A Novel Signature of Lipid Metabolism-Related Gene Predicts Prognosis and Response to Immunotherapy in Lung Adenocarcinoma

Multi-omics analysis reveals epigenetically regulated processes and patient classification in lung adenocarcinoma

Aberrant DNA methylation is a hallmark of many cancer types. Despite our knowledge of epigenetic and transcriptomic alterations in lung adenocarcinoma (LUAD), we lack robust multi-modal molecular classifications for patient stratification. This is partly because the impact of epigenetic alterations on lung cancer development and progression is still not fully understood. To that end, we identified disease-associated processes under epigenetic regulation in LUAD. We performed a genome-wide expression-methylation Quantitative Trait Loci (emQTL) analysis by integrating DNA methylation and gene expression data from 453 patients in the TCGA cohort. Using a community detection algorithm, we identified distinct communities of CpG-gene associations with diverse biological processes. Interestingly, we identified a community linked to hormone response and lipid metabolism; the identified CpGs in this community were enriched in enhancer regions and binding regions of transcription factors such as FOXA1/2, GRHL2, HNF1B, AR, and ESR1. Furthermore, the CpGs were connected to their associated genes through chromatin interaction loops. These findings suggest that the expression of genes involved in hormone response and lipid metabolism in LUAD is epigenetically regulated through DNA methylation and enhancer-promoter interactions. By applying consensus clustering on the integrated expression-methylation pattern of the emQTL-genes and CpGs linked to hormone response and lipid metabolism, we further identified subclasses of patients with distinct prognoses. This novel patient stratification was validated in an independent patient cohort of 135 patients and showed increased prognostic significance compared to previously defined molecular subtypes.

Exploring the mechanism of 6-Methoxydihydrosanguinarine in the treatment of lung adenocarcinoma based on network pharmacology, molecular docking and experimental investigation

BACKGROUND

6-Methoxydihydrosanguinarine (6-MDS) has shown promising potential in fighting against a variety of malignancies. Yet, its anti‑lung adenocarcinoma (LUAD) effect and the underlying mechanism remain largely unexplored. This study sought to explore the targets and the probable mechanism of 6-MDS in LUAD through network pharmacology and experimental validation.

METHODS

The proliferative activity of human LUAD cell line A549 was evaluated by Cell Counting Kit-8 (CCK8) assay. LUAD related targets, potential targets of 6-MDS were obtained from databases. Venn plot analysis were performed on 6-MDS target genes and LUAD related genes to obtain potential target genes for 6-MDS treatment of LUAD. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was utilized to perform a protein-protein interaction (PPI) analysis, which was then visualized by Cytoscape. The hub genes in the network were singled out by CytoHubba. Metascape was employed for GO and KEGG enrichment analyses. molecular docking was carried out using AutoDock Vina 4.2 software. Gene expression levels, overall survival of hub genes were validated by the GEPIA database. Protein expression levels, promotor methylation levels of hub genes were confirmed by the UALCAN database. Timer database was used for evaluating the association between the expression of hub genes and the abundance of infiltrating immune cells. Furthermore, correlation analysis of hub genes expression with immune subtypes of LUAD were performed by using the TISIDB database. Finally, the results of network pharmacology analysis were validated by qPCR.

RESULTS

Experiments in vitro revealed that 6-MDS significantly reduced tumor growth. A total of 33 potential targets of 6-MDS in LUAD were obtained by crossing the LUAD related targets with 6-MDS targets. Utilizing CytoHubba, a network analysis tool, the top 10 genes with the highest centrality measures were pinpointed, including MMP9, CDK1, TYMS, CCNA2, ERBB2, CHEK1, KIF11, AURKB, PLK1 and TTK. Analysis of KEGG enrichment hinted that these 10 hub genes were located in the cell cycle signaling pathway, suggesting that 6-MDS may mainly inhibit the occurrence of LUAD by affecting the cell cycle. Molecular docking analysis revealed that the binding energies between 6-MDS and the hub proteins were all higher than - 6 kcal/Mol with the exception of AURKB, indicating that the 9 targets had strong binding ability with 6-MDS.These results were corroborated through assessments of mRNA expression levels, protein expression levels, overall survival analysis, promotor methylation level, immune subtypes andimmune infiltration. Furthermore, qPCR results indicated that 6-MDS can significantly decreased the mRNA levels of CDK1, CHEK1, KIF11, PLK1 and TTK.

CONCLUSIONS

According to our findings, it appears that 6-MDS could possibly serve as a promising option for the treatment of LUAD. Further investigations in live animal models are necessary to confirm its potential in fighting cancer and to delve into the mechanisms at play.

