Using SAAS-CNV to Detect and Characterize Somatic Copy Number Alterations in Cancer Genomes from Next Generation Sequencing and SNP Array Data

Descriptive Statistics and Genome-Wide Copy Number Analysis of Milk Production Traits of Jiangsu Chinese Holstein Cows

Milk production traits are the most important quantitative economic traits in dairy cow production; improving the yield and quality of milk is an important way to ensure the production efficiency of the dairy industry. This study carried out a series of in-depth statistical genetics studies and molecular analyses on the Chinese Holstein cows in the Jiangsu Province, such as descriptive statistics and copy number variation analysis. A genetic correlation, phenotypic correlation, and descriptive statistical analysis of five milk production traits (milk yield, milk fat percentage, milk fat yield, milk protein percentage, and milk protein yield) of the dairy cows were analyzed using the SPSS and DMU software. Through quality control, 4173 cows and their genomes were used for genomic study. Then, SNPs were detected using DNA chips, and a copy number variation (CNV) analysis was carried out to locate the quantitative trait loci (QTL) of the milk production traits by Perl program software Penn CNV and hidden Markov model (HMM). The phenotypic means of the milk yield, milk fat percentage, milk fat mass, milk protein percentage, and milk protein mass at the first trimester were lower than those at the other trimesters by 8.821%, 1.031%, 0.930%, 0.003%, and 0.826%, respectively. The five milk production traits showed a significant phenotypic positive correlation (p < 0.01) and a high genetic positive correlation among the three parities. Based on the GGPBovine 100 K SNP data, QTL-detecting research on the fist-parity milk performance of dairy cows was carried out via the CNV. We identified 1731 CNVs and 236 CNVRs in the 29 autosomes of 984 Holstein dairy cows, and 19 CNVRs were significantly associated with the milk production traits (p < 0.05). These CNVRs were analyzed via a bioinformatics analysis; a total of 13 gene ontology (GO) terms and 20 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were significantly enriched (p < 0.05), and these terms and pathways are mainly related to lipid metabolism, amino acid metabolism, and cellular catabolic processes. This study provided a theoretical basis for the molecular-marker-assisted selection of dairy cows by developing descriptive statistics on the milk production traits of dairy cows and by locating the QTL and functional genes that affect the milk production traits of first-born dairy cows. The results describe the basic status of the milk production traits of the Chinese Holstein cows in Jiangsu and locate the QTL and functional genes that affect the milk production traits of the first-born cows, providing a theoretical basis for the molecular-marker-assisted selection of dairy cows.

Identification and Validation of SNP-Containing Genes With Prognostic Value in Gastric Cancer via Integrated Bioinformatics Analysis

Background

Gastric cancer is one of the most common malignancies worldwide. Although the diagnosis and treatment of this disease have substantially improved in recent years, the five-year survival rate of gastric cancer is still low due to local recurrence and distant metastasis. An in-depth study of the molecular pathogenesis of gastric cancer and related prognostic markers will help improve the quality of life and prognosis of patients with this disease. The purpose of this study was to identify and verify key SNPs in genes with prognostic value for gastric cancer.

Methods

SNP-related data from gastric cancer patients were obtained from The Cancer Genome Atlas (TCGA) database, and the functions and pathways of the mutated genes were analyzed using DAVID software. A protein-protein interaction (PPI) network was constructed using the STRING database and visualized by Cytoscape software, and molecular complex detection (MCODE) was used to screen the PPI network to extract important mutated genes. Ten hub genes were identified using cytoHubba, and the expression levels and the prognostic value of the central genes were determined by UALCAN and Kaplan-Meier Plotter. Finally, quantitative PCR and Western blotting were used to verify the expression of the hub genes in gastric cancer cells.

Results

From the database, 945 genes with mutations in more than 25 samples were identified. The PPI network had 360 nodes and 1616 edges. Finally, cytoHubba identified six key genes (TP53, HRAS, BRCA1, PIK3CA, AKT1, and SMARCA4), and their expression levels were closely related to the survival rate of gastric cancer patients.

