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The traditional way for discovering genes which drive cancer (namely cancer drivers) neglects the dynamic information of cancer development, even though it is well known that cancer progresses dynamically. To enhance cancer driver discovery, we expand cancer driver concept to dynamic cancer driver as a gene driving one or more bio-pathological transitions during cancer progression. Our method refers to the fact that cancer should not be considered as a single process but a compendium of altered biological processes causing the disease to develop over time. Reciprocally, different drivers of cancer can potentially be discovered by analysing different bio-pathological pathways. We propose a novel approach for causal inference of genes driving one or more core processes during cancer development (i.e. dynamic cancer driver). We use the concept of pseudotime for inferring the latent progression of samples along a biological transition during cancer and identifying a critical event when such a process is significantly deviated from normal to carcinogenic. We infer driver genes by assessing the causal effect they have on the process after such a critical event. We have applied our method to single-cell and bulk sequencing datasets of breast cancer. The evaluation results show that our method outperforms well-recognized cancer driver inference methods. These results suggest that including information of the underlying dynamics of cancer improves the inference process (in comparison with using static data), and allows us to discover different sets of driver genes from different processes in cancer. R scripts and datasets can be found at https://github.com/AndresMCB/DynamicCancerDriver [ABSTRACT FROM AUTHOR]

Nowadays, the heterogeneous characteristics of cancer patients throw a big challenge to precision medicine and targeted therapy. Identifying cancer subtypes shed new light on effective personalized cancer medicine, future therapeutic strategies and minimizing treatment-related costs. Recently, there are many clustering methods have been proposed in categorizing cancer patients. Although these methods obtained a certain achievement in cancer subtype identification, they still fail to fully use the prior known biological information in the model designing process to improve precision and efficiency. It is acknowledged that the driver gene always regulates its downstream genes in the network to perform a certain function. By analyzing the known clinic cancer subtype data, we found some special co-pathways between the driver genes and the downstream genes in the cancer patients of the same subgroup. Hence, we proposed a novel model named DDCMNMF(Driver and Downstream gene Co-Module Assisted Multiple Non-negative Matrix Factorization model) that first for cancer subtypes by identifying co-modules of driver genes and downstream genes. We applied our model on lung and breast cancer datasets and compared it with the other four state-of-the-art models. The final results show that our model could identify the cancer subtypes with high compactness and separateness and achieve a high degree of consistency with the known cancer subtypes. The survival time analysis further proves the significant clinical characteristic of identified cancer subgroups by our model. [ABSTRACT FROM AUTHOR]

Although implicated as deleterious in many organisms, aneuploidy can underlie rapid phenotypic evolution. However, aneuploidy will be maintained only if the benefit outweighs the cost, which remains incompletely understood. To quantify this cost and the molecular determinants behind it, we generated a panel of chromosome duplications in Saccharomyces cerevisiae and applied comparative modeling and molecular validation to understand aneuploidy toxicity. We show that 74%-94% of the variance in aneuploid strains' growth rates is explained by the cumulative cost of genes on each chromosome, measured for single-gene duplications using a genomic library, along with the deleterious contribution of small nucleolar RNAs (snoRNAs) and beneficial effects of tRNAs. Machine learning to identify properties of detrimental gene duplicates provided no support for the balance hypothesis of aneuploidy toxicity and instead identified gene length as the best predictor of toxicity. Our results present a generalized framework for the cost of aneuploidy with implications for disease biology and evolution., Competing Interests: Declaration of interests The authors have no competing interests to declare., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective: Cancer is a complex genetic disease characterized by the accumulation of various mutations, with driver genes playing a crucial role in cancer initiation and progression. Distinguishing driver genes from passenger mutations is essential for understanding cancer biology and discovering therapeutic targets. However, the majority of existing methods ignore the mutational heterogeneity and commonalities among patients, which hinders the identification of driver genes more effectively., Methods: This study introduces MCSdriver, a novel computational model that integrates network and pathway information to prioritize the identification of cancer driver genes. MCSdriver employs a bidirectional random walk algorithm to quantify the mutual exclusivity and functional relationships between mutated genes within patient cohorts. It calculates similarity scores based on a mutual exclusivity-weighted network and pathway coverage patterns, accounting for patient-specific heterogeneity and molecular profile similarity., Results: This approach enhances the accuracy and quality of driver gene identification. MCSdriver demonstrates superior performance in identifying cancer driver genes across four cancer types from The Cancer Genome Atlas, showing a higher F-score, Recall and Precision compared to existing ranking list-based and module-based models., Conclusion: The MCSdriver model not only outperforms other models in identifying known cancer driver genes but also effectively identifies novel driver genes involved in cancer-related biological processes. The model's consideration of patient-specific heterogeneity and similarity in molecular profiles significantly enhances the accuracy and quality of driver gene identification. Validation through Gene Ontology enrichment analysis and literature mining further underscores its potential application value in personalized cancer therapy, offering a promising tool for advancing our understanding and treatment of cancer., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Gastric cancer (GC) pathogenesis is complex and heterogeneous, reflecting morphological, molecular and genetic diversity. Diffuse gastric cancer (DGC) and intestinal gastric cancer (IGC) are the major histological types. GC may be sporadic or hereditary; sporadic GC is related to environmental and genetic low‐risk factors and hereditary GC is caused by inherited high‐risk mutations, so far identified only for the diffuse histotype. DGC phenotypic heterogeneity challenges the current understanding of molecular mechanisms underlying carcinogenesis. The definition of a DGC‐specific mutational profile remains controversial, possibly reflecting the heterogeneity of DGC‐related histological subtypes [signet‐ring cell carcinoma (SRCC) and poorly cohesive carcinoma not otherwise specified (PCC‐NOS)]. Indeed, DGC and DGC‐related subtypes may present specific mutational profiles underlying the particularly aggressive behaviour and dismal prognosis of DGC vs IGC and PCC‐NOS vs SRCC. In this systematic review, we revised the histological presentations, molecular classifications and approved therapies for gastric cancer, with a focus on DGC. We then analysed results from the most relevant studies, reporting mutational analysis data specifying mutational frequencies, and their relationship with DGC and IGC histological types, and with specific DGC subtypes (SRCC and PCC‐NOS). We aimed at identifying histology‐associated mutational profiles with an emphasis in DGC and its subtypes (DGC vs IGC; sporadic vs hereditary DGC; and SRCC vs PCC‐NOS). We further used these mutational profiles to identify the most commonly affected molecular pathways and biological functions, and explored the clinical trials directed specifically to patients with DGC. This systematic analysis is expected to expose a DGC‐specific molecular profile and shed light into potential targets for therapeutic intervention, which are currently missing.

