Project description:Accumulation of genetic and epigenetic changes alters regulation of a web of interconnected genes including miRNAs, which confer hallmark capabilities and characteristic cancer features. In this study, the miRNA and mRNA expression profiles of 126 non-small cell lung cancer specimens were analyzed, with special attention given to the diversity of lung adenocarcinomas. Of those, 76 adenocarcinomas were classified into two major subtypes, developing lung-like and adult lung-like, based on their distinctive miRNA expression profiles resembling those of either developing or adult lungs, respectively. A systems biology-based approach using a Bayesian network and nonparametric regression was employed to estimate the gene regulatory circuitry functioning in patient tumors in order to identify subnetworks enriched for genes with differential expression between the two major subtypes. miR-30d and miR-195, identified as hub genes in such subnetworks, had lower levels of expression in the developing lung-like subtype, while introduction of miR-30d or miR-195 into the lung cancer cell lines evoked shifts of mRNA expression profiles towards the adult lung-like subtype. Conversely, the influence of miR-30d and miR-195 was significantly different between the developing lung- and adult lung-like subtypes in our analysis of the patient dataset. In addition, RRM2, a child gene of the miR-30d-centered subnetwork, was found to be a direct target of miR-30d. Together, our findings reveal the existence of two miRNA expression profile-defined lung adenocarcinoma subtypes with distinctive clinicopathologic features and also suggest the usefulness of a systems biology-based approach to gain insight into the altered regulatory circuitry involved in cancer development. Microarray analysis using a Whole Human Genome 4 x 44K Microarray G4112F (Agilent) was conducted to examine changes in expression of 400 genes in SiGN network by transfection of Pre-miR-30d, Pre-miR-195 or Pre-miR-NC#2 (Ambion) in SK-LC-7 cells, which were then harvested at 72 hours after transfection.

Project description: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.

Project description:Accumulation of genetic and epigenetic changes alters regulation of a web of interconnected genes including miRNAs, which confer hallmark capabilities and characteristic cancer features. In this study, the miRNA and mRNA expression profiles of 126 non-small cell lung cancer specimens were analyzed, with special attention given to the diversity of lung adenocarcinomas. Of those, 76 adenocarcinomas were classified into two major subtypes, developing lung-like and adult lung-like, based on their distinctive miRNA expression profiles resembling those of either developing or adult lungs, respectively. A systems biology-based approach using a Bayesian network and nonparametric regression was employed to estimate the gene regulatory circuitry functioning in patient tumors in order to identify subnetworks enriched for genes with differential expression between the two major subtypes. miR-30d and miR-195, identified as hub genes in such subnetworks, had lower levels of expression in the developing lung-like subtype, while introduction of miR-30d or miR-195 into the lung cancer cell lines evoked shifts of mRNA expression profiles towards the adult lung-like subtype. Conversely, the influence of miR-30d and miR-195 was significantly different between the developing lung- and adult lung-like subtypes in our analysis of the patient dataset. In addition, RRM2, a child gene of the miR-30d-centered subnetwork, was found to be a direct target of miR-30d. Together, our findings reveal the existence of two miRNA expression profile-defined lung adenocarcinoma subtypes with distinctive clinicopathologic features and also suggest the usefulness of a systems biology-based approach to gain insight into the altered regulatory circuitry involved in cancer development. Microarray analysis was conducted to examine miRNA expression profiles using a Human miRNA Microarray, pre-commercial version 6.0 (Agilent) with 470 miRNA probes, according to the manufacturer’s instructions

Project description:Accumulation of genetic and epigenetic changes alters regulation of a web of interconnected genes including miRNAs, which confer hallmark capabilities and characteristic cancer features. In this study, the miRNA and mRNA expression profiles of 126 non-small cell lung cancer specimens were analyzed, with special attention given to the diversity of lung adenocarcinomas. Of those, 76 adenocarcinomas were classified into two major subtypes, developing lung-like and adult lung-like, based on their distinctive miRNA expression profiles resembling those of either developing or adult lungs, respectively. A systems biology-based approach using a Bayesian network and nonparametric regression was employed to estimate the gene regulatory circuitry functioning in patient tumors in order to identify subnetworks enriched for genes with differential expression between the two major subtypes. miR-30d and miR-195, identified as hub genes in such subnetworks, had lower levels of expression in the developing lung-like subtype, while introduction of miR-30d or miR-195 into the lung cancer cell lines evoked shifts of mRNA expression profiles towards the adult lung-like subtype. Conversely, the influence of miR-30d and miR-195 was significantly different between the developing lung- and adult lung-like subtypes in our analysis of the patient dataset. In addition, RRM2, a child gene of the miR-30d-centered subnetwork, was found to be a direct target of miR-30d. Together, our findings reveal the existence of two miRNA expression profile-defined lung adenocarcinoma subtypes with distinctive clinicopathologic features and also suggest the usefulness of a systems biology-based approach to gain insight into the altered regulatory circuitry involved in cancer development. Microarray analysis using a Whole Human Genome 4 x 44K Microarray G4112F (Agilent) was conducted to examine changes in expression of 400 genes in SiGN network by transfection of Pre-miR-30d, Pre-miR-195 or Pre-miR-NC#2 (Ambion) in SK-LC-7 cells, which were then harvested at 72 hours after transfection.

Dataset Information

Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma.

Publications

Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma.

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