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Background: Hepatitis B virus (HBV) infection is one of the major causes of chronic liver disease, which progresses from chronic hepatitis B (CHB) to fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Early detection and laboratory-based screening of hepatocellular carcinoma are still major challenges. This study was undertaken to determine whether the cancer hallmark gene signatures that are released into circulation as circulating tumour DNA (ctDNA) can be used as a liquid biopsy marker for screening, early detection, and prognosis of HCC.Methods: A total of 130 subjects, including HBV-HCC (n = 80), HBV-cirrhotic and non-cirrhotic (n = 35), and healthy (n = 15) controls, were evaluated for TP53 and beta-catenin (CTNNB1) gene hotspot mutations in ctDNA by Sanger-based cycle sequencing and droplet digital PCR (ddPCR) assays. Mutation detection frequency, percentage mutant fractions, and their association with tumour stage, mortality, and smoking habits were determined.Results: Sanger-based cycle sequencing was carried out for 32 HCC patients. Predict SNP Tools analysis indicated several pathogenic driver mutations in the ctDNA sequence, which include p.D228N, p.C229R, p.H233R, p.Y234D, p.S240T, p.G245S, and p.R249M for TP53 gene exon 7 and p.S33T for CTNNB1 gene exon 3. The TP53 c.746G>T (p.R249M) mutation was detected predominately (25% cases) by sequencing, but there was no dominant mutation at position c.747G>T (p.R249S) that was reported for HBV-HCC patients. A dual-probe ddPCR assay was developed to determine mutant and wild-type copy numbers of TP53 (p.R249M and p.R249S) and CTNNB1 (p.S45P) and their percentage mutant fraction in all 130 subjects. The TP53 R249M and CTNNB1 S45P mutations were detected in 31.25% and 26.25% of HCC patients, respectively, with a high mutant-to-wild-type fraction percentage (1.81% and 1.73%), which is significant as compared to cirrhotic and non-cirrhotic patients. Poor survival was observed in HCC patients with combined TP53 and CTNNB1 gene driver mutations. The TP53 R249M mutation was also significantly (p < 0.0001) associated with smoking habits (OR, 11.77; 95% CI, 3.219–36.20), but not the same for the TP53 R249S mutation.Conclusion: Screening of ctDNA TP53 and CTNNB1 gene mutations by ddPCR may be helpful for early detection and identifying the risk of HCC progression.

Introduction: Driver mutations play a critical role in the occurrence and development of human cancers. Most studies have focused on missense mutations that function as drivers in cancer. However, accumulating experimental evidence indicates that synonymous mutations can also act as driver mutations. Methods: Here, we proposed a computational method called PredDSMC to accurately predict driver synonymous mutations in human cancers. We first systematically explored four categories of multimodal features, including sequence features, splicing features, conservation scores, and functional scores. Further feature selection was carried out to remove redundant features and improve the model performance. Finally, we utilized the random forest classifier to build PredDSMC. Results: The results of two independent test sets indicated that PredDSMC outperformed the state-of-the-art methods in differentiating driver synonymous mutations from passenger mutations. Discussion: In conclusion, we expect that PredDSMC, as a driver synonymous mutation prediction method, will be a valuable method for gaining a deeper understanding of synonymous mutations in human cancers. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer became the third most common cancer among women, and genome characterization of cervical cancer patients has revealed the extensive complexity of molecular alterations. However, identifying driver mutation and depicting molecular classification in cervical cancer remain a challenge.Methods: We performed an integrative multi-platform analysis of a cervical cancer cohort from The Cancer Genome Atlas (TCGA) based on 284 clinical cases and identified the driver genes and possible molecular classification of cervical cancer.Results: Multi-platform integration showed that cervical cancer exhibited a wide range of mutation. The top 10 mutated genes were TTN, PIK3CA, MUC4, KMT2C, MUC16, KMT2D, SYNE1, FLG, DST, and EP300, with a mutation rate from 12 to 33%. Applying GISTIC to detect copy number variation (CNV), the most frequent chromosome arm-level CNVs included losses in 4p, 11p, and 11q and gains in 20q, 3q, and 1q. Then, we performed unsupervised consensus clustering of tumor CNV profiles and methylation profiles and detected four statistically significant expression subtypes. Finally, by combining the multidimensional datasets, we identified 10 potential driver genes, including GPR107, CHRNA5, ZBTB20, Rb1, NCAPH2, SCA1, SLC25A5, RBPMS, DDX3X, and H2BFM.Conclusions: This comprehensive analysis described the genetic characteristic of cervical cancer and identified novel driver genes in cervical cancer. These results provide insight into developing precision treatment in cervical cancer.

Background: Cervical cancer became the third most common cancer among women, and genome characterization of cervical cancer patients has revealed the extensive complexity of molecular alterations. However, identifying driver mutation and depicting molecular classification in cervical cancer remain a challenge. Methods: We performed an integrative multi-platform analysis of a cervical cancer cohort from The Cancer Genome Atlas (TCGA) based on 284 clinical cases and identified the driver genes and possible molecular classification of cervical cancer. Results: Multi-platform integration showed that cervical cancer exhibited a wide range of mutation. The top 10 mutated genes were TTN, PIK3CA, MUC4, KMT2C, MUC16, KMT2D, SYNE1, FLG, DST, and EP300, with a mutation rate from 12 to 33%. Applying GISTIC to detect copy number variation (CNV), the most frequent chromosome arm-level CNVs included losses in 4p, 11p, and 11q and gains in 20q, 3q, and 1q. Then, we performed unsupervised consensus clustering of tumor CNV profiles and methylation profiles and detected four statistically significant expression subtypes. Finally, by combining the multidimensional datasets, we identified 10 potential driver genes, including GPR107, CHRNA5, ZBTB20, Rb1, NCAPH2, SCA1, SLC25A5, RBPMS, DDX3X, and H2BFM. Conclusions: This comprehensive analysis described the genetic characteristic of cervical cancer and identified novel driver genes in cervical cancer. These results provide insight into developing precision treatment in cervical cancer. [ABSTRACT FROM AUTHOR]