Abstract Background Molecular testing for alterations in oncogenic driver genes and targeted therapies have become standard procedures for non‐small cell lung cancer (NSCLC) patients. However, little evidence has shed light on the pattern of co‐existence of driver genes in NSCLC, and whether they may have different tumor features affecting immunotherapy is still unclarified. Methods Genomic alterations in 14 lung cancer‐related genes were conducted in 3440 Chinese NSCLC patients using next‐generation sequencing. Meanwhile, tumor mutational burden and immunotherapy dataset from the Memorial sloan kettering cancer center (MSKCC) and lung adenocarcinoma dataset from The Cancer Genome Atlas (TCGA) were utilized for analyzing the impact of the co‐occurring alterations on patients’ survival following immunotherapy. Results In this cohort, 90.17% of patients had at least one somatic alteration in the 14 genes, including 51% of co‐occurring alterations. TP53 and epidermal growth factor receptor (EGFR) were the most prevalent genes (54.74% and 53.55%, respectively), followed by KRAS, ERBB2, ALK, PIK3CA, ROS1, RET, MET, BRAF, KIT, FGFR1, PDGFRA, and NRAS. The prevalence of TP53, EGFR, and ERBB2 in our cohort were significantly higher than that from the TCGA database, whereas KRAS, BRAF, and PDGFRA were significantly lower than the latter. Furthermore, the patients who harbored multiple alterations (8.86%, 31/350) in eight driver genes survived longer and have a higher tumor mutation burden compared to the patients with a single alteration. Similar result was found between the patients with co‐occurring alteration of EGFR and other driver genes and the patients with single EGFR alteration. Meanwhile, we found a distinct immune cell infiltration feature between patients with single and multiple driver gene alterations, as well as between patients with only EGFR alteration and co‐occurring groups. Conclusion This study identified a unique driver gene feature and found patients harboring co‐occurring alterations of EGFR and other driver genes may benefit from immunotherapy, which may provide more therapeutic selections for EGFR‐mutated NSCLC patients and merit additional investigation.

Abstract Background As one of the deadliest diseases in the world, cancer is driven by a few somatic mutations that disrupt the normal growth of cells, and leads to abnormal proliferation and tumor development. The vast majority of somatic mutations did not affect the occurrence and development of cancer; thus, identifying the mutations responsible for tumor occurrence and development is one of the main targets of current cancer treatments. Results To effectively identify driver genes, we adopted a semi-local centrality measure and gene mutation effect function to assess the effect of gene mutations on changes in gene expression patterns. Firstly, we calculated the mutation score for each gene. Secondly, we identified differentially expressed genes (DEGs) in the cohort by comparing the expression profiles of tumor samples and normal samples, and then constructed a local network for each mutation gene using DEGs and mutant genes according to the protein–protein interaction network. Finally, we calculated the score of each mutant gene according to the objective function. The top-ranking mutant genes were selected as driver genes. We name the proposed method as mutations effect and network centrality. Conclusions Four types of cancer data in The Cancer Genome Atlas were tested. The experimental data proved that our method was superior to the existing network-centric method, as it was able to quickly and easily identify driver genes and rare driver factors.

Background: Genomic studies of colorectal cancer have revealed the complex genomic heterogeneity of the tumor. The acquisition and selection of genomic alterations may be critical to understanding the initiation and progression of this disease.Methods: In this study, we have systematically characterized the clonal architecture of 97 driver genes in 536 colorectal cancer patients from TCGA.Results: A high proportion of clonal mutations in 93 driver genes were observed. 40 genes showed significant associations between their clonality and multiple clinicopathologic factors. Kaplan-Meier analysis suggested that the mutation clonality of ANK1, CASP8, SMAD2, and ARID1A had a significant impact on the CRC patients' outcomes. Multivariable analysis revealed that subclonal ANK1 mutations, clonal CASP8 mutations, and clonal SMAD2 mutations independently predicted for shorter overall survival after adjusting for clinicopathological factors. The poor outcome of the subclonal ANK1 mutation may be caused by upregulation of IL4I1, IDO1, IFNG and MAPK12 which showed potential roles in tumor immune evasion through accumulation of immunosuppressive cells such as regulatory T cells and myeloid derived suppressor cells.Conclusion: These results suggested that the clonality of driver genes could act as prognostic markers and potential therapeutic targets in human colorectal cancer. [ABSTRACT FROM AUTHOR]

Background: The advent of high throughput sequencing has enabled researchers to systematically evaluate the genetic variations in cancer, identifying many cancer-associated genes. Although cancers in the same tissue are widely categorized in the same group, they demonstrate many differences concerning their mutational profiles. Hence, there is no definitive treatment for most cancer types. This reveals the importance of developing new pipelines to identify cancer-associated genes accurately and re-classify patients with similar mutational profiles. Classification of cancer patients with similar mutational profiles may help discover subtypes of cancer patients who might benefit from specific treatment types. Results: In this study, we propose a new machine learning pipeline to identify protein-coding genes mutated in many samples to identify cancer subtypes. We apply our pipeline to 12,270 samples collected from the international cancer genome consortium, covering 19 cancer types. As a result, we identify 17 different cancer subtypes. Comprehensive phenotypic and genotypic analysis indicates distinguishable properties, including unique cancer-related signaling pathways. Conclusions: This new subtyping approach offers a novel opportunity for cancer drug development based on the mutational profile of patients. Additionally, we analyze the mutational signatures for samples in each subtype, which provides important insight into their active molecular mechanisms. Some of the pathways we identified in most subtypes, including the cell cycle and the Axon guidance pathways, are frequently observed in cancer disease. Interestingly, we also identified several mutated genes and different rates of mutation in multiple cancer subtypes. In addition, our study on "gene-motif" suggests the importance of considering both the context of the mutations and mutational processes in identifying cancer-associated genes. The source codes for our proposed clustering pipeline and analysis are publicly available at: https://github.com/bcb-sut/Pan-Cancer. [ABSTRACT FROM AUTHOR]

