Genes, Chromosomes & Cancer最新文献

Sex cord-stromal tumors are rare in pediatric patients. Leydig cell tumors are a rare subset of sex cord-stromal tumors characterized by unique molecular alterations, including TERT fusions and mutations of CTNNB1, FOXO4, TP53, NBN, MTOR, BAP1, MEN1, and CREBBP. We report a case of a testicular Leydig cell tumor with an EWSR1::CREM fusion, which to our knowledge has not been previously reported in this setting.

Small round cell sarcomas (SRCSs) constitute a heterogeneous group of high-grade tumors with a poor prognosis, predominantly affecting children and young adults. According to the 2020 WHO Soft Tissue Tumor classification, SRCSs are categorized into Ewing sarcoma, round cell sarcoma with EWSR1-non-ETS fusions, CIC-rearranged sarcoma, and sarcoma with BCOR genetic alterations. Herein, we report a case of a 10-month-old boy presenting with a progressively enlarging left lumbar mass. Histopathological examination revealed a well-demarcated lesion composed of small, round to oval cells with scant cytoplasm and mildly irregular nuclei. Immunohistochemical staining demonstrated strong, diffuse positivity for S100 and SOX10, indicating neurocristic differentiation. Next-generation sequencing identified an in-frame fusion between MAZ exon 3 on chromosome 16 and NCOA2 exon 12 on chromosome 8. Fluorescence in situ hybridization (FISH) confirmed a break-apart signal at the NCOA2 locus. To the best of our knowledge, this represents the first documented instance of an NCOA2 rearrangement involving MAZ in SRCSs. This case broadens the molecular spectrum of SRCSs, highlights the importance of incorporating molecular techniques into diagnostic workflows, and may have implications for future diagnostic and therapeutic strategies.

Identifying genes involved in cancer is crucial for understanding the underlying molecular mechanisms of the disease and developing effective treatment strategies. Differential expression analysis (DEA) is the predominant method used to identify cancer-related genes. This approach involves comparing gene expression levels between different samples, such as cancerous and non-cancerous tissues, to identify genes that are significantly upregulated or downregulated in cancer. DEA is based on the commonly believed assumption that genes upregulated in cancerous tissues have the potential to function as oncogenes. Their expression levels often correlate with cancer advancement and unfavorable prognosis, whereas downregulated genes display the opposite correlation. However, contrary to the prevailing belief, our analysis utilizing The Cancer Genome Atlas (TCGA) databases revealed that the upregulated or downregulated genes in cancer do not always align with cancer progression or prognosis. These findings emphasize the need for alternative approaches for identifying cancer-related genes that may be more accurate and effective. To address this need, we compared the effectiveness of machine learning (ML) methods with that of traditional DEA in the identification of cancer-related genes. ML algorithms have the advantage of being able to analyze large-scale genomic data and identify complex patterns that may go unnoticed by traditional methods. Our results demonstrated that ML methods significantly outperformed DEA in the screening of cancer-related genes.

Breast cancer, a molecularly heterogeneous disease, is classified into hormone receptor-positive luminal breast cancer (LBC), human epidermal growth factor receptor 2-positive breast cancer, and triple-negative breast cancer (TNBC). Precursor microRNAs (pre-miRNAs), typically form hairpin structures with a length from 65 to 80 bases, are shown to play crucial roles in breast cancer carcinogenesis. We hypothesized that these pre-miRNAs could have been sequenced in total RNA sequencing (RNA-seq) and developed a novel algorithm to profile pre-miRNAs from raw total RNA-seq data. A total of 907 breast cancer samples curated by Malaysian Breast Cancer Genetic Study (MyBrCa) were profiled using this algorithm and a comparison was made between pre-miRNA profiles and mature miRNA profiles obtained from The Cancer Genome Atlas (TCGA) dataset. We explored differentially expressed pre-miRNAs in TNBC in comparison to LBC and conducted downstream functional analyses of the target genes. A prognostic signature was built by LASSO–Cox regression on selected pre-miRNAs and validated internally and externally by MyBrCa and TCGA datasets, respectively. As a result, 10 common differentially expressed pre-miRNAs were identified. Functional analyses of these pre-miRNAs captured certain aggressive TNBC behaviors. Importantly, a pre-miRNA signature composed of 4 out of these 10 pre-miRNAs significantly prognosticated the breast cancer patients in the MyBrCa cohort and TCGA cohort, independent of conventional prognostic factors. In conclusion, this novel algorithm allows profiling pre-miRNAs from raw total RNA-seq data, which could be cross-validated with mature miRNA profiles for cross-platform comparison. The findings of this study underscore the importance of pre-miRNAs in breast cancer carcinogenesis and as prognostic factors.