Lipid metabolism and tumor immunotherapy

In recent years, the relationship between lipid metabolism and tumour immunotherapy has been thoroughly investigated. An increasing number of studies have shown that abnormal gene expression and ectopic levels of metabolites related to fatty acid synthesis or fatty acid oxidation affect tumour metastasis, recurrence, and drug resistance. Tumour immunotherapy that aims to promote an antitumour immune response has greatly improved the outcomes for tumour patients. However, lipid metabolism reprogramming in tumour cells or tumour microenvironment-infiltrating immune cells can influence the antitumour response of immune cells and induce tumor cell immune evasion. The recent increase in the prevalence of obesity-related cancers has drawn attention to the fact that obesity increases fatty acid oxidation in cancer cells and suppresses the activation of immune cells, thereby weakening antitumour immunity. This article reviews the changes in lipid metabolism in cells in the tumour microenvironment and describes the relationship between lipid metabolism reprogramming in multiple cell types and tumour immunotherapy.

Lipid metabolism-related gene signature predicts prognosis and depicts tumor microenvironment immune landscape in gliomas

Background

Glioma is the most common primary brain tumor in adults and accounts for more than 70% of brain malignancies. Lipids are crucial components of biological membranes and other structures in cells. Accumulating evidence has supported the role of lipid metabolism in reshaping the tumor immune microenvironment (TME). However, the relationship between the immune TME of glioma and lipid metabolism remain poorly described.

Materials and methods

The RNA-seq data and clinicopathological information of primary glioma patients were downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). An independent RNA-seq dataset from the West China Hospital (WCH) also included in the study. Univariate Cox regression and LASSO Cox regression model was first to determine the prognostic gene signature from lipid metabolism-related genes (LMRGs). Then a risk score named LMRGs-related risk score (LRS) was established and patients were stratified into high and low risk groups according to LRS. The prognostic value of the LRS was further demonstrated by construction of a glioma risk nomogram. ESTIMATE and CIBERSORTx were used to depicted the TME immune landscape. Tumor Immune Dysfunction and Exclusion (TIDE) was utilized to predict the therapeutic response of immune checkpoint blockades (ICB) among glioma patients.

Results

A total of 144 LMRGs were differentially expressed between gliomas and brain tissue. Finally, 11 prognostic LMRGs were included in the construction of LRS. The LRS was demonstrated to be an independent prognostic predictor for glioma patients, and a nomogram consisting of the LRS, IDH mutational status, WHO grade, and radiotherapy showed a C-index of 0.852. LRS values were significantly associated with stromal score, immune score, and ESTIMATE score. CIBERSORTx indicated remarkable differences in the abundance of TME immune cells between patients with high and low LRS risk levels. Based on the results of TIDE algorithm, we speculated that the high-risk group had a greater chance of benefiting from immunotherapy.

Conclusion

The risk model based upon LMRGs could effectively predict prognosis in patients with glioma. Risk score also divided glioma patients into different groups with distinct TME immune characteristics. Immunotherapy is potentially beneficial to glioma patients with certain lipid metabolism profiles.

Development and Clinical Validation of a Novel 5 Gene Signature Based on Fatty Acid Metabolism-Related Genes in Oral Squamous Cell Carcinoma

BACKGROUND/AIM

Lipid metabolism disorders play a crucial role in tumor development and progression. The aim of the study focused on constructing a novel prognostic model of oral squamous cell carcinoma (OSCC) patients using fatty acid metabolism-related genes.

METHODS

Microarray test and data from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed genes related to fatty acid metabolism. The quantitative real-time polymerase chain reaction (qRT-PCR) was then used to validate the expression of targeted fatty acid metabolism genes. A risk predictive scoring model of fatty acid metabolism-related genes was generated using a multivariate Cox model. The efficacy of this model was assessed by time-dependent receiver operating characteristic curve (ROC).

RESULTS

14 fatty acid metabolism-related genes were identified by microarray test and TCGA database analysis and then confirmed by PCR. Finally, a 5 gene signature (ACACB, FABP3, PDK4, PPARG, and PLIN5) was constructed and a RiskScore was calculated for each patient. Compared to the high RiskScore group, the low RiskScore group had better overall survival (OS) (p = 0.02). The RiskScore derived from a 5 gene signature was a prognostic factor (HR: 3.73, 95% CI: 1.38, 10.09) for OSCC patients. The predictive classification efficiencies of RiskScore were evaluated and the area under the curve (AUC) values for 1, 3, and 5 years were 0.613, 0.652, and 0.681, respectively. Then we compared the predictive performance of the prognostic model with or without the RiskScore. The 5 gene-derived RiskScore can improve the predictive performance with AUC values of 0.760, 0.803, and 0.830 for 1, 3, and 5 years OS in prognostic model including the RiskScore. While the predicted AUC values of the model without RiskScore for 1, 3, and 5 years OS were 0.699, 0.715, and 0.714, respectively.

