Protein anabolism is key to long-term survival in high-grade serous ovarian cancer

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.

Comprehensive analysis of the basement membrane in lung adenocarcinoma by bulk and single-cell sequencing analysis

Background: The basement membrane (BM), as a critical component of the extracellular matrix, plays a role in cancer progression. However, the role of the BM in lung adenocarcinoma (LUAD) remains unclear. Methods: A total of 1383 patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts were enrolled in the study, and BM-related differentially expressed genes (BM-DEGs) were screened using weighted gene coexpression network analysis (WGCNA) and differential expression analysis. We next built a prognostic model using Cox regression analysis and separated patients into two groups based on the median risk score. This signature was validated with in vitro experiments, and its mechanism was investigated by enrichment and tumour microenvironment analyses. We also evaluated whether this signature could predict sensitivity to chemotherapy and immunotherapy. Finally, single-cell RNA sequencing analysis was utilized to analyse the expression of signature genes in different cells. Results: Thirsty-seven BM-DEGs were discovered, and a prognostic signature based on 4 BM-DEGs (HMCN2, FBLN5, ADAMTS15 and LAD1) was obtained in the TCGA cohort and validated in GEO cohorts. Survival curves and ROC curve analysis demonstrated that the risk score was a significant predictor of survival in all cohorts even when considering the effect of other clinical indexes. Low-risk patients had longer survival times, higher immune cell infiltration levels and better immunotherapeutic responses. Single-cell analysis showed that FBLN5 and LAD1 were overexpressed in fibroblasts and cancer cells, respectively, compared to normal cells. Conclusion: This study evaluated the clinical role of the BM in LUAD and primarily explored its mechanism.

A pan-cancer analysis of the oncogenic role of procollagen C-endopeptidase enhancer (PCOLCE) in human

There is no evidence showing that the expression of procollagen C-endopeptidase enhancer (PCOLCE) is associated with human tumors, and pan-cancer analysis is not available. Based on public databases such as the cancer genome atlas, we investigated the potential role of PCOLCE expression in 33 different human tumors. PCOLCE expression in 11 tumors was significantly correlated with tumor prognosis and was a prognostic predictor for pancreatic adenocarcinoma, thymoma and CES. We also found that PCOLCE expression correlated with the immune microenvironment of tumors and the level of cancer-associated fibroblast infiltration. PCOLCE is a potential predictor of small molecule targeted drugs and immune checkpoint inhibitors. Finally, we found by enrichment analysis that PCOLCE localizes to extracellular structures and the extracellular matrix and exerts substantial effects on tumors through the PI3K-Akt and AGE-RAGE signaling pathways. We have a preliminary and relatively comprehensive understanding of the role of PCOLCE in various tumors.

An Integrative Analysis Framework for Identifying the Prognostic Markers from Multidimensional RNA Data of Clear Cell Renal Cell Carcinoma

The altered regulatory status of long noncoding RNA (lncRNA), miRNA, and mRNA and their interactions play critical roles in tumor proliferation, metastasis, and progression, which ultimately influence cancer prognosis. However, there are limited studies of comprehensive identification of prognostic biomarkers from combined data sets of the three RNA types in the highly metastatic clear cell renal cell carcinoma (ccRCC). The current study employed an integrative analysis framework of functional genomics approaches and machine learning methods to the lncRNA, miRNA, and mRNA data and identified 16 RNAs (3 lncRNAs, 6 miRNAs, and 7 mRNAs) of prognostic value, with 9 of them novel. A 16 RNA-based score was established for prognosis prediction of ccRCC with significance (P < 0.0001). The area under the curve for the score model was 0.868 to 0.870 in the training cohort and 0.714 to 0.778 in the validation cohort. Construction of the lncRNA-miRNA-mRNA interaction network showed that the downstream mRNAs and upstream lncRNAs in the network initiated from the miRNA or lncRNA markers exhibit significant enrichment in functional classifications associated with cancer metastasis, proliferation, progression, or prognosis. The functional analysis provided clear support for the role of the RNA biomarkers in predicting cancer prognosis. This study provides promising biomarkers for predicting prognosis of ccRCC using multidimensional RNA data, and these findings are expected to facilitate potential clinical applications of the biomarkers.