Conclusion

Our results indicate that TP53, HRAS, BRCA1, PIK3CA, AKT1, and SMARCA4 may be key genes for the development and prognosis of gastric cancer. Our research provides an important bioinformatics foundation and related theoretical foundation for further exploring the molecular pathogenesis of gastric cancer and evaluating the prognosis of patients.

USP8 and TP53 Drivers are Associated with CNV in a Corticotroph Adenoma Cohort Enriched for Aggressive Tumors

CONTEXT

Pituitary corticotroph adenomas are rare tumors that can be associated with excess adrenocorticotropin (ACTH) and adrenal cortisol production, resulting in the clinically debilitating endocrine condition Cushing disease. A subset of corticotroph tumors behave aggressively, and genomic drivers behind the development of these tumors are largely unknown.

OBJECTIVE

To investigate genomic drivers of corticotroph tumors at risk for aggressive behavior.

DESIGN

Whole-exome sequencing of patient-matched corticotroph tumor and normal deoxyribonucleic acid (DNA) from a patient cohort enriched for tumors at risk for aggressive behavior.

SETTING

Tertiary care center.

PATIENTS

Twenty-seven corticotroph tumors from 22 patients were analyzed. Twelve tumors were macroadenomas, of which 6 were silent ACTH tumors, 2 were Crooke's cell tumors, and 1 was a corticotroph carcinoma.

INTERVENTION

Whole-exome sequencing.

MAIN OUTCOME MEASURE

Somatic mutation genomic biomarkers.

RESULTS

We found recurrent somatic mutations in USP8 and TP53 genes, both with higher allelic fractions than other somatic mutations. These mutations were mutually exclusive, with TP53 mutations occurring only in USP8 wildtype (WT) tumors, indicating they may be independent driver genes. USP8-WT tumors were characterized by extensive somatic copy number variation compared with USP8-mutated tumors. Independent of molecular driver status, we found an association between invasiveness, macroadenomas, and aneuploidy.

CONCLUSIONS

Our data suggest that corticotroph tumors may be categorized into a USP8-mutated, genome-stable subtype versus a USP8-WT, genome-disrupted subtype, the latter of which has a TP53-mutated subtype with high level of chromosome instability. These findings could help identify high risk corticotroph tumors, namely those with widespread CNV, that may need closer monitoring and more aggressive treatment.

Combining targeted sequencing and ultra-low-pass whole-genome sequencing for accurate somatic copy number alteration detection

This study investigated the feasibility of combining targeted sequencing and ultra-low-pass whole-genome sequencing (ULP-WGS) for improved somatic copy number alteration (SCNA) detection, due to its role in tumorigenesis and prognosis. Cerebrospinal fluid and matched blood samples were obtained from 29 patients with brain metastasis derived from lung cancer. Samples were subjected to targeted sequencing (genomic coverage: 300 kb) and 2×ULP-WGS. The SCNA was detected by the CTLW_CNV, Control-FreeC, and CNVkit methods and their accuracy was analyzed. Eighteen tumor samples showed consistent SCNA results between the three methods, while a small fraction of samples resulted in different SCNA estimations. Further analysis indicated that consistency of SCNA highly correlated with the difference of baseline depth (normalized depth of regions without SCNA events) estimation between methods. Conflict Index showed that CTLW_CNV significantly improved the accuracy of SCNA detection through precise baseline depth estimation. CTLW_CNV combines targeted sequencing and ULP-WGS for improved SCNA detection. The improvement in detection accuracy is mainly due to a refined baseline depth estimation, guided by single-nucleotide polymorphism allele frequencies within the deeply sequenced region (targeted sequencing). This method is especially suitable for tumor samples with biased aneuploidy, a previously under-estimated genomic characteristic across different cancer types.