CONCLUSION

We developed a predictive score model using 5 fatty acid metabolism-related genes, which could be a potential prognostic indicator in OSCC.

Identification of Immune-Related Subtypes and Construction of a Novel Prognostic Model for Bladder Urothelial Cancer

The purpose of this study was to explore the relationship between bladder urothelial cancer (BLCA) and immunity, to screen prognosis-related immune genes (PIGs), and to construct an immune-related prognosis model (IRPM). We processed the relevant data of The Cancer Genome Atlas (TCGA-BLCA) and GSE13507 using R software and Perl. We divided BLCA into high-immunity and low-immunity subtypes. There were significant differences in the two subtypes. In addition, we identified 13 PIGs of BLCA by jointly analyzing the gene expression data and survival information of GSE13507 and TCGA-BLCA, and constructed IRPM through nine of them. The low-risk group had better survival outcome than the high-risk group. We also constructed a nomogram based on clinicopathological information and risk scores of the patients. Moreover, the prognosis of BLCA patients was significantly impacted by the expression of almost every gene used to calculate the risk score. The result of real-time fluorescence quantitative polymerase chain reaction revealed that all the genes used to calculate the risk score were differentially expressed between BLCA and adjacent normal tissues, except PDGFRA. Our research provided potential targets for the treatment of BLCA and a reference for judging the prognosis of BLCA.

A panel of seven immune-related genes can serve as a good predictive biomarker for cervical squamous cell carcinoma

Objective: Cervical cancer is one of the most common gynecological malignancies. The interaction between tumor microenvironment and immune infiltration is closely related to the progression of cervical squamous cell carcinoma (CSCC) and patients' prognosis. Herein, a panel of immune-related genes was established for more accurate prognostic prediction. Methods: The transcriptome information of tumor and normal samples were obtained from TCGA-CSCC and GTEx. Differentially expressed genes (DEGs) were defined from it. Immune-related genes (IRGs) were retrieved from the ImmPort database. After removing the transcriptome data which not mentioned in GSE44001, IR-DEGs were preliminarily identified. Then, TCGA-CSCC samples were divided into training and testing set (3:1) randomly. Univariate Cox analysis, LASSO regression analysis and multivariate Cox analysis were used in turn to construct the signature to predict the overall survival (OS) and disease-free survival (DFS). External validation was performed in GSE44001, and initial clinical validation was performed by qRT-PCR. Function enrichment analysis, immune infiltration analysis and establishment of nomogram were conducted as well. Results: A prognostic prediction signature consisting of seven IR-DEGs was established. High expression of NRP1, IGF2R, SERPINA3, TNF and low expression of ICOS, DES, HCK suggested that CSCC patients had shorter OS (POS<0.001) and DFS (PDFS<0.001). AUC values of 1-, 3-, five- year OS were 0.800, 0.831 and 0.809. Analyses in other validation sets showed good consistency with the results in training set. The signature can serve as an independent prognostic factor for OS (HR = 1.166, p < 0.001). AUC values of 1-, 3-, five- year OS based on the nomogram were 0.769, 0.820 and 0.807. Functional enrichment analysis suggested that these IR-DEGs were associated with receptor interaction and immune cell activity. Immune infiltration analysis indicated that patients in high-risk group had lower immune infiltration, weaker immune function, and were more likely to benefit from immune checkpoint inhibitor therapy. Through qRT-PCR on clinical samples, expression of NRP1, IGF2R, SERPINA3 and TNF were significantly upregulated in tumor tissue, while ICOS and DES were significantly downregulated. Conclusion: To conclude, the immune-related signature can provide strong support for exploration of immune infiltration, prediction of prognosis and response to immunotherapy through stratify CSCC patients into subgroups.

The role of lipid metabolism in tumor immune microenvironment and potential therapeutic strategies

Aberrant lipid metabolism is nonnegligible for tumor cells to adapt to the tumor microenvironment (TME). It plays a significant role in the amount and function of immune cells, including tumor-associated macrophages, T cells, dendritic cells and marrow-derived suppressor cells. It is well-known that the immune response in TME is suppressed and lipid metabolism is closely involved in this process. Immunotherapy, containing anti-PD1/PDL1 therapy and adoptive T cell therapy, is a crucial clinical cancer therapeutic strategy nowadays, but they display a low-sensibility in certain cancers. In this review, we mainly discussed the importance of lipid metabolism in the formation of immunosuppressive TME, and explored the effectiveness and sensitivity of immunotherapy treatment by regulating the lipid metabolism.