High Stromal SFRP2 Expression in Urothelial Carcinoma Confers an Unfavorable Prognosis

Down-regulation of RTEL1 Improves M1/M2 Macrophage Polarization by Promoting SFRP2 in Fibroblasts-derived Exosomes to Alleviate COPD

Chronic obstructive pulmonary disease (COPD) is a common chronic respiratory disease worldwide. Macrophage polarization plays a substantial role in the pathogenesis of COPD. This study is aimed to explore the regulatory mechanism of regulator of telomere elongation 1 (RTEL1) in COPD. COPD model mouse was conducted by cigarette smoke (CS). The pathological features of lung in mice were observed by histological staining. After extracting exosomes, macrophages were co-cultured with fibroblasts-derived exosomes. Then, the effects of RTEL1 and exosomal secreted frizzled-related protein 2 (SFRP2) on macrophage proliferation, inflammation, apoptosis, and M1, M2 macrophage polarization (iNOS and CD206) were evaluated by cell counting kit-8, EdU assay, enzyme-linked immuno sorbent assay, and western blotting, respectively. CS-induced COPD model mouse was successfully constructed. Through in vitro experiments, knockdown of RTEL1 inhibited macrophage proliferation, inflammation (MMP9, IL-1β and TNF-α), and promoted apoptosis (Bax, cleaved-caspase3, Bcl-2) in CS extract-induced lung fibroblasts. Meanwhile, RTEL1 knockdown promoted M1 and suppressed M2 macrophage polarization in COPD. Additionally, silencing SFRP2 in fibroblasts-derived exosomes reversed the effects of RTEL1 knockdown on proliferation, inflammation, apoptosis, and M1, M2 macrophage polarization. Collectively, down-regulation of RTEL1 improved M1/M2 macrophage polarization by promoting SFRP2 in fibroblasts-derived exosomes to alleviate CS-induced COPD.

High expression levels of S1PR3 and PDGFRB indicates unfavorable clinical outcomes in colon adenocarcinoma

Background

Studies verified that sphingosine kinase 1 (SPHK1)/sphingosine 1-phosphate receptors (S1PRs) and platelet-derived growth factor receptors (PDGFRs) play important roles in tumor occurrence and progression. However, the expression and clinical value of SPHK1/S1PRs and PDGFRs in colon adenocarcinoma (COAD) remains unclear. This study aimed to explore the expression of SPHK1/S1PRs and PDGFRs in COAD and further investigate their roles in predicting the prognosis of patients with COAD.

Methods

SPHK1/S1PRs and PDGFRs expression in tissues from patient with COAD were analyzed using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Kaplan-Meier survival analysis was used to evaluate the prognostic roles of SPHK1/S1PRs and PDGFRs in patients with COAD. Spearman's correlation analysis was performed to assess the relationship between SPHK1/S1PRs and PDGFRs in COAD. Then, χ2 test was performed to analyze the correlation between SPHK1/S1PR3/PDGFRB and clinicopathological characteristics of the patients. Additionally, possible signaling pathways co-regulated by S1PR3 and PDGFRB were predicted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Least absolute shrinkage and selection operator (LASSO) regression was used to identify hub genes that co-regulated S1PR3 and PDGFRB expression. A prognostic model based on hub genes was constructed for patients with COPD. Finally, the relationship between the hub genes and tumor immune cell infiltration was investigated.

Results

The expression levels of SPHK1 and PDGFRB were significantly upregulated in COAD patient tissues (P < 0.001 and P < 0.001, respectively). Moreover, Kaplan-Meier analysis showed that patients with COAD with high expression levels of SPHK1 and S1PR3 had shorter overall survival (OS) than those with low expression levels (P = 0.013 and P = 0.005, respectively). Spearman's correlation analysis verified a strong positive correlation (P < 0.001, r = 0.790) between the expression of S1PR3 and PDGFRB. In addition, we found that high SPHK1 and PDGGRB expression levels were associated with perineural invasion (P < 0.001 and P = 0.011, respectively). High expression of S1PR3 and PDGGRB was prominently associated with N stage (P = 0.002 and P = 0.021, respectively). High levels of SPHK1, S1PR3, and PDGFRB were associated with lymph node invasion. (P = 0.018, P = 0.004, and P = 0.001, respectively). GO and KEGG results revealed that S1PR3 and PDGFRB may participate in COAD cell extracellular matrix organization and cellular signal transduction. Five hub genes, SFRP2, GPRC5B, RSPO3, FGF14, and TCF7L1, were identified using LASSO regression. Survival analysis showed that the OS in the high-risk group was remarkably shorter than that in the low-risk group. The results indicated that tumor immune cells were significantly increased in the high-risk group compared to those in the low-risk group.

