NcRNA-Mediated High Expression of HMMR as a Prognostic Biomarker Correlated With Cell Proliferation and Cell Migration in Lung Adenocarcinoma

Identification and analysis of prognostic immune cell homeostasis characteristics in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is one of the most invasive malignant tumor of the respiratory system. It is also the common pathological type leading to the death of LUAD. Maintaining the homeostasis of immune cells is an important way for anti-tumor immunotherapy. However, the biological significance of maintaining immune homeostasis and immune therapeutic effect has not been well studied.

METHODS

We constructed a diagnostic and prognostic model for LUAD based on B and T cells homeostasis-related genes. Minimum absolute contraction and selection operator (LASSO) analysis and multivariate Cox regression are used to identify the prognostic gene signatures. Based on the overall survival time and survival status of LUAD patients, a 10-gene prognostic model composed of ABL1, BAK1, IKBKB, PPP2R3C, CCNB2, CORO1A, FADD, P2RX7, TNFSF14, and ZC3H8 was subsequently identified as prognostic markers from The Cancer Genome Atlas (TCGA)-LUAD to develop a prognostic signature. This study constructed a gene prognosis model based on gene expression profiles and corresponding survival information through survival analysis, as well as 1-year, 3-year, and 5-year ROC curve analysis. Enrichment analysis attempted to reveal the potential mechanism of action and molecular pathway of prognostic genes. The CIBERSORT algorithm calculated the infiltration degree of 22 immune cells in each sample and compared the difference of immune cell infiltration between high-risk group and low-risk group. At the cellular level, PCR and CKK8 experiments were used to verify the differences in the expression of the constructed 10-gene model and its effects on cell viability, respectively. The experimental results supported the significant biological significance and potential application value of the molecular model in the prognosis of lung cancer. Enrichment analyses showed that these genes were mainly related to lymphocyte homeostasis.

CONCLUSION

We identified a novel immune cell homeostasis prognostic signature. Targeting these immune cell homeostasis prognostic genes may be an alternative for LUAD treatment. The reliability of the prediction model was confirmed at bioinformatics level, cellular level, and gene level.

MicroRNA (let-7b-5p)-targeted DARS2 regulates lung adenocarcinoma growth by PI3K/AKT signaling pathway

Background

The aberrant intracellular expression of a mitochondrial aspartyl-tRNA synthetase 2 (DARS2) has been reported in human cancers. Nevertheless, its critical role and detailed mechanism in lung adenocarcinoma (LUAD) remain unexplored.

Methods

Initially, The Cancer Genome Atlas (TCGA)-based Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/) was used to analyze the prognostic relevance of DARS2 expression in LUAD. Further, cell counting kit (CCK)-8, immunostaining, and transwell invasion assays in LUAD cell lines in vitro, as well as DARS2 silence on LUAD by tumorigenicity experiments in vivo in nude mice, were performed. Besides, we analyzed the expression levels of p-PI3K (phosphorylated-Phosphotylinosital3 kinase), PI3K, AKT (Protein Kinase B), p-AKT (phosphorylated-Protein Kinase B), PCNA (proliferating cell nuclear antigen), cleaved-caspase 3, E-cadherin, and N-cadherin proteins using the Western blot analysis.

Results

LUAD tissues showed higher DARS2 expression compared to normal tissues. Upregulation of DARS2 could be related to Tumor-Node-Metastasis (TNM) stage, high lymph node metastasis, and inferior prognosis. DARS2 silence decreased the proliferation, migration, and invasion abilities of LUAD cells. In addition, the DARS2 downregulation decreased the PCNA and N-cadherin expression and increased cleaved-caspase 3 and E-cadherin expressions in LUAD cells, coupled with the inactivation of the PI3K/AKT signaling pathway. Moreover, DARS2 silence impaired the tumorigenicity of LUAD in vivo. Interestingly, let-7b-5p could recognize DARS2 through a complementary sequence. Mechanistically, the increased let-7b-5p expression attenuated the promo-oncogenic action of DARS2 during LUAD progression, which were inversely correlated to each other in the LUAD tissues.

Conclusion

In summary, let-7b-5p downregulated DARS2 expression, regulating the progression of LUAD cells by the PI3K/AKT signaling pathway.

Exploring the interplay between methylation patterns and non-coding RNAs in non-small cell lung cancer: Implications for pathogenesis and therapeutic targets

Lung cancer is a global public health issue, with non-small cell lung cancer (NSCLC) accounting for 80-85 % of cases. With over two million new diagnoses annually, understanding the complex evolution of this disease is crucial. The development of lung cancer involves a complex interplay of genetic, epigenetic, and environmental factors, leading the key oncogenes and tumor suppressor genes to disorder, and activating the cancer related signaling pathway. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNA (lncRNAs), and circular RNA (circRNAs) are unique RNA transcripts with diverse biological functions. These ncRNAs are generated through genome transcription and play essential roles in cellular processes. Epigenetic modifications such as DNA methylation, N6-methyladenosine (m6A) modification, and histone methylation have gained significant attention in NSCLC research. The complexity of the interactions among these methylation modifications and ncRNAs contribute to the precise regulation of NSCLC development. This review comprehensively summarizes the associations between ncRNAs and different methylation modifications and discusses their effects on NSCLC. By elucidating these relationships, we aim to advance our understanding of NSCLC pathogenesis and identify potential therapeutic targets for this devastating disease.

