Increasing stage and depth of invasion (DOI) in patients with tongue cancer during the COVID-19 pandemic: A time series study.

Background and Aims: The outbreak of the Coronavirus disease 2019 (COVID-19) pandemic had a significant effect on the diagnosis and treatment of head and neck cancers. Therefore, in this study, we decided to discuss the impact of COVID-19 on the stage and histological characteristics of patients with tongue cancer from March 2020 to March 2021 and compared to the previous 3 years. Methods: In this time series study, patients diagnosed with squamous cell carcinoma of the operated tongue cancer were divided into two groups. Patients who operated from March 2020 to March 2021 (n = 36) and patients who operated 3 years ago (n = 70) were included in the study. The results were analyzed using SPSS 21 software. Results: The study found that during the pandemic, the stage of tongue cancer in patients who underwent surgery was higher than before the pandemic (p = 0.01). Moreover, the depth of invasion was significantly higher during the COVID-19 outbreak in the pathology sample of the patients (p = 0.006), while the involvement of lymph nodes and other variables between the groups was not statistically significant. Conclusion: COVID-19 has adverse effects on the diagnosis and treatment of tongue cancer. Also, it leads to advanced stages of the tumor and increases the depth of invasion of the cancer. Hence, it is important to plan correctly and appropriately for the diagnosis and treatment of these patients in conditions such as the COVID-19 pandemic.

A Comprehensive Investigation of Genomic Variants in Prostate Cancer Reveals 30 Putative Regulatory Variants.

Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies have shown that genomic alterations represent the most common mechanism for molecular alterations responsible for the development and progression of PC. This highlights the importance of identifying functional genomic variants for early detection in high-risk PC individuals. Great efforts have been made to identify common protein-coding genetic variations; however, the impact of non-coding variations, including regulatory genetic variants, is not well understood. Identification of these variants and the underlying target genes will be a key step in improving the detection and treatment of PC. To gain an understanding of the functional impact of genetic variants, and in particular, regulatory variants in PC, we developed an integrative pipeline (AGV) that uses whole genome/exome sequences, GWAS SNPs, chromosome conformation capture data, and ChIP-Seq signals to investigate the potential impact of genomic variants on the underlying target genes in PC. We identified 646 putative regulatory variants, of which 30 significantly altered the expression of at least one protein-coding gene. Our analysis of chromatin interactions data (Hi-C) revealed that the 30 putative regulatory variants could affect 131 coding and non-coding genes. Interestingly, our study identified the 131 protein-coding genes that are involved in disease-related pathways, including Reactome and MSigDB, for most of which targeted treatment options are currently available. Notably, our analysis revealed several non-coding RNAs, including RP11-136K7.2 and RAMP2-AS1, as potential enhancer elements of the protein-coding genes CDH12 and EZH1, respectively. Our results provide a comprehensive map of genomic variants in PC and reveal their potential contribution to prostate cancer progression and development.

PeakCNV: A multi-feature ranking algorithm-based tool for genome-wide copy number variation-association study.

Copy Number Variation (CNV) refers to a type of structural genomic alteration in which a segment of chromosome is duplicated or deleted. To date, many CNVs have been identified as causative genetic elements for several diseases and phenotypes. However, performing a CNV-based genome-wide association study is challenging due to inconsistency in length and occurrence of CNVs across different individuals under investigation. One of the most efficient strategies to address this issue is building CNV regions (genomic regions in which CNVs are overlapping - CNVRs). However, this approach is susceptible to a high false positive rate due to overlapping and co-occurring of confounding CNVRs with true positive CNVRs. Here, we develop PeakCNV that differentiates false-positive CNVRs from true positives by calculating a new metric, independence ranking score, (IR-score) via a feature ranking approach. We compared the performance of PeakCNV with other current existing tools by carrying out two case studies one using the CNV genotype data for individuals with prostate cancer (194 cases and 2,392 healthy individuals) and the second one for individuals with neurodevelopmental disorders (19,642 cases and 6,451 healthy individuals). Crucially, our benchmarking analyses on prostate cancer cohort indicated that PeakCNV identifies a fewer risk candidate CNVRs with shorter lengths compared to other tools. Importantly, these CNVRs cover a greater proportion of case over healthy individuals compared to other tools. The accuracy of PeakCNV in identifying relevant candidate CNVRs was reproducible in the case study on neurodevelopmental disorders. Using data from the FANTOM5 expression atlas and the Clinical Genomic Database, we show that the candidate CNVRs identified by PeakCNV for neurodevelopmental disorders overlap with a greater number of genes with the brain-enriched expression, and a greater number of genes that are associated with neurological conditions compared to candidate CNVRs identified by other tools. Taken together, PeakCNV outperformed current existing CNV association study tools by identifying more biologically meaningful CNVRs relevant to the phenotype of interest. PeakCNV is publicly available for the analysis of CNV-associated diseases and is accessible from https://rdrr.io/github/mahdieh1/PeakCNV.