Recent whole-genome sequencing studies have identified millions of somatic variants present in tumor samples. Most of these variants reside in non-coding regions of the genome potentially affecting transcriptional and post-transcriptional gene regulation. Although a few hallmark examples of driver mutations in non-coding regions have been reported, the functional role of the vast majority of somatic non-coding variants remains to be determined. This is because the few driver variants in each sample must be distinguished from the thousands of passenger variants and because the logic of regulatory element function has not yet been fully elucidated. Thus, variants prioritized based on mutational burden and location within regulatory elements need to be validated experimentally. This is generally achieved by combining assays that measure physical binding, such as chromatin immunoprecipitation, with those that determine regulatory activity, such as luciferase reporter assays. Here, we present an overview of in silico approaches used to prioritize somatic non-coding variants and the experimental methods used for functional validation and characterization.

Recent whole-genome sequencing studies have identified millions of somatic variants present in tumor samples. Most of these variants reside in non-coding regions of the genome potentially affecting transcriptional and post-transcriptional gene regulation. Although a few hallmark examples of driver mutations in non-coding regions have been reported, the functional role of the vast majority of somatic non-coding variants remains to be determined. This is because the few driver variants in each sample must be distinguished from the thousands of passenger variants and because the logic of regulatory element function has not yet been fully elucidated. Thus, variants prioritized based on mutational burden and location within regulatory elements need to be validated experimentally. This is generally achieved by combining assays that measure physical binding, such as chromatin immunoprecipitation, with those that determine regulatory activity, such as luciferase reporter assays. Here, we present an overview of in silico approaches used to prioritize somatic non-coding variants and the experimental methods used for functional validation and characterization. [ABSTRACT FROM AUTHOR]

Background: Hepatitis B virus (HBV) infection is one of the major causes of chronic liver disease, which progresses from chronic hepatitis B (CHB) to fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Early detection and laboratory-based screening of hepatocellular carcinoma are still major challenges. This study was undertaken to determine whether the cancer hallmark gene signatures that are released into circulation as circulating tumour DNA (ctDNA) can be used as a liquid biopsy marker for screening, early detection, and prognosis of HCC. Methods: A total of 130 subjects, including HBV-HCC ( n = 80), HBV-cirrhotic and non-cirrhotic ( n = 35), and healthy ( n = 15) controls, were evaluated for TP53 and beta-catenin (CTNNB1) gene hotspot mutations in ctDNA by Sanger-based cycle sequencing and droplet digital PCR (ddPCR) assays. Mutation detection frequency, percentage mutant fractions, and their association with tumour stage, mortality, and smoking habits were determined. Results: Sanger-based cycle sequencing was carried out for 32 HCC patients. Predict SNP Tools analysis indicated several pathogenic driver mutations in the ctDNA sequence, which include p.D228N, p.C229R, p.H233R, p.Y234D, p.S240T, p.G245S, and p.R249M for TP53 gene exon 7 and p.S33T for CTNNB1 gene exon 3. The TP53 c.746G>T (p.R249M) mutation was detected predominately (25% cases) by sequencing, but there was no dominant mutation at position c.747G>T (p.R249S) that was reported for HBV-HCC patients. A dual-probe ddPCR assay was developed to determine mutant and wild-type copy numbers of TP53 (p.R249M and p.R249S) and CTNNB1 (p.S45P) and their percentage mutant fraction in all 130 subjects. The TP53 R249M and CTNNB1 S45P mutations were detected in 31.25% and 26.25% of HCC patients, respectively, with a high mutant-to-wild-type fraction percentage (1.81% and 1.73%), which is significant as compared to cirrhotic and non-cirrhotic patients. Poor survival was observed in HCC patients with combined TP53 and CTNNB1 gene driver mutations. The TP53 R249M mutation was also significantly ( p < 0.0001) associated with smoking habits (OR, 11.77; 95% CI, 3.219-36.20), but not the same for the TP53 R249S mutation. Conclusion: Screening of ctDNA TP53 and CTNNB1 gene mutations by ddPCR may be helpful for early detection and identifying the risk of HCC progression., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kumar, Nadda, Quadri, Kumar, Paul, Tanwar, Gamanagatti, Dash, Saraya, Shalimar and Nayak.)

Introduction: Driver mutations play a critical role in the occurrence and development of human cancers. Most studies have focused on missense mutations that function as drivers in cancer. However, accumulating experimental evidence indicates that synonymous mutations can also act as driver mutations. Methods: Here, we proposed a computational method called PredDSMC to accurately predict driver synonymous mutations in human cancers. We first systematically explored four categories of multimodal features, including sequence features, splicing features, conservation scores, and functional scores. Further feature selection was carried out to remove redundant features and improve the model performance. Finally, we utilized the random forest classifier to build PredDSMC. Results: The results of two independent test sets indicated that PredDSMC outperformed the state-of-the-art methods in differentiating driver synonymous mutations from passenger mutations. Discussion: In conclusion, we expect that PredDSMC, as a driver synonymous mutation prediction method, will be a valuable method for gaining a deeper understanding of synonymous mutations in human cancers., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wang, Sun, Ma, Xia and Li.)