Cushing's disease represents 60–70% of all cases of Cushing's syndrome, presenting with a constellation of clinical features associated with sustained hypercortisolism. Molecular alterations in corticotrope cells lead to the formation of ACTH-secreting adenomas, with subsequent excessive production of endogenous glucocorticoids. In the last few years, many authors have contributed to analyzing the etiopathogenesis and pathophysiology of corticotrope adenomas, which still need to be fully clarified. New molecular modifications such as somatic mutations of USP8 and other genes have been identified, and several case series and case reports have been published, highlighting new molecular alterations that need to be explored. To investigate the current knowledge of the genetics of ACTH-secreting adenomas, we performed a bibliographic search of the recent scientific literature to identify all pertinent articles. This review presents the most recent updates on somatic and germline mutations underlying Cushing's disease. The prognostic implications of these mutations, in terms of clinical outcomes and therapeutic scenarios, are still debated. Further research is needed to define the clinical features associated with the different genotypes and potential pharmacological targets. [ABSTRACT FROM AUTHOR]

Simple Summary: HCC is a leading cause of cancer-related deaths worldwide. However, its tremendous inter- and intra-tumor heterogeneity has made it difficult to identify driver genes for HCC. Transposon mutagenesis is a versatile in vivo tool to identify cancer genes in tissues of interest. Herein, we analyzed transposon mutagenesis screening data in the liver and discovered a novel tumor suppressor gene, RNF125. This gene functions as a negative regulator of cell proliferation through transcriptional suppression of multiple genes important for cell proliferation and liver regeneration. This gene is frequently inactivated in human HCC and has a significant impact on patient prognosis. Our findings identify a new regulatory network of cell proliferation mediated by RNF125 and its contribution to HCC development and progression. Hepatocellular carcinoma (HCC) is one of the deadliest cancers worldwide and the only cancer with an increasing incidence in the United States. Recent advances in sequencing technology have enabled detailed profiling of liver cancer genomes and revealed extensive inter- and intra-tumor heterogeneity, making it difficult to identify driver genes for HCC. To identify HCC driver genes, we performed transposon mutagenesis screens in a mouse HBV model of HCC and discovered many candidate cancer genes (SB/HBV-CCGs). Here, we show that one of these genes, RNF125 is a potent anti-proliferative tumor suppressor gene in HCC. RNF125 is one of nine CCGs whose expression was >3-fold downregulated in human HCC. Depletion of RNF125 in immortalized mouse liver cells led to tumor formation in transplanted mice and accelerated growth of human liver cancer cell lines, while its overexpression inhibited their growth, demonstrating the tumor-suppressive function of RNF125 in mouse and human liver. Whole-transcriptome analysis revealed that RNF125 transcriptionally suppresses multiple genes involved in cell proliferation and/or liver regeneration, including Egfr, Met, and Il6r. Blocking Egfr or Met pathway expression inhibited the increased cell proliferation observed in RNF125 knockdown cells. In HCC patients, low expression levels of RNF125 were correlated with poor prognosis demonstrating an important role for RNF125 in HCC. Collectively, our results identify RNF125 as a novel anti-proliferative tumor suppressor in HCC. [ABSTRACT FROM AUTHOR]

Abstract Background Malignant melanoma of the female genital tract is relatively uncommon and accounts for 3–7% of all melanoma localizations. This study aimed to identify driver genes in melanoma of the female genital tract with the purpose of enhancing understanding of disease pathogenesis and identifying potential new therapeutic targets to develop effective therapies. Methods KIT (CD117) and BRAF expression were detected immunohistochemically. Polymerase Chain Reaction (PCR) and Sanger sequencing techniques were performed to identify the mutational status of BRAF, NRAS, KRAS, NF1, KIT, PDGFRA and SF3B1 on 19 melanomas of the female genital tract, paired with 25 cutaneous melanomas, 18 acral melanomas and 11 melanomas of nasal cavity. Results Somatic variant analysis identified KRAS (6/19; 32%) as the most commonly mutated gene, followed by KIT (4/19; 21%), SF3B1 (3/19; 16%) and NRAS (1/19; 5%). None of the cases were found to harbor BRAF, NF1 and PDGFRA mutations in melanomas of the female genital tract. However, none of the cases were found to harbor SF3B1 and KIT mutations in cutaneous melanomas, acral melanomas and melanomas of nasal cavity. Recurrent KIT mutations, as well as mutations in the less frequently mutated genes NRAS and SF3B1, were exclusively detected in vulvovaginal melanomas, but not in tumors arising in the cervix. However, recurrent KRAS mutations were detected in similar frequencies in tumors of the vulva, vagina, and cervix. Additionally, recurrent KRAS and KIT mutations occurred predominantly in polygonal and epithelioid cell types of melanoma in the female genital tract. Immunohistochemistry revealed moderate or strong cytoplasmic CD117 expression in 6 of the 19 cases (31.6%). Conclusions We observed that gynecologic melanoma harbored distinct mutation rates in the KIT, BRAF, SF3B1, KRAS, and NRAS genes. Our findings support the notion that gynecologic melanoma is a distinct entity from non-gynecologic melanoma, and these findings offer insights into future therapeutic options for these patients.