Integrative analysis of mutated genes and mutational processes reveals novel mutational biomarkers in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Recent studies have observed causative mutations in susceptible genes related to colorectal cancer in 10 to 15% of the patients. This highlights the importance of identifying mutations for early detection of this cancer for more effective treatments among high risk individuals. Mutation is considered as the key point in cancer research. Many studies have performed cancer subtyping based on the type of frequently mutated genes, or the proportion of mutational processes. However, to the best of our knowledge, combination of these features has never been used together for this task. This highlights the potential to introduce better and more inclusive subtype classification approaches using wider range of related features to enable biomarker discovery and thus inform drug development for CRC. RESULTS: In this study, we develop a new pipeline based on a novel concept called 'gene-motif', which merges mutated gene information with tri-nucleotide motif of mutated sites, for colorectal cancer subtype identification. We apply our pipeline to the International Cancer Genome Consortium (ICGC) CRC samples and identify, for the first time, 3131 gene-motif combinations that are significantly mutated in 536 ICGC colorectal cancer samples. Using these features, we identify seven CRC subtypes with distinguishable phenotypes and biomarkers, including unique cancer related signaling pathways, in which for most of them targeted treatment options are currently available. Interestingly, we also identify several genes that are mutated in multiple subtypes but with unique sequence contexts. CONCLUSION: Our results highlight the importance of considering both the mutation type and mutated genes in identification of cancer subtypes and cancer biomarkers. The new CRC subtypes presented in this study demonstrates distinguished phenotypic properties which can be effectively used to develop new treatments. By knowing the genes and phenotypes associated with the subtypes, a personalized treatment plan can be developed that considers the specific phenotypes associated with their genomic lesion.

Impact of the COVID-19 pandemic on the stage and the type of surgical treatment of laryngeal cancer.

PURPOSE: Changes in the entire health care system during COVID-19 epidemic have affected the management of patients with head and neck cancer and posed several clinical challenges for ENT surgeons. Therefore, the present study aimed to investigate the effect of COVID-19 on the stage and the type of surgical treatments used in laryngeal cancer (including total laryngectomy, supracricoid partial laryngectomy (SCPL) and transoral laser microsurgery (TLM)) and also to compare the results of April 2020 to April 2021 with the previous year. MATERIALS AND METHODS: This cross-sectional study was performed on all patients with a diagnosis of laryngeal cancer who underwent surgery in the tertiary care center from April 2020 to April 2021 and the year before the pandemic in the same time. Demographic, cancer stage, and treatment data of all patients were recorded and analysis in two groups. RESULTS: Patients referred at the time of the virus outbreak; 111 were male and 5 were female, and in the group of patients referred before COVID-19, 90 were male and 12 were female. The type of surgical treatment of laryngeal cancer, mean time elapsed from sampling to surgery, stage of disease and mean tumor volume was statistically significant differences in patients before and during the outbreak. CONCLUSION: Patients who referred for diagnosis and treatment at the time of COVID-19 outbreak had more advanced stages of the disease and also the tumor volume was higher in them than patients who had referred before the outbreak. It is necessary to provide new solutions, education and treatment management for patients with laryngeal cancer in such pandemics.

Whole-Genome Analysis of De Novo Somatic Point Mutations Reveals Novel Mutational Biomarkers in Pancreatic Cancer.

It is now known that at least 10% of samples with pancreatic cancers (PC) contain a causative mutation in the known susceptibility genes, suggesting the importance of identifying cancer-associated genes that carry the causative mutations in high-risk individuals for early detection of PC. In this study, we develop a statistical pipeline using a new concept, called gene-motif, that utilizes both mutated genes and mutational processes to identify 4211 3-nucleotide PC-associated gene-motifs within 203 significantly mutated genes in PC. Using these gene-motifs as distinguishable features for pancreatic cancer subtyping results in identifying five PC subtypes with distinguishable phenotypes and genotypes. Our comprehensive biological characterization reveals that these PC subtypes are associated with different molecular mechanisms including unique cancer related signaling pathways, in which for most of the subtypes targeted treatment options are currently available. Some of the pathways we identified in all five PC subtypes, including cell cycle and the Axon guidance pathway are frequently seen and mutated in cancer. We also identified Protein kinase C, EGFR (epidermal growth factor receptor) signaling pathway and P53 signaling pathways as potential targets for treatment of the PC subtypes. Altogether, our results uncover the importance of considering both the mutation type and mutated genes in the identification of cancer subtypes and biomarkers.