Systematic analysis of TREM2 and its carcinogenesis in pancreatic cancer.

Background: Triggering receptor expressed on myeloid cells 2 (TREM2), a transmembrane immunoglobulin-superfamily receptor, is expressed primarily on cells such as macrophages and dendritic cells. TREM2 has been shown to be associated with diseases such as neurodegeneration, fatty liver, obesity, and atherosclerosis. Currently, it has become one of the hotspots in oncology research. However, the role of TREM2 in pan-cancer, especially pancreatic cancer, remains unclear. Methods: We used the Tumor-immune System Interactions Database (TISIDB) to explore TREM2 expression differences, Tumor Immune Single-cell Hub 2 (TISCH2) to explore TREM2 expression distribution, Tumor IMmune Estimation Resource 2.0 (TIMER 2.0) to explore immune infiltration, cBio Cancer Genomics Portal (cBioPortal) to explore genetic variation, Genomics of Drug Sensitivity in Cancer (GDSC) to explore drug resistance, and Kaplan-Meier plotter database to explore the relationship between TREM2 and prognosis in pancreatic cancer. In addition, we used The Cancer Genome Atlas-pancreatic adenocarcinoma (TCGA-PAAD) and normal pancreas samples from the Genotype-Tissue Expression (GTEx) databases to explore the relationship between TREM2 and lymph node metastasis. We verified the protein level of TREM2 in pancreatic cancer by Human Protein Atlas (HPA) and western blotting and detected the colocalization of TREM2 with malignant cell markers by multiplex immunohistochemistry (mIHC). Finally, we identified the tumor-promoting role of TREM2 in pancreatic cancer via in vitro experiments, such as cell cycle assays, colony formation assays, and transwell migration and invasion assays. Results: Our results showed that TREM2 was differentially expressed in various tumors according to different molecular and immune subtypes of pan-cancer. It was found that TREM2 was mainly expressed in monocytes/macrophages. In addition, our study showed that TREM2 expression was closely associated with macrophages in the tumor microenvironment (TME) of pan-cancer. TREM2 was shown to be related to anti-inflammatory and immunosuppressive effects in most cancers. Furthermore, we found that amplification was the main somatic mutation of TREM2 in pan-cancer. Further correlational analysis revealed a significant negative association of TREM2 expression with sensitivity to AZD8186, which is a selective inhibitor of PI3K, but not gemcitabine and paclitaxel. Finally, through the knockdown and overexpression of TREM2, our findings verified that TREM2 on cancer cells promoted the progression of PAAD. Conclusions: In conclusion, our comprehensive analysis identified that TREM2 expression level was correlated with the TME and the immunosuppressive effects. In particular, our study indicated that TREM2 was involved in the progression of pancreatic cancer.

A novel machine learning-derived four-gene signature predicts STEMI and post-STEMI heart failure.

High mortality and morbidity rates associated with ST-elevation myocardial infarction (STEMI) and post-STEMI heart failure (HF) necessitate proper risk stratification for coronary artery disease (CAD). A prediction model that combines specificity and convenience is highly required. This study aimed to design a monocyte-based gene assay for predicting STEMI and post-STEMI HF. A total of 1,956 monocyte expression profiles and corresponding clinical data were integrated from multiple sources. Meta-results were obtained through the weighted gene co-expression network analysis (WGCNA) and differential analysis to identify characteristic genes for STEMI. Machine learning models based on the decision tree (DT), support vector machine (SVM), and random forest (RF) algorithms were trained and validated. Five genes overlapped and were subjected to the model proposal. The discriminative performance of the DT model outperformed the other two methods. The established four-gene panel (HLA-J, CFP, STX11, and NFYC) could discriminate STEMI and HF with an area under the curve (AUC) of 0.86 or above. In the gene set enrichment analysis (GSEA), several cardiac pathogenesis pathways and cardiovascular disorder signatures showed statistically significant, concordant differences between subjects with high and low expression levels of the four-gene panel, affirming the validity of the established model. In conclusion, we have developed and validated a model that offers the hope for accurately predicting the risk of STEMI and HF, leading to optimal risk stratification and personalized management of CAD, thereby improving individual outcomes.

Thyroid-stimulating hormone-thyroid hormone signaling contributes to circadian regulation through repressing clock2/npas2 in zebrafish.

