Comprehensive analysis of the lncRNAs-related immune gene signatures and their correlation with immunotherapy in lung adenocarcinoma

Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy

Background

Lung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and molecular alterations. Developing a multi-omics-based classification system for LUAD is urgently needed to advance biological understanding.

Methods

Data on clinical and pathological characteristics, genetic alterations, DNA methylation patterns, and the expression of mRNA, lncRNA, and microRNA, along with somatic mutations in LUAD patients, were gathered from the TCGA and GEO datasets. A computational workflow was utilized to merge multi-omics data from LUAD patients through 10 clustering techniques, which were paired with 10 machine learning methods to pinpoint detailed molecular subgroups and refine a prognostic risk model. The disparities in somatic mutations, copy number alterations, and immune cell infiltration between high- and low-risk groups were assessed. The effectiveness of immunotherapy in patients was evaluated through the TIDE and SubMap algorithms, supplemented by data from various immunotherapy groups. Furthermore, the Cancer Therapeutics Response Portal (CTRP) and the PRISM Repurposing dataset (PRISM) were employed to investigate new drug treatment approaches for LUAD. In the end, the role of SLC2A1 in tumor dynamics was examined using RT-PCR, immunohistochemistry, CCK-8, wound healing, and transwell tests.

Results

By employing multi-omics clustering, we discovered two unique cancer subtypes (CSs) linked to prognosis, with CS2 demonstrating a better outcome. A strong model made up of 17 genes was created using a random survival forest (RSF) method, which turned out to be an independent predictor of overall survival and showed reliable and impressive performance. The low-risk group not only had a better prognosis but also was more likely to display the "cold tumor" phenotype. On the other hand, individuals in the high-risk group showed a worse outlook and were more likely to respond positively to immunotherapy and six particular chemotherapy medications. Laboratory cell tests demonstrated that SLC2A1 is abundantly present in LUAD tissues and cells, greatly enhancing the proliferation and movement of LUAD cells.

Conclusions

Thorough examination of multi-omics data offers vital understanding and improves the molecular categorization of LUAD. Utilizing a powerful machine learning system, we highlight the immense potential of the riskscore in providing individualized risk evaluations and customized treatment suggestions for LUAD patients.

Integrative Bioinformatics Analysis and Experimental Study of NLRP12 Reveal Its Prognostic Value and Potential Functions in Ovarian Cancer

NLRP12 plays a significant role in cellular functional behavior and immune homeostasis, influencing inflammation, tumorigenesis, and prognosis. This study aimed to explore its specific effects on the tumor microenvironment (TME) and its contribution to heterogeneity in ovarian cancer (OV) through bioinformatics analysis and experimental verification. Utilizing various bioinformatics databases and clinical specimens, we investigated NLRP12 expression and its relationship with OV prognosis and immune infiltration. In vitro assays were conducted to assess the impact of NLRP12 on the proliferation and invasion of OV cells. Our findings indicate that NLRP12 is upregulated in OV, with high expression correlating with a negative prognosis. Furthermore, NLRP12 expression demonstrated a positive correlation with the infiltration of various immune cells and the expression of immune checkpoint molecules in OV. Analysis of The Cancer Immunome Atlas (TCIA) database revealed that OV patients with lower NLRP12 expression may exhibit an enhanced response to immunotherapy, particularly CTLA4 blockers, a finding validated in animal experiments. Additionally, the study emphasized the role of NLRP12 in influencing the prognosis of OV patients by promoting epithelial-mesenchymal transition (EMT) in ovarian cancer cells. Finally, we identified a potential therapeutic compound, Schisandrin B (Schi B), which decreases NLRP12 expression in ovarian cancer cells by binding to the transcription factor SPI1 associated with NLRP12. Our findings suggest that NLRP12 serves as a crucial immune-related biomarker predicting poor outcomes in OV, and targeting NLRP12 may represent a promising therapeutic approach for OV patients in the future.

Multi-omics identification of GPCR gene features in lung adenocarcinoma based on multiple machine learning combinations

Background: Lung adenocarcinoma is a common malignant tumor that ranks second in the world and has a high mortality rate. G protein-coupled receptors (GPCRs) have been reported to play an important role in cancer; however, G protein-coupled receptor-associated features have not been adequately investigated. Methods: In this study, GPCR-related genes were screened at single-cell and bulk transcriptome levels based on AUcell, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. And a new machine learning framework containing 10 machine learning algorithms and their multiple combinations was used to construct a consensus G protein-coupled receptor-related signature (GPCRRS). GPCRRS was validated in the training set and external validation set. We constructed GPCRRS-integrated nomogram clinical prognosis prediction tools. Multi-omics analyses included genomics, single-cell transcriptomics, and bulk transcriptomics to gain a more comprehensive understanding of prognostic features. We assessed the response of risk subgroups to immunotherapy and screened for personalized drugs targeting specific risk subgroups. Finally, the expression of key GPCRRS genes was verified by RT-qPCR. Results: In this study, we identified 10 GPCR-associated genes that were significantly associated with the prognosis of lung adenocarcinoma by single-cell transcriptome and bulk transcriptome. Univariate and multivariate showed that the survival rate was higher in low risk than in high risk, which also suggested that the model was an independent prognostic factor for LUAD. In addition, we observed significant differences in biological function, mutational landscape, and immune cell infiltration in the tumor microenvironment between high and low risk groups. Notably, immunotherapy was also relevant in the high and low risk groups. In addition, potential drugs targeting specific risk subgroups were identified. Conclusion: In this study, we constructed and validated a lung adenocarcinoma G protein-coupled receptor-related signature, which has an important role in predicting the prognosis of lung adenocarcinoma and the effect of immunotherapy. It is hypothesized that LDHA, GPX3 and DOCK4 are new potential targets for lung adenocarcinoma, which can achieve breakthroughs in prognosis prediction, targeted prevention and treatment of lung adenocarcinoma and provide important guidance for anti-tumor.