Discovery of Lipid Metabolism-Related Genes for Predicting Tumor Immune Microenvironment Status and Prognosis in Prostate Cancer

Comprehensive Analysis of Fatty Acid Metabolism in Diabetic Nephropathy from the Perspective of Immune Landscapes, Diagnosis and Precise Therapy

Objective

Diabetic nephropathy (DN) represents the principal cause of end-stage renal diseases worldwide, lacking effective therapies. Fatty acid (FA) serves as the primary energy source in the kidney and its dysregulation is frequently observed in DN. Nevertheless, the roles of FA metabolism in the occurrence and progression of DN have not been fully elucidated.

Methods

Three DN datasets (GSE96804/GSE30528/GSE104948) were obtained and combined. Differentially expressed FA metabolism-related genes were identified and subjected to DN classification using "ConsensusClusterPlus". DN subtypes-associated modules were discovered by "WGCNA", and module genes underwent functional enrichment analysis. The immune landscapes and potential drugs were analyzed using "CIBERSORT" and "CMAP", respectively. Candidate diagnostic biomarkers of DN were screened using machine learning algorithms. A prediction model was constructed, and the performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The online tool "Nephroseq v5" was conducted to reveal the clinical significance of the candidate diagnostic biomarkers in patients with DN. A DN mouse model was established to verify the biomarkers' expression.

Results

According to 39 dysregulated FA metabolism-related genes, DN samples were divided into two molecular subtypes. Patients in Cluster B exhibited worse outcomes with a different immune landscape compared with those in Cluster A. Ten potential small-molecular drugs were predicted to treat DN in Cluster B. The diagnostic model based on PRKAR2B/ANXA1 was created with ideal predictive values in early and advanced stages of DN. The correlation analysis revealed significant association between PRKAR2B/ANXA1 and clinical characteristics. The DN mouse model validated the expression patterns of PRKAR2B/ANXA1.

Conclusion

Our study provides new insights into the role of FA metabolism in the classification, immunological pathogenesis, early diagnosis, and precise therapy of DN.

Expressions of glucose transporter genes are diversely attenuated and significantly associated with prostate cancer progression

Prostate cancer is a health-threaten disease in men worldwide, however, lacking is the reliable biomarkers for patient management. Aberrant metabolic events including glucose metabolism are involved in prostate cancer progression. To examine the involvement of glucose metabolic pathways in prostate cancer, we analyzed the expression profiles of glucose transporter family genes using multiple RNA-seq datasets. Our results showed that three SLC2A family genes (SLC2A4/5/9) were significantly downregulated in primary prostate cancers compared to their benign compartments. These down-regulated expressions were inversely correlated with their gene promoter methylation and genome abnormalities. Among these three SLC2A genes, only SLC2A4 showed a significantly reverse correlation with all clinicopathological parameters, including TNM stage, disease relapse, Gleason score, disease-specific survival, and progression-free interval. In addition, the expression levels of these three genes were strongly correlated with anti-cancer immune cell filtration in primary prostate cancers. In a group of patients with early-onset prostate cancers, SLC2A4 also showed a strong negative correlation with multiple clinicopathological parameters, such as tumor mutation burden, biochemical relapse, pre-surgical PSA levels, and Gleason score but a positive correlation with progression-free interval after surgery. In metastatic castration-resistant prostate cancers (CRPC), SLC2A9 gene expression but not SLC2A4 or SLC2A5 genes showed a significant correlation with androgen receptor (AR) activity score and neuroendocrinal (NE) activity score. Meanwhile, SLC2A2/9/13 expression was significantly elevated in CRPC tumors with neuroendocrinal features compared to those without NE features. On the other hand, SLC2A10 and SlC2A12 gene expression were significantly reduced in NEPC tumors compared to CRPC tumors. Consistently, SLC2A10/12 expression levels were significantly reduced in castrated animals carrying the LuCaP35 xenograft models. Survival outcome analysis revealed that SLC2A4 expression in primary tumors is a favorable prognostic factor and SLC2A6 is a worse prognostic factor for disease-specific survival and progression-free survival in prostate cancer patients. In conclusion, our results suggest that SLC2A4/6 expressions are strong prognostic factors for prostate cancer progression and survival. The significance of SLC2A2/9/13 over-expression during NEPC progression needs more investigation.

A lipid metabolism-related gene signature reveals dynamic immune infiltration of the colorectal adenoma-carcinoma sequence

BACKGROUND

Lipid metabolism-related genes (LMRGs) have been reported to be correlated with the immune infiltration of colorectal cancer (CRC). This study aimed to investigate the immune infiltration characteristics along the colorectal adenoma-carcinoma sequence (ACS) based on LMRGs.

METHODS

Gene expression data of colorectal adenoma and carcinoma samples were obtained from the public databases. The "limma" package was applied to determine the differentially expressed LMRGs. Unsupervised consensus clustering was used to cluster colorectal samples. The features of the tumor microenvironment were analyzed by the "ESTIMATE", "GSVA", and "TIDE" algorithms.

RESULTS

The expression of 149 differentially expressed LMRGs was defined as the LMRG signature. Based on this signature, the adenoma and carcinoma samples were divided into three clusters. Unexpectedly, these sequential clusters showed a directional relationship and collectively constituted the progressive course of colorectal ACS. Interestingly, the LMRG signature revealed that adenoma progression was accompanied by a progressive loss of immune infiltration and a stepwise establishment of a cold microenvironment, but carcinoma progression was characterized by a progressive gain of immune infiltration and a gradual establishment of a hot microenvironment.

CONCLUSIONS

The LMRG signature reveals dynamic immune infiltration along the colorectal ACS, which substantially changes the understanding of the tumor microenvironment of CRC carcinogenesis and provides novel insight into the role of lipid metabolism in this process.

Establishing and Validating an Aging-Related Prognostic Signature in Osteosarcoma

Aging is an inevitable process that biological changes accumulate with time and results in increased susceptibility to different tumors. But currently, aging-related genes (ARGs) in osteosarcoma were not clear. We investigated the potential prognostic role of ARGs and established an ARG-based prognostic signature for osteosarcoma. The transcriptome data and corresponding clinicopathological information of patients with osteosarcoma were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Molecular subtypes were generated based on prognosis-related ARGs obtained from univariate Cox analysis. With ARGs, a risk signature was built by univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Differences in clinicopathological features, immune infiltration, immune checkpoints, responsiveness to immunotherapy and chemotherapy, and biological pathways were assessed according to molecular subtypes and the risk signature. Based on risk signature and clinicopathological variables, a nomogram was established and validated. Three molecular subtypes with distinct clinical outcomes were classified based on 36 prognostic ARGs for osteosarcoma. A nine-ARG-based signature in the TCGA cohort, including BMP8A, CORT, SLC17A9, VEGFA, GAL, SSX1, RASGRP2, SDC3, and EVI2B, has been created and developed and could well perform patient stratification into the high- and low-risk groups. There were significant differences in clinicopathological features, immune checkpoints and infiltration, responsiveness to immunotherapy and chemotherapy, cancer stem cell, and biological pathways among the molecular subtypes. The risk signature and metastatic status were identified as independent prognostic factors for osteosarcoma. A nomogram combining ARG-based risk signature and metastatic status was established, showing great prediction accuracy and clinical benefit for osteosarcoma OS. We characterized three ARG-based molecular subtypes with distinct characteristics and built an ARG-based risk signature for osteosarcoma prognosis, which could facilitate prognosis prediction and making personalized treatment in osteosarcoma.