Thyroid-stimulating hormone (TSH) is important for the thyroid gland, development, growth, and metabolism. Defects in TSH production or the thyrotrope cells within the pituitary gland cause congenital hypothyroidism (CH), resulting in growth retardation and neurocognitive impairment. While human TSH is known to display rhythmicity, the molecular mechanisms underlying the circadian regulation of TSH and the effects of TSH-thyroid hormone (TH) signaling on the circadian clock remain elusive. Here we show that TSH, thyroxine (T4), triiodothyronine (T3), and tshba display rhythmicity in both larval and adult zebrafish and tshba is regulated directly by the circadian clock via both E'-box and D-box. Zebrafish tshba-/- mutants manifest congenital hypothyroidism, with the characteristics of low levels of T4 and T3 and growth retardation. Loss or overexpression of tshba alters the rhythmicity of locomotor activities and expression of core circadian clock genes and hypothalamic-pituitary-thyroid (HPT) axis-related genes. Furthermore, TSH-TH signaling regulates clock2/npas2 via the thyroid response element (TRE) in its promoter, and transcriptome analysis reveals extensive functions of Tshba in zebrafish. Together, our results demonstrate that zebrafish tshba is a direct target of the circadian clock and in turn plays critical roles in circadian regulation along with other functions.

STAT6 contributes to renal fibrosis by modulating PPARα-mediated tubular fatty acid oxidation.

Lipid metabolism, especially fatty acid oxidation (FAO) dysfunction, is a major driver of renal fibrosis; however, the detailed regulatory mechanisms involved remain unclear. In this study, we showed that there existed an association between the signal transducer and activator of transcription 6 (STAT6) and tubular lipid metabolism in fibrotic kidneys. Specifically, STAT6 was activated along with the accumulation of lipids via the downregulation of FAO-related genes when mice were subjected to unilateral ureteral obstruction (UUO) or high-fat diet challenge. Tubular-specific depletion, or pharmacologic inhibitor of Stat6 in mice, and Stat6 knockdown in cultured tubular cells attenuated lipid accumulation and renal fibrosis by enhancing FAO. Mechanistically, STAT6 transcriptionally inhibited the expression of PPARα and its FAO-related target genes through a sis-inducible element located in the promoter region of the protein. In conclusion, our study demonstrates the mechanistic details of STAT6-mediated FAO dysregulation in the progression of renal fibrosis and provides a preclinical rationale for efforts to improve the management of renal fibrosis brought about by FAO dysregulation.

Transcriptomic Analysis Identifies Complement Component 3 as a Potential Predictive Biomarker for Chemotherapy Resistance in Colorectal Cancer.

Objective: 5-fluorouracil- and oxaliplatin-based FOLFOX regimens are mainstay chemotherapeutics for colorectal cancer (CRC) but drug resistance represents a major therapeutic challenge. To improve patient survival, there is a need to identify resistance genes to better understand the mechanisms underlying chemotherapy resistance. Methods: Transcriptomic datasets were retrieved from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and combined with our own microarray data. Weighted gene co-expression network analysis (WGCNA) was used to dissect the functional networks and hub genes associated with FOLFOX resistance and cancer recurrence. We then conducted analysis of prognosis, profiling of tumor infiltrating immune cells, and pathway overrepresentation analysis to comprehensively elucidate the biological impact of the identified hub gene in CRC. Results: WGCNA analysis identified the complement component 3 (C3) gene as the only hub gene associated with both FOLFOX chemotherapy resistance and CRC recurrence after FOLFOX chemotherapy. Subsequent survival analysis confirmed that high C3 expression confers poor progression-free survival, disease-free survival, and recurrence-free survival. Further correlational analysis revealed significant negative association of C3 expression with sensitivity to oxaliplatin, but not 5-fluorouracil. Moreover, in silico analysis of tumor immune cell infiltration suggested the change of C3 expression could affect tumor microenvironment. Finally, gene set enrichment analysis (GSEA) revealed a hyperactivation of pathways contributing to invasion, metastasis, lymph node spread, and oxaliplatin resistance in CRC samples with C3 overexpression. Conclusion: Our results suggest that high C3 expression is a debilitating factor for FOLFOX chemotherapy, especially for oxaliplatin sensitivity, and C3 may represent a novel biomarker for treatment decision of CRC.