bioRxivGenomicsPreprint
Meiosis related cancer-testis genes correlate with tumor aneuploidy and immune infiltration in 21 cancer types
Xuewei WangiD, Yide XuiD, Yu‐Ting ChangiD, Sihan JuiD, Yang LiuiD, Qufei Qian, Yao Chen, Shuaizhou Chen, Na QiniD, Zijian MaiD, Juncheng DaiiD, Hongxia MaiD, Jin Guangfu, Erbao ZhangiD, Cheng WangiD, Zhibin HuiD
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Abstract
The meiosis stage of spermatogenesis and the aneuploidy generation in tumorigenesis are highly linked. Cancer-testis genes (CT genes) were specifically expressed in testis and cancers and may serve as the molecular basis of the shared features between spermatogenesis and tumorigenesis. In the present study, we integrated multi-omics data from bulk samples and cell lines, and single cell RNA-Seq data from testis and two tumors to systematically investigate the association between CT genes and aneuploidy. After ranking genes according to their association with aneuploidy level, we found that CT genes, especially CT genes specifically expressed in meiosis, showed a consistently positive correlation with aneuploidy and homologous recombination deficiency (HRD) level. Similar results were also found in the CT non-coding genes. Then, we constructed a regulatory network based on the single cell RNA-seq data and revealed that the gain of accelerator transcriptional factors (TFs) E2F7 and E2F8 and the loss of the stabilizer TFs RFX2 and NFYA aberrantly activated CT genes in the cancers and were associated with the increase of HRD level. Finally, we found that the association between CT genes and cytotoxic infiltrating lymphocytes can be influenced by the HRD level. In sum, our results revealed the evidence of pseudomeiotic functions of CT genes in the aneuploidy generation process in the cancer cells. The results may help illuminate the origin of aneuploidy in cancers and guide future immunotherapy targeting CT antigens.
- organism: human
- assay: bulk RNA-seq
- assay: scRNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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