Background: Breast cancer (BC) is the most common malignancy in women with high heterogeneity. The heterogeneity of cancer cells from different BC subtypes has not been thoroughly characterized and there is still no valid biomarker for predicting the prognosis of BC patients in clinical practice. Methods: Cancer cells were identified by calculating single cell copy number variation using the inferCNV algorithm. SCENIC was utilized to infer gene regulatory networks. CellPhoneDB software was used to analyze the intercellular communications in different cell types. Survival analysis, univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were used to construct subtype specific prognostic models. Results: Triple-negative breast cancer (TNBC) has a higher proportion of cancer cells than subtypes of HER2+ BC and luminal BC, and the specifically upregulated genes of the TNBC subtype are associated with antioxidant and chemical stress resistance. Key transcription factors (TFs) of tumor cells for three subtypes varied, and most of the TF-target genes are specifically upregulated in corresponding BC subtypes. The intercellular communications mediated by different receptor–ligand pairs lead to an inflammatory response with different degrees in the three BC subtypes. We establish a prognostic model containing 10 genes (risk genes: ATP6AP1, RNF139, BASP1, ESR1 and TSKU; protective genes: RPL31, PAK1, STARD10, TFPI2 and SIAH2) for luminal BC, seven genes (risk genes: ACTR6 and C2orf76; protective genes: DIO2, DCXR, NDUFA8, SULT1A2 and AQP3) for HER2+ BC, and seven genes (risk genes: HPGD, CDC42 and PGK1; protective genes: SMYD3, LMO4, FABP7 and PRKRA) for TNBC. Three prognostic models can distinguish high-risk patients from low-risk patients and accurately predict patient prognosis. Conclusions: Comparative analysis of the three BC subtypes based on cancer cell heterogeneity in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy for BC patients.
Many dysregulated microRNAs (miRNAs) have been suggested to serve as oncogenes or tumor suppressors to act as diagnostic and prognostic factors for HCC patients. However, the dysregulated mechanisms of miRNAs in HCC remain largely unknown. Herein, we firstly identify 114 disordered mature miRNAs in HCC, 93 of them are caused by dysregulated transcription factors, and 10 of them are driven by the DNA methylation of their promoter regions. Secondly, we find that seven up-regulated miRNAs (miR-9-5p, miR-452-5p, miR-452-3p, miR-1180-3p, miR-4746-5p, miR-3677-3 and miR-4661-5p) can promote tumorigenesis via inhibiting multiple tumor suppressor genes participated in metabolism, which may act as oncogenes, and seven down-regulated miRNAs (miR-99-5p, miR-5589-5p, miR-5589-3p, miR-139-5p, miR-139-3p, miR-101-3p and miR-125b-5p) can suppress abnormal cell proliferation via suppressing a number of oncogenes involved in cancer-related pathways, which may serve as tumor suppressors. Thirdly, our findings reveal a mechanism that transcription factor and miRNA interplay can form various regulatory loops to synergistically control the occurrence and development of HCC. Finally, our results demonstrate that this key transcription factor FOXO1 can activate a certain number of tumor suppressor miRNAs to improve the survival of HCC patients, suggesting FOXO1 as an effective therapeutic target for HCC patients. Overall, our study not only reveals the dysregulated mechanisms of miRNAs in HCC, but provides several novel prognostic biomarkers and potential therapeutic targets for HCC patients.
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