Background: Driver mutations are the genetic components responsible for tumor initiation and progression. These variants, which may be inherited, influence cancer risk and therefore underlie many familial cancers. The present study examines the potential association between SNPs in driver genes SF3B1 (rs4685), TBX3 (rs12366395, rs8853, and rs1061651) and MAP3K1 (rs72758040) and BC in BRCA1/2-negative Chilean families. Methods: The SNPs were genotyped in 486 BC cases and 1258 controls by TaqMan Assay. Results: Our data do not support an association between rs4685:C > T, rs8853:T > C, or rs1061651:T > C and BC risk. However, the rs12366395-G allele (A/G + G/G) was associated with risk in families with a strong history of BC (OR = 1.2 [95% CI 1.0–1.6] p = 0.02 and OR = 1.5 [95% CI 1.0–2.2] p = 0.02, respectively). Moreover, rs72758040-C was associated with increased risk in cases with a moderate-to-strong family history of BC (OR = 1.3 [95% CI 1.0–1.7] p = 0.02 and OR = 1.3 [95% CI 1.0–1.8] p = 0.03 respectively). Finally, risk was significantly higher in homozygous C/C cases from families with a moderate-to-strong BC history (OR = 1.8 [95% CI 1.0–3.1] p = 0.03 and OR = 1.9 [95% CI 1.1–3.4] p = 0.01, respectively). We also evaluated the combined impact of rs12366395-G and rs72758040-C. Familial BC risk increased in a dose-dependent manner with risk allele count, reflecting an additive effect (p-trend = 0.0002). Conclusions: Our study suggests that germline variants in driver genes TBX3 (rs12366395) and MAP3K1 (rs72758040) may influence BC risk in BRCA1/2-negative Chilean families. Moreover, the presence of rs12366395-G and rs72758040-C could increase BC risk in a Chilean population. [ABSTRACT FROM AUTHOR]

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require multiple samples to identify less frequently mutated driver genes. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumor progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify personalized driver genes based on changes in expression. Our method is the first machine learning model to classify genes as tumor suppressor gene (TSG), oncogene (OG), or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalized Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalized driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labeling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared with mutation and expression data, achieving an accuracy ≥ 0.99 for BRCA, LUAD, and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD, and LUAD cancer types reveal commonly altered genes such as TP53 and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9, and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalized driver genes as TSGs and OGs and also identified rare driver genes. [ABSTRACT FROM AUTHOR]

Objective To establish a druggability evaluation method for new targets of anti-tumor drugs by analyzing the mutation genes of common tumors in the digestive system. Methods We collected the mutant gene data of the five common tumors of the digestive system (esophageal cancer, gastric cancer, colorectal cancer, liver cancer and pancreatic cancer) in the Integrative Onco Genomics database, and screened out the genes with higher mutation rates in each tumor. We evaluated the druggability of these genes or their encoded proteins, and discovered the potential targets for the new anti-tumor drugs. Results A total of five tumors, 35 cohorts and 5445 tumor samples were collected in this study. The top 10 mutation genes were selected for further analysis. The canSAR database was used to analyze the druggability of unpublished mutant genes or their encoded proteins, and a total of 17 potential therapeutic drug targets were screened out. Conclusion A method for evaluating druggability of targets based on mutant genes or their encoded protein is established in this study. The application of this method can provide a reference for discovering new anti-tumor therapeutic target, saving the cost and time of target screening in new drug development. [ABSTRACT FROM AUTHOR]

Background: Esophageal squamous cell carcinoma (ESCC) has a poor prognosis and occurs with high frequency in China. In particular, Fujian is one of the high-incidence areas of ESCC in China and the somatic mutation profile of ESCC there remains unclear. Patients and methods: Whole-exome sequencing (WES) was performed in 49 matched ESCC tumor-normal specimens to examine the somatic mutation profiles. Hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between mutational profile and survival were derived from Cox regression model. Results: We constructed a preliminary somatic mutation profiling of ESCC in Fujian. Exome sequencing data showed that the main base substitutions in ESCC were C > T transformation (close to 50%), C > A and T > C transversion. The study identified 21 significantly mutated genes, including 8 driver genes and 11 predicted driver genes. Among the 19 driver or predicted driver genes, 9 are novel (OBSCN, PKHD1L1, FSIP2, HRNR, CUBN, CELSR3, SCN7A, TULP4, SRRM2) and 10 have been previously reported. Three mutational signatures were identified to be prevalent in ESCC including Signature_15, Signature_4 and Signature_6, of which Signature_15 was related to prognosis of ESCC (HR 2.81, 95% CI 1.30–6.05; p = 0.008). Survival analysis showed that SCN7A was correlated to overall survival with an HR of 2.76 (95% CI 0.96–7.90, p = 0.058). After controlling for confounding factors such as age, gender, stage and location, the correlation between SCN7A and survival was statistically significant based on multivariate COX regression analysis (HR 4.76, 95% CI 1.20–18.85; p = 0.026, padjust = 0.053). The tumor vascular invasion was associated with SCN7A of ESCC patients (p = 0.028). Conclusion: In summary, this study provided comprehensive analysis of the somatic mutation profiles of ESCC, and identified SCN7A and Signature_15 for the prognosis of ESCC for the first time. The findings might serve as a conceptual basis for molecular diagnosis and prevention of ESCC. [ABSTRACT FROM AUTHOR]

