[Detection of preoperative chemoradiotherapy sensitivity molecular characteristics of rectal cancer by transcriptome second generation sequencing]

Characteristics of Hepatitis B virus integration and mechanism of inducing chromosome translocation

Hepatitis B virus (HBV) integration is closely associated with the onset and progression of tumors. This study utilized the DNA of 27 liver cancer samples for high-throughput Viral Integration Detection (HIVID), with the overarching goal of detecting HBV integration. KEGG pathway analysis of breakpoints was performed using the ClusterProfiler software. The breakpoints were annotated using the latest ANNOVAR software. We identified 775 integration sites and detected two new hotspot genes for virus integration, N4BP1 and WASHP, along with 331 new genes. Furthermore, we conducted a comprehensive analysis to determine the critical impact pathways of virus integration by combining our findings with the results of three major global studies on HBV integration. Meanwhile, we found common characteristics of virus integration hotspots among different ethnic groups. To specify the direct impact of virus integration on genomic instability, we explained the causes of inversion and the frequent occurrence of translocation due to HBV integration. This study detected a series of hotspot integration genes and specified common characteristics of critical hotspot integration genes. These hotspot genes are universal across different ethnic groups, providing an effective target for better research on the pathogenic mechanism. We also demonstrated more comprehensive key pathways affected by HBV integration and elucidated the mechanism for inversion and frequent translocation events due to virus integration. Apart from the great significance of the rule of HBV integration, the current study also provides valuable insights into the mechanism of virus integration.

Comprehensive Profiling of Genomic and Transcriptomic Differences between Risk Groups of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Lung cancer is the second most frequently diagnosed cancer type and responsible for the highest number of cancer deaths worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are subtypes of non-small-cell lung cancer which has the highest frequency of lung cancer cases. We aimed to analyze genomic and transcriptomic variations including simple nucleotide variations (SNVs), copy number variations (CNVs) and differential expressed genes (DEGs) in order to find key genes and pathways for diagnostic and prognostic prediction for lung adenocarcinoma and lung squamous cell carcinoma. We performed a univariate Cox model and then lasso-regularized Cox model with leave-one-out cross-validation using The Cancer Genome Atlas (TCGA) gene expression data in tumor samples. We generated 35- and 33-gene signatures for prognostic risk prediction based on the overall survival time of the patients with LUAD and LUSC, respectively. When we clustered patients into high- and low-risk groups, the survival analysis showed highly significant results with high prediction power for both training and test datasets. Then, we characterized the differences including significant SNVs, CNVs, DEGs, active subnetworks, and the pathways. We described the results for the risk groups and cancer subtypes separately to identify specific genomic alterations between both high-risk groups and cancer subtypes. Both LUAD and LUSC high-risk groups have more downregulated immune pathways and upregulated metabolic pathways. On the other hand, low-risk groups have both up- and downregulated genes on cancer-related pathways. Both LUAD and LUSC have important gene alterations such as CDKN2A and CDKN2B deletions with different frequencies. SOX2 amplification occurs in LUSC and PSMD4 amplification in LUAD. EGFR and KRAS mutations are mutually exclusive in LUAD samples. EGFR, MGA, SMARCA4, ATM, RBM10, and KDM5C genes are mutated only in LUAD but not in LUSC. CDKN2A, PTEN, and HRAS genes are mutated only in LUSC samples. The low-risk groups of both LUAD and LUSC tend to have a higher number of SNVs, CNVs, and DEGs. The signature genes and altered genes have the potential to be used as diagnostic and prognostic biomarkers for personalized oncology.