Conclusions

S1PR3 and PDGFRB may be important markers for predicting lymphatic metastasis and poor prognosis in patients with COAD. The underlying mechanisms may involve immune cell infiltration.

Prognostic Biomarkers for Hepatocellular Carcinoma Based on Serine and Glycine Metabolism-related Genes

Background and Aims

Targeted therapy and immunotherapy have emerged as treatment options for hepatocellular carcinoma (HCC) in recent years. The significance of serine and glycine metabolism in various cancers is widely acknowledged. This study aims to investigate their correlation with the prognosis and tumor immune microenvironment (TIME) of HCC.

Methods

Based on the public database, different subtypes were identified by cluster analysis, and the prognostic model was constructed through regression analysis. The gene expression omnibus (GEO) data set was used as the validation set to verify the performance of the model. The survival curve evaluated prognostic ability. CIBERSORT was used to evaluate the level of immune cell infiltration, and maftools analyzed the mutations. DsigDB screened small molecule compounds related to prognostic genes.

Results

HCC was found to have two distinct subtypes. Subsequently, we constructed a risk score prognostic model through regression analysis based on serine and glycine metabolism-related genes (SGMGs). A nomogram was constructed based on risk scores and other clinical factors. HCC patients with a higher risk score showed a poor prognosis, and there were significant differences in immune cell infiltration between the high- and low-risk groups. In addition, three potential drugs associated with prognostic genes, streptozocin, norfloxacin, and hydrocotarnine, were identified.

Conclusions

This study investigated the expression patterns of SGMGs and their relationship with tumor characteristics, resulting in the development of a novel model for predicting the prognosis of HCC patients. The study provides a reference for clinical prognosis prediction and treatment of HCC patients.

Identifying Explainable Machine Learning Models and a Novel SFRP2+ Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer

Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functional subsets in the tumor microenvironment are essential to the development of patient management strategies. However, the development of robust models remains unsatisfactory. We aimed to utilize artificial intelligence and single-cell analysis to address this issue. Two independent datasets were screened from the Gene Expression Omnibus (GEO) database and processed to obtain overlapping differentially expressed genes (DEGs) in stage II-IV vs. stage I diseases. Three explainable machine learning algorithms were integrated to construct models that could determine the tumor stage and extract important characteristic genes as diagnostic biomarkers. Correlations between cancer-associated fibroblast (CAF) infiltration and characteristic gene expression were analyzed using TIMER2.0 and their relationship with survival rates was comprehensively explored via the Kaplan-Meier plotter (KM-plotter) online database. The specific expression of characteristic genes in fibroblast subsets was investigated through single-cell analysis. A novel fibroblast subset signature was explored to predict immune checkpoint inhibitor (ICI) response and oncogene mutation through Tumor Immune Dysfunction and Exclusion (TIDE) and artificial neural network algorithms, respectively. We found that Support Vector Machine-Shapley Additive Explanations (SVM-SHAP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) successfully diagnosed early-stage OC (stage I). The area under the receiver operating characteristic curves (AUCs) of these models exceeded 0.990. Their overlapping characteristic gene, secreted frizzled-related protein 2 (SFRP2), was a risk factor that affected the overall survival of OC patients with stage II-IV disease (log-rank test: p < 0.01) and was specifically expressed in a fibroblast subset. Finally, the SFRP2+ fibroblast signature served as a novel predictor in evaluating ICI response and exploring pan-cancer tumor protein P53 (TP53) mutation (AUC = 0.853, 95% confidence interval [CI]: 0.829-0.877). In conclusion, the models based on SVM-SHAP, XGBoost, and RF enabled the early detection of OC for clinical decision making, and SFRP2+ fibroblast signature used in diagnostic models can inform OC treatment selection and offer pan-cancer TP53 mutation detection.