Identifying cancer driver genes is essential for understanding the mechanisms of carcinogenesis and designing therapeutic strategies. Although driver genes have been identified for many cancer types, it is still not clear whether the selection pressure of driver genes is homogeneous across cancer subtypes. We propose a statistical framework MutScot to improve the identification of driver genes and to investigate the heterogeneity of driver genes across cancer subtypes. Through simulation studies, we show that MutScot properly controls the type I error in detecting driver genes. In addition, we demonstrate that MutScot can identify subtype heterogeneity of driver genes. Applications to three studies in The Cancer Genome Atlas (TCGA) project showcase that MutScot has a desirable sensitivity for detecting driver genes and that MutScot identifies subtype heterogeneity of driver genes in breast cancer and lung cancer with regards to the status of hormone receptor and to the smoking status, respectively. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) results from the uncontrolled growth of cells in the colon, rectum, or appendix. The 5-year relative survival rate for patients with CRC is 65% and is correlated with the stage at diagnosis (being 91% for stage I at diagnosis versus 12% for stage IV). This study aimed to identify CRC driver genes to assist in the design of a cancer panel to detect gene mutations during clinical early-stage screening and identify genes for use in prognostic assessments and the evaluation of appropriate treatment options. First, we utilized bioinformatics approaches to analyze 354 paired sequencing profiles from The Cancer Genome Atlas (TCGA) to identify CRC driver genes and analyzed the sequencing profiles of 38 patients with >5 years of follow-up data to search for prognostic genes. The results revealed eight driver genes and ten prognostic genes. Next, the presence of the identified gene mutations was verified using tissue and blood samples from Taiwanese CRC patients. The results showed that the set identified gene mutations provide high coverage for driver gene screening, and APC, TP53, PIK3CA, and FAT4 could be detected in blood as ctDNA test targets. We further found that BCL7A gene mutation was correlated with prognosis in CRC (log-rank p-value = 0.02), and that mutations of BCL7A could be identified in ctDNA samples. These findings may be of value in clinical early cancer detection, disease monitoring, drug development, and treatment efforts in the future.

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require multiple samples to identify less frequently mutated driver genes. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumor progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify personalized driver genes based on changes in expression. Our method is the first machine learning model to classify genes as tumor suppressor gene (TSG), oncogene (OG), or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalized Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalized driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labeling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared with mutation and expression data, achieving an accuracy ≥0.99 for BRCA, LUAD, and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD, and LUAD cancer types reveal commonly altered genes such as TP53 and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9, and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalized driver genes as TSGs and OGs and also identified rare driver genes.

Breast cancer is a heterogeneous disease, and its development is closely associated with the underlying molecular regulatory network. In this paper, we propose a new way to measure the regulation strength between genes based on their expression values, and construct the dysregulated networks (DNs) for the four subtypes of breast cancer. Our results show that the key dysregulated networks (KDNs) are significantly enriched in critical breast cancer-related pathways and driver genes; closely related to drug targets; and have significant differences in survival analysis. Moreover, the key dysregulated genes could serve as potential driver genes, drug targets, and prognostic markers for each breast cancer subtype. Therefore, the KDN is expected to be an effective and novel way to understand the mechanisms of breast cancer. [ABSTRACT FROM AUTHOR]

Abstract Background A retrospective analysis verified the role of gene mutations in brain metastasis in patients with non‐small cell lung cancer (NSCLC). Methods Data from 552 patients with advanced NSCLC treated from January 2015 to June 2017 in the Affiliated Cancer Hospital of Zhengzhou University were retrospectively analyzed. Next‐generation sequencing was used to detect mutations in eight reported driver genes and various risk factors were evaluated. Results Of the 552 patients with advanced NSCLC, 153 (27.7%) had brain metastases. The univariate analysis showed that age (P = .008), gender (P = .016), smoking history (P = .010), lymph node metastasis (P = .003), and three driver genes, positive epidermal growth factor receptor (EGFR) mutation (P = .001), positive anaplastic lymphoma kinase (ALK) gene fusion (P = .021), and positive rearranged during transfection (RET) gene fusion (P = .003), were the factors influencing the incidence of brain metastasis. Logistic multivariate regression analysis revealed that positive EGFR mutation (P = .012), positive ALK gene fusion (P = .015), positive RET gene fusion (P = .003), pathological type (P = .009), lymph node N2‐3 metastasis (P

Breast cancer is a heterogeneous disease, and its development is closely associated with the underlying molecular regulatory network. In this paper, we propose a new way to measure the regulation strength between genes based on their expression values, and construct the dysregulated networks (DNs) for the four subtypes of breast cancer. Our results show that the key dysregulated networks (KDNs) are significantly enriched in critical breast cancer-related pathways and driver genes; closely related to drug targets; and have significant differences in survival analysis. Moreover, the key dysregulated genes could serve as potential driver genes, drug targets, and prognostic markers for each breast cancer subtype. Therefore, the KDN is expected to be an effective and novel way to understand the mechanisms of breast cancer.

Abstract Background Genomic structural variants (SVs) can affect many genes and regulatory elements. Therefore, the molecular mechanisms driving the phenotypes of patients carrying de novo SVs are frequently unknown. Methods We applied a combination of systematic experimental and bioinformatic methods to improve the molecular diagnosis of 39 patients with multiple congenital abnormalities and/or intellectual disability harboring apparent de novo SVs, most with an inconclusive diagnosis after regular genetic testing. Results In 7 of these cases (18%), whole-genome sequencing analysis revealed disease-relevant complexities of the SVs missed in routine microarray-based analyses. We developed a computational tool to predict the effects on genes directly affected by SVs and on genes indirectly affected likely due to the changes in chromatin organization and impact on regulatory mechanisms. By combining these functional predictions with extensive phenotype information, candidate driver genes were identified in 16/39 (41%) patients. In 8 cases, evidence was found for the involvement of multiple candidate drivers contributing to different parts of the phenotypes. Subsequently, we applied this computational method to two cohorts containing a total of 379 patients with previously detected and classified de novo SVs and identified candidate driver genes in 189 cases (50%), including 40 cases whose SVs were previously not classified as pathogenic. Pathogenic position effects were predicted in 28% of all studied cases with balanced SVs and in 11% of the cases with copy number variants. Conclusions These results demonstrate an integrated computational and experimental approach to predict driver genes based on analyses of WGS data with phenotype association and chromatin organization datasets. These analyses nominate new pathogenic loci and have strong potential to improve the molecular diagnosis of patients with de novo SVs.

