Biological differences between normal and cancer-associated fibroblasts in breast cancer.

Background: Cancer-associated fibroblasts (CAFs) constitute the primary constituents of the tumor microenvironment (TME) and exert significant influences on cancer progression. However, adequate comprehension of CAF profiles in breast cancer, as well as the precise mechanisms underlying their promotion of cancer, remains lacking. Objectives: To discerns the biological differences between normal fibroblasts (NFs) and CAFs in breast cancer and explore the underlying mechanism. Methods: Three pairs of CAFs and NFs were isolated from breast cancer patients of diverse subtypes who had not undergone prior radiotherapy or chemotherapy. Morphological characteristics of CAFs and NFs were assessed through optical and electron microscopy, their biological attributes were examined using cell counting kits and transwell assays, and their impact on breast cancer cells was simulated using a coculture system. Furthermore, the miRNA profiles of CAFs and NFs were sequenced via an Illumina HiSeq 2500 platform. Results: CAFs exhibited higher growth rate and motility than NFs and a stronger potential to promote the malignancy of breast cancer cells. RNA sequencing of both NFs and CAFs revealed differentially expressed miRNAs with notable variability among distinct patients within their NFs and CAFs, while the enrichment of the target genes of differentially expressed miRNAs within both GO terms and KEGG pathways demonstrated significant similarity across patients with different profiles. Conclusion: CAFs have greater malignancy and higher potential to influence the growth, migration, invasion and chemoresistance of cocultured breast cancer cells than NFs. In addition, the miRNAs that are differentially expressed in CAFs when compared to NFs display substantial variability across patients with distinct breast cancer subtypes, while the enrichment of target genes regulated by these miRNAs, within GO terms and KEGG pathways, remains remarkably consistent among patients with varying profiles.

Cancer-associated fibroblasts in breast cancer: Challenges and opportunities.

The tumor microenvironment is proposed to contribute substantially to the progression of cancers, including breast cancer. Cancer-associated fibroblasts (CAFs) are the most abundant components of the tumor microenvironment. Studies have revealed that CAFs in breast cancer originate from several types of cells and promote breast cancer malignancy by secreting factors, generating exosomes, releasing nutrients, reshaping the extracellular matrix, and suppressing the function of immune cells. CAFs are also becoming therapeutic targets for breast cancer due to their specific distribution in tumors and their unique biomarkers. Agents interrupting the effect of CAFs on surrounding cells have been developed and applied in clinical trials. Here, we reviewed studies examining the heterogeneity of CAFs in breast cancer and expression patterns of CAF markers in different subtypes of breast cancer. We hope that summarizing CAF-related studies from a historical perspective will help to accelerate the development of CAF-targeted therapeutic strategies for breast cancer.

A novel five-lncRNA signature panel improves high-risk survival prediction in patients with cholangiocarcinoma.

Cholangiocarcinoma (CCA) is a fatal disease with dismal survival rates. Long non-coding RNA (lncRNA) expression profiling as potential prognostic biomarkers play critical roles in tumor initiation, development, and poor prognosis. Identifying specific lncRNA to predict the prognosis of CCA patients in the early stages is very important for improving a patient's survival. In the current study, we aimed to establish a novel risk-stratification lncRNA signature panel in CCA. The initial lncRNA discovery was identified in The Cancer Genome Atlas database (TCGA cohort). The Cox regression analysis was used to establish the lncRNA prognostic model and the receiver operating characteristic (ROC) curve analysis was performed to assess the specificity and sensitivity of the model. This was followed by independent validation of the lncRNA signature in the CCA patients from the First Affiliated Hospital of Wenzhou Medical University (WMU cohort). Furthermore, by using the Gene Ontology function and Kyoto Encyclopedia Gene and Genome pathway enrichment analysis, we explored the potential function of prognosis lncRNA. Finally, five lncRNA (HULC; AL359715.5; AC006504.8; AC090114.2; AP00943.4) were screened to establish the predictive model that significantly associated with poor overall survival(HR:4.879;95%CI,1.587-14.996;p=0.006). This five-lncRNA signature model showed excellent accuracy in the TCGA cohort (AUC=0.938), and also robustly predicted survival in the validation WMU cohort(AUC=0.816). Functional enrichment analysis suggested prognostic lncRNA was primarily associated with CCA-related biological processes. Our data established a novel lncRNA signature model for CCA risk-stratification and robust identification of CCA patients with poor molecular genotypes. Moreover, it revealed new molecular mechanisms of CCA.