Imaging genetic association analysis of triple-negative breast cancer based on the integration of prior sample information

Singular Value Decomposition-Driven Non-negative Matrix Factorization with Application to Identify the Association Patterns of Sarcoma Recurrence

Sarcomas are malignant tumors from mesenchymal tissue and are characterized by their complexity and diversity. The high recurrence rate making it important to understand the mechanisms behind their recurrence and to develop personalized treatments and drugs. However, previous studies on the association patterns of multi-modal data on sarcoma recurrence have overlooked the fact that genes do not act independently, but rather function within signaling pathways. Therefore, this study collected 290 whole solid images, 869 gene and 1387 pathway data of over 260 sarcoma samples from UCSC and TCGA to identify the association patterns of gene-pathway-cell related to sarcoma recurrences. Meanwhile, considering that most multi-modal data fusion methods based on the joint non-negative matrix factorization (NMF) model led to poor experimental repeatability due to random initialization of factorization parameters, the study proposed the singular value decomposition (SVD)-driven joint NMF model by applying the SVD method to calculate initialized weight and coefficient matrices to achieve the reproducibility of the results. The results of the experimental comparison indicated that the SVD algorithm enhances the performance of the joint NMF algorithm. Furthermore, the representative module indicated a significant relationship between genes in pathways and image features. Multi-level analysis provided valuable insights into the connections between biological processes, cellular features, and sarcoma recurrence. In addition, potential biomarkers were uncovered, while various mechanisms of sarcoma recurrence were identified from an imaging genetic perspective. Overall, the SVD-NMF model affords a novel perspective on combining multi-omics data to explore the association related to sarcoma recurrence.

CPSF3 regulates alternative polyadenylation of CNIH2 to promote esophageal squamous cell carcinoma progression

Alternative polyadenylation (APA), an important post-transcriptional regulatory mechanism, is aberrantly activated in cancer，but how APA functions in tumorigenesis remains elusive. We analyzed APA events in RNA-seq data in TCGA and reported 3'UTR alterations associated with esophageal squamous cell carcinoma (ESCC) patient prognosis and gene expression changes involving loss of tumor-suppressive miRNA binding sites. Moreover, we investigated the expression and function of cleavage and polyadenylation specific factor 3 (CPSF3), a key APA regulator in ESCC. By immunohistochemistry and qRT-PCR, we found that CPSF3 was highly expressed in ESCC tissues and associated with poor patient prognosis. Overexpression of CPSF3 enhanced, while knockdown of CPSF3 inhibited ESCC cell proliferation and migration in vitro and in vivo, as determined by colony formation, transwell assays and animal experiments. Iso-Seq and RNA-seq data analysis indicated that knockdown of CPSF3 favored use of the distal poly (A) site in the 3'UTR of Cornichon family AMPA receptor auxiliary protein 2 (CNIH2), resulting in a long-3'UTR CNIH2 isoform that produced less CNIH2 protein due to miR-125a-5p targeting and downregulating CNIH2 mRNA through a miR-125a-5p binding site in the long CNIH2 mRNA 3'UTR. Moreover, CPSF3-induced ESCC tumorigenicity was mediated by CNIH2. Taken together, CPSF3 promotes ESCC progression by upregulating CNIH2 expression through loss of miR-125a-5p-mediated CNIH2 repression through alternative splicing and polyadenylation of the CNIH2 mRNA 3'UTR.