Abstract Background Cancer as a kind of genomic alteration disease each year deprives many people’s life. The biggest challenge to overcome cancer is to identify driver genes that promote the cancer development from a huge amount of passenger mutations that have no effect on the selective growth advantage of cancer. In order to solve those problems, some researchers have started to focus on identification of driver genes by integrating networks with other biological information. However, more efforts should be needed to improve the prediction performance. Methods Considering the facts that driver genes have impact on expression of their downstream genes, they likely interact with each other to form functional modules and those modules should tend to be expressed similarly in the same tissue. We proposed a novel model named by DyTidriver to identify driver genes through involving the gene dysregulated expression, tissue-specific expression and variation frequency into the human functional interaction network (e.g. human FIN). Results This method was applied on 974 breast, 316 prostate and 230 lung cancer patients. The consequence shows our method outperformed other five existing methods in terms of Fscore, Precision and Recall values. The enrichment and cociter analysis illustrate DyTidriver can not only identifies the driver genes enriched in some significant pathways but also has the capability to figure out some unknown driver genes. Conclusion The final results imply that driver genes are those that impact more dysregulated genes and express similarly in the same tissue.

Abstract Background Although there are huge volumes of genomic data, how to decipher them and identify driver events is still a challenge. The current methods based on network typically use the relationship between genomic events and consequent changes in gene expression to nominate putative driver genes. But there may exist some relationships within the transcriptional network. Methods We developed MECoRank, a novel method that improves the recognition accuracy of driver genes. MECoRank is based on bipartite graph to propagates the scores via an iterative process. After iteration, we will obtain a ranked gene list for each patient sample. Then, we applied the Condorcet voting method to determine the most impactful drivers in a population. Results We applied MECoRank to three cancer datasets to reveal candidate driver genes which have a greater impact on gene expression. Experimental results show that our method not only can identify more driver genes that have been validated than other methods, but also can recognize some impactful novel genes which have been proved to be more important in literature. Conclusions We propose a novel approach named MECoRank to prioritize driver genes based on their impact on the expression in the molecular interaction network. This method not only assesses mutation’s effect on the transcriptional network, but also assesses the differential expression’s effect within the transcriptional network. And the results demonstrated that MECoRank has better performance than the other competing approaches in identifying driver genes.

Objectives: To identify somatic mutations in tumors from young women with triple-negative or luminal breast cancer, through targeted sequencing and to explore the cancer driver potential of these gene variants., Methods: A customized gene panel was assembled based on data from previous sequencing studies of breast cancer from young women. Triple-negative and luminal tumors and paired blood samples from young breast cancer patients were sequenced, and identified gene variants were searched for their driver potential, in databases and literature. Additionally, the authors performed an exploratory analysis using large, curated databases to evaluate the frequency of somatic mutations in this gene panel in tumors stratified by age groups (every 10 years)., Results: A total of 28 young women had their tumoral tissue and blood samples sequenced. Using a customized panel of 64 genes, the authors could detect cancer drivers in 11/12 (91.7 %) TNBC samples and 11/16 (68.7 %) luminal samples. Among TNBC patients, the most frequent cancer driver was TP53, followed by NF1, NOTCH1 and PTPN13. In luminal samples, PIK3CA and GATA3 were the main cancer drivers, and other drivers were GRHL2 and SMURF2. CACNA1E was involved in both TN and luminal BC. The exploratory analysis also indicated a role for SMURF2 in luminal BC development in young patients., Conclusions: The data further indicates that some cancer drivers are more common in a specific breast cancer subtype from young patients, such as TP53 in TNBC and PIK3CA and GATA3 in luminal samples. These results also provide additional evidence that some genes not considered classical cancer-causing genes, such as CACNA1E, GRHL2 and SMURF2 might be cancer drivers in this age group., Competing Interests: Declaration of competing interest Giselly Encinas is an employee of Agilent Technologies. All other authors declare no conflicts of interest., (Copyright © 2024 HCFMUSP. Published by Elsevier España, S.L.U. All rights reserved.)

Cancer is a heterogeneous disease that results from genetic alteration of cell cycle and proliferation controls. Identifying mutations that drive cancer, understanding cancer type specificities, and delineating how driver mutations interact with each other to establish disease is vital for identifying therapeutic vulnerabilities. Such cancer specific patterns and gene co-occurrences can be identified by studying tumor genome sequences, and networks have proven effective in uncovering relationships between sequences. We present two network-based approaches to identify driver gene patterns among tumor samples. The first approach relies on analysis using the Directed Weighted All Nearest Neighbors (DiWANN) model, which is a variant of sequence similarity network, and the second approach uses bipartite network analysis. A data reduction framework was implemented to extract the minimal relevant information for the sequence similarity network analysis, where a transformed reference sequence is generated for constructing the driver gene network. This data reduction process combined with the efficiency of the DiWANN network model, greatly lowered the computational cost (in terms of execution time and memory usage) of generating the networks enabling us to work at a much larger scale than previously possible. The DiWANN network helped us identify cancer types in which samples were more closely connected to each other suggesting they are less heterogeneous and potentially susceptible to a common drug. The bipartite network analysis provided insight into gene associations and co-occurrences. We identified genes that were broadly mutated in multiple cancer types and mutations exclusive to only a few. Additionally, weighted one-mode gene projections of the bipartite networks revealed a pattern of occurrence of driver genes in different cancers. Our study demonstrates that network-based approaches can be an effective tool in cancer genomics. The analysis identifies co-occurring and exclusive driver genes and mutations for specific cancer types, providing a better understanding of the driver genes that lead to tumor initiation and evolution., 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 © 2024 Patil, Roberts and Gebremedhin.)

Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network’s central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment.

Aneuploidy, the presence of an aberrant number of chromosomes, has been associated with tumorigenesis for over a century. More recently, advances in karyotyping techniques have revealed its high prevalence in cancer: About 90% of solid tumors and 50-70% of hematopoietic cancers exhibit chromosome gains or losses. When analyzed at the level of specific chromosomes, there are strong patterns that are observed in cancer karyotypes both pan-cancer and for specific cancer types. These specific aneuploidy patterns correlate strongly with outcomes for tumor initiation, progression, metastasis formation, immune evasion and resistance to therapeutic treatment. Despite their prominence, understanding the basis underlying aneuploidy patterns in cancer has been challenging. Advances in genetic engineering and bioinformatic analyses now offer insights into the genetic determinants of aneuploidy pattern selection. Overall, there is substantial evidence that expression changes of particular genes can act as the positive selective forces for adaptation through aneuploidy. Recent findings suggest that multiple genes contribute to the selection of specific aneuploid chromosomes in cancer; however, further research is necessary to identify the most impactful driver genes. Determining the genetic basis and accompanying vulnerabilities of specific aneuploidy patterns is an essential step in selectively targeting these hallmarks of tumors., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.)

