Translational Research

Proteomic analysis of malignant and benign endometrium according to obesity and insulin-resistance status using Reverse Phase Protein Array

Obesity and hyperinsulinemia are known risk factors for endometrial cancer, yet the biological pathways underlying this relationship are incompletely understood. This study investigated protein expression in endometrial cancer and benign tissue and its correlation with obesity and insulin resistance. One hundred and seven women undergoing hysterectomy for endometrial cancer or benign conditions provided a fasting blood sample and endometrial tissue. We performed proteomic expression according to body mass index, insulin resistance, and serum marker levels. We used linear regression and independent t test for statistical analysis. Proteomic data from 560 endometrial cancer cases from The Cancer Genome Atlas (TCGA) databank were used to assess reproducibility of results. One hundred and twenty seven proteins were significantly differentially expressed between 66 cancer and 26 benign patients. Protein expression involved in cell cycle progression, impacting cytoskeletal dynamics (PAK1) and cell survival (Rab 25), were most significantly altered. Obese women with cancer had increased PRAS40_pT246; a downstream marker of increased PI3K-AKT signaling. Obese women without cancer had increased mitogenic and antiapoptotic signaling by way of upregulation of Mcl-1, DUSP4, and Insulin Receptor-b. This exploratory study identified a number of candidate proteins specific to endometrioid endometrial cancer and benign endometrial tissues. Obesity and insulin resistance in women with benign endometrium leads to specific upregulation of proteins involved in insulin and driver oncogenic signaling pathways such as the PI3K-AKT-mTOR and growth factor signaling pathways which are mitogenic and also disruptive to metabolism.

DC-derived CXCL10 promotes CTL activation to suppress ovarian cancer

This study investigates the role of dendritic cells (DCs), with a focus on their CXCL10 marker gene, in the activation of cytotoxic T lymphocytes (CTLs) within the ovarian cancer microenvironment and its impact on disease progression. Utilizing scRNA-seq data and immune infiltration analysis, we identified a diminished DC presence in ovarian cancer. Gene analysis pinpointed CXCL10 as a key regulator in OV progression via its influence on DCs and CTLs. Prognostic analysis and in vitro experiments substantiated this role. Our findings reveal that DC-derived CXCL10 significantly affects CTL activation and proliferation. Reduced CXCL10 levels hinder CTL cytotoxicity, promoting ovarian cancer cell migration and invasion. Experimental studies using animal models have provided further evidence that the capacity of CTLs to suppress tumor development is significantly diminished when treated with DCs that have low expression of CXCL10. Dendritic cell-derived CXCL10 emerges as a pivotal factor in restraining ovarian cancer growth and metastasis through the activation of cytotoxic T lymphocytes. This study sheds light on the crucial interplay within the ovarian cancer microenvironment, offering potential therapeutic targets for ovarian cancer treatment.

Long-read sequencing reveals oncogenic mechanism of HPV-human fusion transcripts in cervical cancer

Integration of high-risk human papillomavirus (HPV) into the host genome is a crucial event for the development of cervical cancer, however, the underlying mechanism of HPV integration-driven carcinogenesis remains unknown. Here, we performed long-read RNA sequencing on 12 high-grade squamous intraepithelial lesions (HSIL) and cervical cancer patients, including 3 pairs of cervical cancer and corresponding para-cancerous tissue samples to investigate the full-length landscape of cross-species genome integrations. In addition to massive unannotated isoforms, transcriptional regulatory events, and gene chimerism, more importantly, we found that HPV-human fusion events were prevalent in HPV-associated cervical cancers. Combined with the genome data, we revealed the existence of a universal transcription pattern in these fusion events, whereby structurally similar fusion transcripts were generated by specific splicing in E6 and a canonical splicing donor site in E1 linking to various human splicing acceptors. Highly expressed HPV-human fusion transcripts, eg, HPV16 E6*I-E7-E1

The unveiled mosaic of intra-tumor heterogeneity in ovarian cancer through spatial transcriptomic technologies: A systematic review

Epithelial ovarian cancer is a significant global health issue among women. Diagnosis and treatment pose challenges due to difficulties in predicting patient responses to therapy, primarily stemming from gaps in understanding tumor chemoresistance mechanisms. Recent advancements in transcriptomic technologies like single-cell RNA sequencing and spatial transcriptomics have greatly improved our understanding of ovarian cancer intratumor heterogeneity and tumor microenvironment composition. Spatial transcriptomics, in particular, comprises a plethora of technologies that enable the detection of hundreds of transcriptomes and their spatial distribution within a histological section, facilitating the study of cell types, states, and interactions within the tumor and its microenvironment. Studies investigating the spatial distribution of gene expression in ovarian cancer masses have identified specific features that impact prognosis and therapy outcomes. Emerging evidence suggests that specific spatial patterns of tumor cells and their immune and non-immune microenvironment significantly influence therapy response, as well as the behavior and progression of primary tumors and metastatic sites. The importance of spatially contextualizing ovarian cancer transcriptomes is underscored by these findings, which will advance our understanding and therapeutic approaches for this complex disease.

Noninvasive early differential diagnosis and progression monitoring of ovarian cancer using the copy number alterations of plasma cell-free DNA

Ovarian cancer (OV) is the most lethal gynecological malignancy and requires improved early detection methods and more effective intervention to achieve a better prognosis. The lack of sensitive and noninvasive biomarkers with clinical utility remains a challenge. Here, we conducted a genome-wide copy number variation (CNV) profiling analysis using low-coverage whole genome sequencing (LC-WGS) of plasma cfDNA in patients with nonmalignant and malignant ovarian tumors and identified 10 malignancy-specific and 12 late-stage-specific CNV markers from plasma cfDNA LC-WGS data. Concordance analysis indicated a significant correlation of identified CNV markers between CNV profiles of plasma cfDNA and tissue DNA (Pearson's r = 0.64, P = 0.006 for the TCGA cohort and r = 0.51, P = 0.04 for the Dariush cohort). By leveraging these specific CNV markers and machine learning algorithms, we developed robust predictive models showing excellent performance in distinguishing between malignant and nonmalignant ovarian tumors with F1-scores of 0.90 and ranging from 0.75 to 0.99, and prediction accuracy of 0.89 and ranging from 0.66 to 0.98, respectively, as well as between early- and late-stage ovarian tumors with F1-scores of 0.84 and ranging from 0.61 to 1.00, and prediction accuracy of 0.82 and ranging from 0.63 to 0.96 in our institute cohort and other external validation cohorts. Furthermore, we also discovered and validated certain CNV features associated with survival outcomes and platinum-based chemotherapy response in multicenter cohorts. In conclusion, our study demonstrated the clinical utility of CNV profiling in plasma cfDNA using LC-WGS as a cost-effective and accessible liquid biopsy for OV.