, and 18 genes in this module were defined as hub genes, which were related to immunity and expressed in multiple diseases, particularly systemic lupus erythematosus. Some hub genes were regulated by SPI1 and associated with the blood, spleen, and lung. After overlapping with DEGs of GSE28829 and GSE120521, a total of 10 genes (HCK, ITGAM, CTSS, TYROBP, LAPTM5, FCER1G, ITGB2, NCF2, AIF1, and CD86) were identified as key genes. All key genes were validated and evaluated successfully and were related to immune response pathways.

Conclusion

Our study suggests that the key genes related to immune and inflammatory responses are involved in the development of AA. This may deepen our understanding of the mechanisms of and provide valuable therapeutic targets for AA.