Gastric cancer (GC) pathogenesis is complex and heterogeneous, reflecting morphological, molecular and genetic diversity. Diffuse gastric cancer (DGC) and intestinal gastric cancer (IGC) are the major histological types. GC may be sporadic or hereditary; sporadic GC is related to environmental and genetic low‐risk factors and hereditary GC is caused by inherited high‐risk mutations, so far identified only for the diffuse histotype. DGC phenotypic heterogeneity challenges the current understanding of molecular mechanisms underlying carcinogenesis. The definition of a DGC‐specific mutational profile remains controversial, possibly reflecting the heterogeneity of DGC‐related histological subtypes [signet‐ring cell carcinoma (SRCC) and poorly cohesive carcinoma not otherwise specified (PCC‐NOS)]. Indeed, DGC and DGC‐related subtypes may present specific mutational profiles underlying the particularly aggressive behaviour and dismal prognosis of DGC vs IGC and PCC‐NOS vs SRCC. In this systematic review, we revised the histological presentations, molecular classifications and approved therapies for gastric cancer, with a focus on DGC. We then analysed results from the most relevant studies, reporting mutational analysis data specifying mutational frequencies, and their relationship with DGC and IGC histological types, and with specific DGC subtypes (SRCC and PCC‐NOS). We aimed at identifying histology‐associated mutational profiles with an emphasis in DGC and its subtypes (DGC vs IGC; sporadic vs hereditary DGC; and SRCC vs PCC‐NOS). We further used these mutational profiles to identify the most commonly affected molecular pathways and biological functions, and explored the clinical trials directed specifically to patients with DGC. This systematic analysis is expected to expose a DGC‐specific molecular profile and shed light into potential targets for therapeutic intervention, which are currently missing. [ABSTRACT FROM AUTHOR]

Background: Molecular testing for alterations in oncogenic driver genes and targeted therapies have become standard procedures for non‐small cell lung cancer (NSCLC) patients. However, little evidence has shed light on the pattern of co‐existence of driver genes in NSCLC, and whether they may have different tumor features affecting immunotherapy is still unclarified. Methods: Genomic alterations in 14 lung cancer‐related genes were conducted in 3440 Chinese NSCLC patients using next‐generation sequencing. Meanwhile, tumor mutational burden and immunotherapy dataset from the Memorial sloan kettering cancer center (MSKCC) and lung adenocarcinoma dataset from The Cancer Genome Atlas (TCGA) were utilized for analyzing the impact of the co‐occurring alterations on patients' survival following immunotherapy. Results: In this cohort, 90.17% of patients had at least one somatic alteration in the 14 genes, including 51% of co‐occurring alterations. TP53 and epidermal growth factor receptor (EGFR) were the most prevalent genes (54.74% and 53.55%, respectively), followed by KRAS, ERBB2, ALK, PIK3CA, ROS1, RET, MET, BRAF, KIT, FGFR1, PDGFRA, and NRAS. The prevalence of TP53, EGFR, and ERBB2 in our cohort were significantly higher than that from the TCGA database, whereas KRAS, BRAF, and PDGFRA were significantly lower than the latter. Furthermore, the patients who harbored multiple alterations (8.86%, 31/350) in eight driver genes survived longer and have a higher tumor mutation burden compared to the patients with a single alteration. Similar result was found between the patients with co‐occurring alteration of EGFR and other driver genes and the patients with single EGFR alteration. Meanwhile, we found a distinct immune cell infiltration feature between patients with single and multiple driver gene alterations, as well as between patients with only EGFR alteration and co‐occurring groups. Conclusion: This study identified a unique driver gene feature and found patients harboring co‐occurring alterations of EGFR and other driver genes may benefit from immunotherapy, which may provide more therapeutic selections for EGFR‐mutated NSCLC patients and merit additional investigation. [ABSTRACT FROM AUTHOR]

A major challenge in cancer genomics is to identify cancer driver genes and modules. Most existing methods to identify cancer driver modules (iCDM) identify groups of genes whose somatic mutational patterns exhibit either mutual exclusivity or high coverage of patient samples, without considering other biological information from multiomics data sets. Here we integrate mutual exclusivity, coverage, and protein–protein interaction information to construct an edge-weighted network, and present a graph clustering approach based on symmetric non-negative matrix factorization to iCDM. iCDM was tested on pan-cancer data and the results were compared with those from several advanced computational methods. Our approach outperformed other methods in recovering known cancer driver modules, and the identified driver modules showed high accuracy in classifying normal and tumor samples. [ABSTRACT FROM AUTHOR]

Background: As one of the deadliest diseases in the world, cancer is driven by a few somatic mutations that disrupt the normal growth of cells, and leads to abnormal proliferation and tumor development. The vast majority of somatic mutations did not affect the occurrence and development of cancer; thus, identifying the mutations responsible for tumor occurrence and development is one of the main targets of current cancer treatments. Results: To effectively identify driver genes, we adopted a semi-local centrality measure and gene mutation effect function to assess the effect of gene mutations on changes in gene expression patterns. Firstly, we calculated the mutation score for each gene. Secondly, we identified differentially expressed genes (DEGs) in the cohort by comparing the expression profiles of tumor samples and normal samples, and then constructed a local network for each mutation gene using DEGs and mutant genes according to the protein–protein interaction network. Finally, we calculated the score of each mutant gene according to the objective function. The top-ranking mutant genes were selected as driver genes. We name the proposed method as mutations effect and network centrality. Conclusions: Four types of cancer data in The Cancer Genome Atlas were tested. The experimental data proved that our method was superior to the existing network-centric method, as it was able to quickly and easily identify driver genes and rare driver factors. [ABSTRACT FROM AUTHOR]