Identification of fatty acid-related subtypes, the establishment of a prognostic signature, and immune infiltration characteristics in lung adenocarcinoma

Abnormal fatty acid (FA) metabolism can change the inflammatory microenvironment and promote tumor progression and metastasis, however, the potential association between FA-related genes (FARGs) and lung adenocarcinoma (LUAD) is still unclear. In this study, we described the genetic and transcriptomic changes of FARGs in LUAD patients and identified two different FA subtypes, which were significantly correlated with overall survival and tumor microenvironment infiltrating cells in LUAD patients. In addition, the FA score was also constructed through the LASSO Cox to evaluate the FA dysfunction of each patient. Multivariate Cox analysis proved that the FA score was an independent predictor and created the FA score integrated nomogram, which offered a quantitative tool for clinical practice. The performance of the FA score has been substantiated in numerous datasets for its commendable accuracy in estimating overall survival in LUAD patients. The groups with high and low FA scores exhibited different mutation spectrums, copy number variations, enrichment pathways, and immune status. Noteworthy differences between the two groups in terms of immunophenoscore and Tumor Immune Dysfunction and Exclusion were observed, suggesting that the group with a low FA score was more responsive to immunotherapy, and similar results were also confirmed in the immunotherapy cohort. In addition, seven potential chemotherapeutic drugs related to FA score targeting were predicted. Ultimately, we ascertained that the attenuation of KRT6A expression impeded the proliferation, migration, and invasion of LUAD cell lines. In summary, this research offers novel biomarkers to facilitate prognostic forecasting and clinical supervision for individuals afflicted with LUAD.

Guidelines on lung adenocarcinoma prognosis based on immuno-glycolysis-related genes

OBJECTIVES

This study developed a new model for risk assessment of immuno-glycolysis-related genes for lung adenocarcinoma (LUAD) patients to predict prognosis and immunotherapy efficacy.

METHODS

LUAD samples and data obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases are used as training and test columns, respectively. Twenty-two (22) immuno-glycolysis-related genes were screened, the patients diagnosed with LUAD were divided into two molecular subtypes by consensus clustering of these genes. The initial prognosis model was developed using the multiple regression analysis method and Receiver Operating characteristic (ROC) analysis was used to verify its predictive potential. Gene set enrichment analysis (GSEA) showed the immune activities and pathways in different risk populations, we calculated immune checkpoints, immune escape, immune phenomena (IPS), and tumor mutation burden (TMB) based on TCGA datasets. Finally, the relationship between the model and drug sensitivity was analyzed.

RESULTS

Fifteen (15) key differentially expressed genes (DEGs) with prognostic value were screened and a new prognostic model was constructed. Four hundred and forty-three (443) samples were grouped into two different risk cohorts based on median model risk values. It was observed that survival rates in high-risk groups were significantly low. ROC curves were used to evaluate the model's accuracy in determining the survival time and clinical outcome of LUAD patients. Cox analysis of various clinical factors proved that the risk score has great potential as an independent prognostic factor. The results of immunological analysis can reveal the immune infiltration and the activity of related functions in different pathways in the two risk groups, and immunotherapy was more effective in low-risk patients. Most chemotherapeutic agents are more sensitive to low-risk patients, making them more likely to benefit.

CONCLUSION

A novel prognostic model for LUAD patients was established based on IGRG, which could more accurately predict the prognosis and an effective immunotherapy approach for patients.

Identification of cancer risk assessment signature in patients with chronic obstructive pulmonary disease and exploration of the potential key genes

It is essential to assess the cancer risk for patients with chronic obstructive pulmonary disease (COPD). Comparing gene expression data from patients with lung cancer (a total of 506 samples) and those with cancer-adjacent normal lung tissues (a total of 370 samples), we generated a qualitative transcriptional signature consisting of 2046 gene pairs. The signature was verified in an evaluation dataset comprising 18 subjects with severe disease and 52 subjects with moderate disease (Wilcoxon rank-sum test; p = 7.33 × 10^-5). Similar results were obtained in other independent datasets. Among the gene pairs in the signature, 326 COPD stage-related gene pairs were identified based on Spearman's rank correlation tests and those gene pairs comprised 368 unique genes. Of these 368 genes, 16 genes were significantly dysregulated in COPD rat model data compared with control data. Some of these genes (Dhx16, Upf2, Notch3, Sec61a1, Dyrk2, and Hmmr) were altered when the COPD rat model was treated with traditional Chinese medicines (TCM), including Bufei Yishen formula, Bufei Jianpi formula, and Yiqi Zishen formula. Overall, the signature could predict the cancer incidence-risk of COPD and the identified key genes might provide guidance regarding both the treatment of COPD using TCM and the prevention of cancer in patients with COPD. KEY MESSAGESA cancer risk assessment signature was identified in patients with COPD.The signature is insensitive to batch effects and is well verified.COPD key genes identified in this study might play a crucial role in TCM treatment and cancer prevention.

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.