BackgroundHepatocellular carcinoma (HCC) is a complex disease with a poor outlook for patients in advanced stages. Immune cells play an important role in the progression of HCC. The metabolism of sphingolipids functions in both tumor growth and immune infiltration. However, little research has focused on using sphingolipid factors to predict HCC prognosis. This study aimed to identify the key sphingolipids genes (SPGs) in HCC and develop a reliable prognostic model based on these genes.MethodsThe TCGA, GEO, and ICGC datasets were grouped using SPGs obtained from the InnateDB portal. A prognostic gene signature was created by applying LASSO-Cox analysis and evaluating it with Cox regression. The validity of the signature was verified using ICGC and GEO datasets. The tumor microenvironment (TME) was examined using ESTIMATE and CIBERSORT, and potential therapeutic targets were identified through machine learning. Single-cell sequencing was used to examine the distribution of signature genes in cells within the TME. Cell viability and migration were tested to confirm the role of the key SPGs.ResultsWe identified 28 SPGs that have an impact on survival. Using clinicopathological features and 6 genes, we developed a nomogram for HCC. The high- and low-risk groups were found to have distinct immune characteristics and response to drugs. Unlike CD8 T cells, M0 and M2 macrophages were found to be highly infiltrated in the TME of the high-risk subgroup. High levels of SPGs were found to be a good indicator of response to immunotherapy. In cell function experiments, SMPD2 and CSTA were found to enhance survival and migration of Huh7 cells, while silencing these genes increased the sensitivity of Huh7 cells to lapatinib.ConclusionThe study presents a six-gene signature and a nomogram that can aid clinicians in choosing personalized treatments for HCC patients. Furthermore, it uncovers the connection between sphingolipid-related genes and the immune microenvironment, offering a novel approach for immunotherapy. By focusing on crucial sphingolipid genes like SMPD2 and CSTA, the efficacy of anti-tumor therapy can be increased in HCC cells.
Background Associations of High-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, total cholesterol (CHL), and triglyceride (TRG) concentrations with risk of biliary tract cancer (BtC) were conflicting in observational studies. We aim to investigate the causal link between circulating lipids and BtC using genetic information. Methods Single nucleotide polymorphisms of the four circulating lipids (n = 34,421) and BtC (418 cases and 159,201 controls) were retrieved from two independent GWAS studies performed in East Asian populations. Two-sample univariate and multivariate Mendelian Randomization (MR) analyses were conducted to determine the causal link between circulating lipids and BtC. Results No significant horizontal pleiotropy was detected for all circulating lipids according to the MR-PRESSO global test (P = 0.458, 0.368, 0.522, and 0.587 for HDL, LDL, CHL, and TRG, respectively). No significant evidence of heterogeneity and directional pleiotropy was detected by the Cochran’s Q test and MR-Egger regression. Univariate MR estimates from inverse variance weighting method suggested that one standard deviation (1-SD) increase of inverse-normal transformed HDL (OR = 1.38, 95% CI 0.98–1.94), LDL (OR = 1.46, 95% CI 0.96–2.23), and CHL (OR = 1.34, 95% CI 0.83–2.16) were not significantly associated with BtC risk. Whereas 1-SD increase of inverse-normal transformed TRG showed a significantly negative association with BtC risk (OR = 0.48, 95% CI 0.31–0.74). In multivariate MR analyses including all the four lipid traits, we found that 1-SD increase of LDL and TRG was significantly associated with elevated (OR = 1.32, 95% CI 1.04–2.01) and decreased (OR = 0.54, 95% CI 0.42–0.68) risk of BtC, respectively. Conclusion Circulating lipids, particularly LDL and TRG, may have roles in the development of BtC. However, the results of this study should be replicated in MR with larger GWAS sample sizes for BtC.
Background: Previous studies have suggested that patients with lung adenocarcinoma (LUAD) will significantly benefit from epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI). However, many LUAD patients will develop resistance to EGFR-TKI. Thus, our study aims to develop models to predict EGFR-TKI resistance and the LUAD prognosis.Methods: Two Gene Expression Omnibus (GEO) datasets (GSE31625 and GSE34228) were used as the discovery datasets to find the common differentially expressed genes (DEGs) in EGFR-TKI resistant LUAD profiles. The association of these common DEGs with LUAD prognosis was investigated in The Cancer Genome Atlas (TCGA) database. Moreover, we constructed the risk score for prognosis prediction of LUAD by LASSO analysis. The performance of the risk score for predicting LUAD prognosis was calculated using an independent dataset (GSE37745). A random forest model by risk score genes was trained in the training dataset, and the diagnostic ability for distinguishing sensitive and EGFR-TKI resistant samples was validated in the internal testing dataset and external testing datasets (GSE122005, GSE80344, and GSE123066).Results: From the discovery datasets, 267 common upregulated genes and 374 common downregulated genes were identified. Among these common DEGs, there were 59 genes negatively associated with prognosis, while 21 genes exhibited positive correlations with prognosis. Eight genes (ABCC2, ARL2BP, DKK1, FUT1, LRFN4, PYGL, SMNDC1, and SNAI2) were selected to construct the risk score signature. In both the discovery and independent validation datasets, LUAD patients with the higher risk score had a poorer prognosis. The nomogram based on risk score showed good performance in prognosis prediction with a C-index of 0.77. The expression levels of ABCC2, ARL2BP, DKK1, LRFN4, PYGL, SMNDC1, and SNAI2 were positively related to the resistance of EGFR-TKI. However, the expression level of FUT1 was favorably correlated with EGFR-TKI responsiveness. The RF model worked wonderfully for distinguishing sensitive and resistant EGFR-TKI samples in the internal and external testing datasets, with predictive area under the curves (AUC) of 0.973 and 0.817, respectively.Conclusion: Our investigation revealed eight genes associated with EGFR-TKI resistance and provided models for EGFR-TKI resistance and prognosis prediction in LUAD patients.
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