ZYG11A Is Expressed in Epithelial Ovarian Cancer and Correlates With Low Grade Disease

Prognostic Outcome of Mesenchymal Transition Biomarkers in Correlation with EGFR Expression in Epithelial Ovarian Carcinoma Patients

BACKGROUND

CD44 is an epithelial-mesenchymal transition (EMT) surface receptor that regulates the interactivity between the cells and the extracellular matrix, thereby promoting cell migration. The epidermal growth factor receptor (EGFR) family is a trans-membrane kinase-related protein. It regulates cell adhesion proteins, which may promote cell proliferation and invasiveness. Mesenchymal epithelial transition (MET) is another EMT receptor that stimulates cell proliferation, invasion, survival, and angiogenesis. This study aimed to evaluate the prognostic impact of CD44, EGFR expressions, and MET gene amplification in epithelial ovarian cancer (EOC).

METHODS

This is a retrospective cohort study, including 85 cases of EOC. CD44 and EGFR expressions were evaluated in both epithelial and stromal cells by immunohistochemistry. Tumor cells also underwent a cytogenetic analysis using fluorescent in situ hybridization (FISH) to detect MET gene amplification.

RESULTS

High CD44 expression in tumors was significantly associated with serous subtypes (P=0.001), peritoneal deposits (P=0.002), and advanced stage (P=0.002). EGFR high tumor expression demonstrated a significant association with lymph node metastasis (P=0.038) and the advanced stage of EOC (P=0.016). Increased copy number of the MET gene was significantly associated with partial therapy response (P=0.030).  CD44 and EGFR tumor high expression was associated with poor overall survival (OS). In addition, MET gene gain in tumors was associated with a shorter OS (P=0.000).

CONCLUSION

EMT biomarkers (CD44 and MET) and EGFR expression in EOC are independent prognostic factors for OS. MET gene increase copy number was detected in cases of serous neoplasm and associated with poor survival and minimal therapy response.

Prognosis Prediction Through an Integrated Analysis of Single-Cell and Bulk RNA-Sequencing Data in Triple-Negative Breast Cancer

Background: Genomic and antigenic heterogeneity pose challenges in the precise assessment of outcomes of triple-negative breast cancer (TNBC) patients. Thus, this study was designed to investigate the cardinal genes related to cell differentiation and tumor malignant grade to advance the prognosis prediction in TNBC patients through an integrated analysis of single-cell and bulk RNA-sequencing (RNA-seq) data.

Methods: We collected RNA-seq and microarray data of TNBC from two public datasets. Using single-cell pseudotime analysis, differentially expressed genes (DEGs) among trajectories from 1534 cells of 6 TNBC patients were identified as the potential genes crucial for cell differentiation. Furthermore, the grade- and tumor mutational burden (TMB)-related DEGs were explored via a weighted correlation network analysis using the Molecular Taxonomy of Breast Cancer International Consortium dataset. Subsequently, we utilized the DEGs to construct a prognostic signature, which was validated using another independent dataset. Moreover, as gene set variation analysis indicated the differences in immune-related pathways between different risk groups, we explored the immune differences between the two groups.

Results: A signature including 10 genes related to grade and TMB was developed to assess the outcomes of TNBC patients, and its prognostic efficacy was prominent in two cohorts. The low-risk group generally harbored lower immune infiltration compared to the high-risk group.

Conclusion: Cell differentiation and grade- and TMB-related DEGs were identified using single-cell and bulk RNA-seq data. A 10-gene signature for prognosis prediction in TNBC patients was constructed, and its performance was excellent. Interestingly, the signature was found to be closely related to tumor immune infiltration, which might provide evidence for the crucial roles of immune cells in malignant initiation and progression in TNBC.

Establish of an Initial Platinum-Resistance Predictor in High-Grade Serous Ovarian Cancer Patients Regardless of Homologous Recombination Deficiency Status

Backgrounds

Ovarian cancer (OC) is still the leading aggressive and lethal disease of gynecological cancers, and platinum-based regimes are the standard treatments. However, nearly 20%–30% of patients with OC are initial platinum resistant (IPR), and there is a lack of valid tools to predict whether they will be primary platinum resistant or not prior to chemotherapy.

Methods

Transcriptome data from The Cancer Genome Atlas (TCGA) was downloaded as the training data, and transcriptome data of GSE15622, GSE102073, GSE19829, and GSE26712 were retrieved from Gene Expression Omnibus (GEO) as the validation cohorts. Differentially expressed genes (DEGs) were selected between platinum-sensitive and platinum-resistant patients from the training cohort, and multiple machine-learning algorithms [including random forest, XGboost, and least absolute shrinkage and selection operator (LASSO) regression] were utilized to determine the candidate genes from DEGs. Then, we applied logistic regression to establish the IPR signature based on the expression. Finally, comprehensive clinical, genomic, and survival feature were analyzed to understand the application value of the established IPR signature.

Results

A total of 532 DEGs were identified between platinum-resistant and platinum-sensitive samples, and 11 of them were shared by these three-machine learning algorithms and utilized to construct an IPR prediction signature. The area under receiver operating characteristic curve (AUC) was 0.841 and 0.796 in the training and validation cohorts, respectively. Notably, the prediction capacity of this signature was stable and robust regardless of the patients’ homologous recombination deficiency (HRD) and mutation burden status. Meanwhile, the genomic feature was concordant between samples with high- or low-IPR signature, except a significantly higher prevalence of gain at Chr19q.12 (regions including CCNE1) in the high-IPR signature samples. The efficacy of prediction of platinum resistance of IPR signature successfully transferred to the precise survival prediction, with the AUC of 0.71, 0.72, and 0.66 to predict 1-, 3-, and 5-year survival, respectively. At last, we found a significantly different tumor-infiltrated lymphocytes feature, including lower abundance of CD4+ naive T cells in the samples with high-IPR signature. A relatively lower tumor immune dysfunction and exclusion (TIDE) value and more sensitivity to multiple therapies including Gefitinib may suggest the potency to transfer from platinum-based therapy to immunotherapy or target therapies in patients with high-IPR signature.

Conclusion

Our study established an IPR signature based on the expression of 11 genes that could stably and robustly distinguish OC patients with IPR and/or poor outcomes, which may guide therapeutic regimes tailoring.