Pre-existing cancer cells and induced fibroblasts are key cells for early chemoresistance in ovarian cancer
Gu, L.; He, S.; Wu, L.; Zeng, Y.; Zhang, Y.; Zheng, C.; Wu, C.; Xu, H.; Zhang, X.; Shen, H.; Yao, S.; Ren, Y.; Yang, G.
Show abstract
Chemoresistance has long been a significant but unresolved issue in the treatment of various cancers, including the most deadly gynecological cancer, the high-grade serous ovary cancer (HGSOC). In this study, single nuclei transcriptome analyses were utilized to identify key cells and core networks for chemoresistance in HGSOC patients with different early responses to platinum-based chemotherapy at the single-cell level. Biomarkers for chemoresistance were also screened using bulk transcriptome data from independent cohorts with larger sample sizes. A total of 62,482 single cells from six samples were analyzed, revealing that chemoresistant cancer cells (Epithelial cells_0) pre-existed within individual patient before treatment. Two network modules formed with hub genes such as hormone-related genes (ESR1 and AR), insulin-related genes (INSR and IGF1R), and CTNNB1, were significantly overexpressed in these cells in the chemoresistant patient. BMP1 and TPM2 could be promise biomarkers in identifying chemoresistant patients before chemotherapy using bulk transcriptome data. Additionally, chemotherapy-induced fibroblasts (Fibroblasts_01_after) emerged as key stromal cells for chemoresistance. One network module containing one subnetwork formed by cholesterol biosynthesis-related genes and one subnetwork formed by cancer-related genes such as STAT3 and MYC, was significantly overexpressed in these cells in the chemoresistant patient. Notably, the NAMPT-INSR was the most prioritized ligand-receptor pair for cells interacting with Fibroblasts_01_after cells and Epithelial cells_0 cells to drive the up-regulation of their core genes, including IL1R1, MYC and INSR itself. Our findings deepen the understandings about mechanisms of early chemoresistance in HGSOC patients.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- BNIP3 upregulation characterizes cancer cell subpopulation with increased fitness and proliferation 96%
- Establishment of a prognosis prediction model based on pyroptosis-related signatures associated with the immune microenvironment and molecular heterogeneity in clear cell renal carcinoma 95%
- Pyroptosis-related gene signatures can robustly diagnose skin cutaneous melanoma and predict the prognosis 95%
Similar papers in this journal
- Unveiling chemotherapy-induced immune landscape remodeling and metabolic reprogramming in lung adenocarcinoma by scRNA-sequencing 97%
- Multi-gradient Permutation Survival Analysis Identifies Mitosis and Immune Signatures Steadily Associated with Cancer Patient Prognosis 96%
- Mapping of single-cell landscape of acral melanoma and analysis of molecular regulatory network of tumor microenvironment 95%
Similar papers in this journal
- A 'one-two punch' therapy strategy to target chemoresistance in estrogen receptor positive breast cancer 97%
- PMAIP1-Mediated Glucose Metabolism and its Impact on the Tumor Microenvironment in Breast Cancer: Integration of Multi-Omics Analysis and Experimental Validation 96%
- Enhancing Chemotherapy Response Prediction via Matched Colorectal Tumor-Organoid Gene Expression Analysis and Network-Based Biomarker Selection 94%
Similar papers in this journal
- The Epithelial and Stromal Immune Microenvironment in Gastric Cancer: A Comprehensive Analysis Reveals Prognostic Factors with Digital Cytometry 96%
- The MEK1/2 pathway as a therapeutic target in high-grade serous ovarian carcinoma 95%
- Omics integration analyses reveal the early evolution of malignancy in breast cancer 94%
Similar papers in this journal
- Essential role of PLD2 in hypoxia-induced stemness and therapy resistance in ovarian tumors 95%
- Long-term patient-derived ovarian cancer organoids closely recapitulate tumor of origin and clinical response 95%
- FSTL3 is a biomarker of poor prognosis and is associated with immunotherapy resistance in ovarian cancer. 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.