Yan Ren

A novel non-invasive mRNA-lncRNA biomarker panel for accurate prediction of cervical squamous cell carcinoma and adenocarcinoma

Squamous cell carcinoma (SCC) and adenocarcinoma (ADC) represent predominant histological subtypes of cervical cancer. To improve screening efficacy, we leveraged RNA sequencing data from 4 cervical SCC samples, 4 cervical ADC samples, and 8 normal cervix samples and conducted a comprehensive mRNA and long noncoding RNA (lncRNA) profiling analysis followed with a multi-phase study comprising 556 samples. Validating the RNA sequencing data in a clinical sample set comprising 45 normal cervix tissues, 45 SCC tissues, and 45 ADC tissues, we identified 9 mRNAs (SMC1B, OTX1, GRP, CELSR3, HOXC6, ITGB6, WDR62, SEPT3, and KLHL34) and 4 lncRNAs (FEZF1-AS1, LINC01305, LINC00857, and LINC00673) differentially expressed in both SCC and ADC samples. Utilizing quantitative reverse transcription polymerase chain reaction analysis and receiver operating characteristic (ROC) curve analysis in a training set (45 normal, 126 SCC, and 82 ADC tissues), we refined a novel mRNA-lncRNA-based panel (SMC1B/CELSR3/FEZF1-AS1/LINC01305). Employing logistic regression model and ROC analysis, this panel exhibited significant distinctions and promising area under the curve (AUC) values in both SCC (AUC=0.9520, p<0.0001) and ADC (AUC=0.9748, p<0.0001) tissues. Subsequent validation in an independent set (11 normal, 32 SCC, and 20 ADC tissues) demonstrated its diagnostic accuracy in both SCC (AUC=0.9659, p<0.0001) and ADC (AUC=0.9636, p<0.0001) patients. Notably, this tissue-based biomarker panel robustly discriminated precancerous lesion and cervical cancer patients from non-disease controls in a blood-based validation set (30 normal, 25 HSIL and 50 cervical cancer) with an AUC value of 0.9320. This study presents a non-invasive, efficient diagnostic panel for cervical cancer screening.

Single-Cell RNA Sequencing Reveals the Tissue Architecture in Human High-Grade Serous Ovarian Cancer

Abstract Purpose: The heterogeneity of high-grade serous ovarian cancer (HGSOC) is not well studied, which severely hinders clinical treatment of HGSOC. Thus, it is necessary to characterize the heterogeneity of HGSOC within its tumor microenvironment (TME). Experimental Design: The tumors of 7 treatment-naïve patients with HGSOC at early or late stages and five age-matched nonmalignant ovarian samples were analyzed by deep single-cell RNA sequencing (scRNA-seq). Results: A total of 59,324 single cells obtained from HGSOC and nonmalignant ovarian tissues were sequenced by scRNA-seq. Among those cells, tumor cells were characterized by a set of epithelial-to-mesenchymal transition (EMT)-associated gene signatures, in which a combination of NOTCH1, SNAI2, TGFBR1, and WNT11 was further selected as a genetic panel to predict the poor outcomes of patients with HGSOC. Matrix cancer-associated fibroblasts (mCAF) expressing α-SMA, vimentin, COL3A, COL10A, and MMP11 were the dominant CAFs in HGSOC tumors and could induce EMT properties of ovarian cancer cells in the coculture system. Specific immune cell subsets such as C7-APOBEC3A M1 macrophages, CD8+ TRM, and TEX cells were preferentially enriched in early-stage tumors. In addition, an immune coinhibitory receptor TIGIT was highly expressed on CD8+ TEX cells and TIGIT blockade could significantly reduce ovarian cancer tumor growth in mouse models. Conclusions: Our transcriptomic results analyzed by scRNA-seq delineate an ecosystemic landscape of HGSOC at early or late stages with a focus on its heterogeneity with TME. The major applications of our findings are a four–EMT gene model for prediction of HGSOC patient outcomes, mCAFs’ capability of enhancing ovarian cancer cell invasion and potential therapeutic value of anti-TIGIT treatment.

ANXA2 is a Potential Marker for the Diagnosis of Human Cervical Cancer