The Role of the Extracellular Matrix and Tumor-Infiltrating Immune Cells in the Prognostication of High-Grade Serous Ovarian Cancer

Ovarian cancer is the eighth global leading cause of cancer-related death among women. The most common form is the high-grade serous ovarian carcinoma (HGSOC). No further improvements in the 5-year overall survival have been seen over the last 40 years since the adoption of platinum- and taxane-based chemotherapy. Hence, a better understanding of the mechanisms governing this aggressive phenotype would help identify better therapeutic strategies. Recent research linked onset, progression, and response to treatment with dysregulated components of the tumor microenvironment (TME) in many types of cancer. In this study, using bioinformatic approaches, we identified a 19-gene TME-related HGSOC prognostic genetic panel (19 prognostic genes (PLXNB2, HMCN2, NDNF, NTN1, TGFBI, CHAD, CLEC5A, PLXNA1, CST9, LOXL4, MMP17, PI3, PRSS1, SERPINA10, TLL1, CBLN2, IL26, NRG4, and WNT9A) by assessing the RNA sequencing data of 342 tumors available in the TCGA database. Using machine learning, we found that specific patterns of infiltrating immune cells characterized each risk group. Furthermore, we demonstrated the predictive potential of our risk score across different platforms and its improved prognostic performance compared with other gene panels.

Integrated weighted gene coexpression network analysis identifies Frizzled 2 (FZD2) as a key gene in invasive malignant pleomorphic adenoma

Background

Invasive malignant pleomorphic adenoma (IMPA) is a highly malignant neoplasm of the oral salivary glands with a poor prognosis and a considerable risk of recurrence. Many disease-causing genes of IMPA have been identified in recent decades (e.g., P53, PCNA and HMGA2), but many of these genes remain to be explored. Weighted gene coexpression network analysis (WGCNA) is a newly emerged algorithm that can cluster genes and form modules based on similar gene expression patterns. This study constructed a gene coexpression network of IMPA via WGCNA and then carried out multifaceted analysis to identify novel disease-causing genes.

Methods

RNA sequencing (RNA-seq) was performed for 10 pairs of IMPA and normal tissues to acquire the gene expression profiles. Differentially expressed genes (DEGs) were screened out with the cutoff criteria of |log₂ Fold change (FC)|> 1 and adjusted p value  < 0.05. Then, WGCNA was applied to systematically identify the hidden diagnostic hub genes of IMPA.

Results

In this research, a total of 1970 DEGs were screened out in IMPA tissues, including 1056 upregulated DEGs and 914 downregulated DEGs. Functional enrichment analysis was performed for identified DEGs and revealed an enrichment of tumor-associated GO terms and KEGG pathways. We used WGCNA to identify gene module most relevant with the histological grade of IMPA. The gene FZD2 was then recognized as the hub gene of the selected module with the highest module membership (MM) value and intramodule connectivity in protein–protein interaction (PPI) network. According to immunohistochemistry (IHC) staining, the expression level of FZD2 was higher in low-grade IMPA than in high-grade IMPA.

Conclusion

FZD2 shows an expression dynamic that is negatively correlated with the clinical malignancy of IMPA and it plays a central role in the transcription network of IMPA. Thus, FZD2 serves as a promising histological indicator for the precise prediction of IMPA histological stages.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03204-7.

Anti-malarial drug dihydroartemisinin downregulates the expression levels of CDK1 and CCNB1 in liver cancer

Liver cancer is the third leading cause of cancer-associated mortality worldwide. By the time liver cancer is diagnosed, it is already in the advanced stage. Therefore, novel therapeutic strategies need to be identified to improve the prognosis of patients with liver cancer. In the present study, the profiles of GSE84402, GSE19665 and GSE121248 were used to screen differentially expressed genes (DEGs). Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses for DEGs were conducted using the Database for Annotation, Visualization and Integrated Discovery. The protein-protein interaction network was established to screen the hub genes associated with liver cancer. Additionally, the expression levels of hub genes were validated using the Gene Expression Profiling Interactive Analysis and Oncomine databases. In addition, the prognostic value of hub genes in patients with liver cancer was analyzed using Kaplan-Meier Plotter. It was demonstrated that 132 and 246 genes were upregulated and downregulated, respectively, in patients with liver cancer. Among these DEGs, 10 hub genes with high connected node values were identified, which were AURKA, BIRC5, BUB1B, CCNA2, CCNB1, CCNB2, CDC20, CDK1, DLGAP5 and MAD2L1. CDK1 and CCNB1 had the most connection nodes and the highest score and were therefore, the most significantly expressed. In addition, it was demonstrated that high expression levels of CDK1 and CCNB1 were associated with poor overall survival time of patients with liver cancer. Dihydroartemisinin (DHA) is a Food and Drug Administration-approved drug, which is derived from the traditional Chinese medicine Artemisia annua Linn. DHA inhibits cell proliferation in numerous cancer types, including liver cancer. In our previous study, it was revealed that DHA inhibited the proliferation of HepG2215 cells. In the present study, it was further demonstrated that DHA reduced the expression levels of CDK1 and CCNB1 in liver cancer. Overall, CDK1 and CCNB1 were the potential therapeutic targets of liver cancer, and DHA reduced the expression levels of CDK1 and CCNB1, and inhibited the proliferation of liver cancer cells.