Somatic copy-number alterations (SCNAs) are major contributors to cancer development that are pervasive and highly heterogeneous in human cancers. However, the driver roles of SCNAs in cancer are insufficiently characterized. We combined network propagation and linear regression models to design an integrative strategy to identify driver SCNAs and dissect the functional roles of SCNAs by integrating profiles of copy number and gene expression in lower-grade glioma (LGG). We applied our strategy to 511 LGG patients and identified 98 driver genes that dysregulated 29 cancer hallmark signatures, forming 143 active gene-hallmark pairs. We found that these active gene-hallmark pairs could stratify LGG patients into four subtypes with significantly different survival times. The two new subtypes with similar poorest prognoses were driven by two different gene sets (one including EGFR, CDKN2A, CDKN2B, INFA8, and INFA5, and the other including CDK4, AVIL, and DTX3), respectively. The SCNAs of the two gene sets could disorder the same cancer hallmark signature in a mutually exclusive manner (including E2F_TARGETS and G2M_CHECKPOINT). Compared with previous methods, our strategy could not only capture the known cancer genes and directly dissect the functional roles of their SCNAs in LGG, but also discover the functions of new driver genes in LGG, such as IFNA5, IFNA8, and DTX3. Additionally, our method can be applied to a variety of cancer types to explore the pathogenesis of driver SCNAs and improve the treatment and diagnosis of cancer.

Background: Malignant melanoma of the female genital tract is relatively uncommon and accounts for 3-7% of all melanoma localizations. This study aimed to identify driver genes in melanoma of the female genital tract with the purpose of enhancing understanding of disease pathogenesis and identifying potential new therapeutic targets to develop effective therapies.Methods: KIT (CD117) and BRAF expression were detected immunohistochemically. Polymerase Chain Reaction (PCR) and Sanger sequencing techniques were performed to identify the mutational status of BRAF, NRAS, KRAS, NF1, KIT, PDGFRA and SF3B1 on 19 melanomas of the female genital tract, paired with 25 cutaneous melanomas, 18 acral melanomas and 11 melanomas of nasal cavity.Results: Somatic variant analysis identified KRAS (6/19; 32%) as the most commonly mutated gene, followed by KIT (4/19; 21%), SF3B1 (3/19; 16%) and NRAS (1/19; 5%). None of the cases were found to harbor BRAF, NF1 and PDGFRA mutations in melanomas of the female genital tract. However, none of the cases were found to harbor SF3B1 and KIT mutations in cutaneous melanomas, acral melanomas and melanomas of nasal cavity. Recurrent KIT mutations, as well as mutations in the less frequently mutated genes NRAS and SF3B1, were exclusively detected in vulvovaginal melanomas, but not in tumors arising in the cervix. However, recurrent KRAS mutations were detected in similar frequencies in tumors of the vulva, vagina, and cervix. Additionally, recurrent KRAS and KIT mutations occurred predominantly in polygonal and epithelioid cell types of melanoma in the female genital tract. Immunohistochemistry revealed moderate or strong cytoplasmic CD117 expression in 6 of the 19 cases (31.6%).Conclusions: We observed that gynecologic melanoma harbored distinct mutation rates in the KIT, BRAF, SF3B1, KRAS, and NRAS genes. Our findings support the notion that gynecologic melanoma is a distinct entity from non-gynecologic melanoma, and these findings offer insights into future therapeutic options for these patients. [ABSTRACT FROM AUTHOR]

Somatic copy-number alterations (SCNAs) are major contributors to cancer development that are pervasive and highly heterogeneous in human cancers. However, the driver roles of SCNAs in cancer are insufficiently characterized. We combined network propagation and linear regression models to design an integrative strategy to identify driver SCNAs and dissect the functional roles of SCNAs by integrating profiles of copy number and gene expression in lower-grade glioma (LGG). We applied our strategy to 511 LGG patients and identified 98 driver genes that dysregulated 29 cancer hallmark signatures, forming 143 active gene-hallmark pairs. We found that these active gene-hallmark pairs could stratify LGG patients into four subtypes with significantly different survival times. The two new subtypes with similar poorest prognoses were driven by two different gene sets (one including EGFR, CDKN2A, CDKN2B, INFA8 , and INFA5 , and the other including CDK4, AVIL , and DTX3), respectively. The SCNAs of the two gene sets could disorder the same cancer hallmark signature in a mutually exclusive manner (including E2F_TARGETS and G2M_CHECKPOINT). Compared with previous methods, our strategy could not only capture the known cancer genes and directly dissect the functional roles of their SCNAs in LGG, but also discover the functions of new driver genes in LGG, such as IFNA5, IFNA8 , and DTX3. Additionally, our method can be applied to a variety of cancer types to explore the pathogenesis of driver SCNAs and improve the treatment and diagnosis of cancer. [ABSTRACT FROM AUTHOR]

Prostate cancer is one of the most common uro-oncological disease in men and is globally leading cause of cancer related deaths in males. The somatic mutation has a strong link in the occurrence of cancer. Mutation in the oncogenes and tumor suppressor genes that alter key cellular functions can lead to prostate cancer initiation and progression. Whole genome sequencing has identified numerous genetic alternations and further provided a detail view of the mutations in genes that drive progression of prostate cancer. TP53, SPOP, PTEN, ATM, AR, CTNNB1, FOXA1, KMT2D, BRACA2 and APC were found as frequently mutated genes in prostate cancer. Using data from cBioPortal and PubMed, this review summarizes the status and possible impact of mutations in these driver genes on survival, progression, and metastasis of prostate cancer. This study will contribute a better understanding of biological basis for clinical variability in prostate cancer patients and may provide new genetic diagnostic markers and drug targets.

Introduction: Neoplasia is defined as abnormal and excessive growth of selected tissue. Several factors, such as mutations in selected genes, can increase the risk of cancer expansion in human body. The aim of the article is to review the selected genetic factors which increase the risk of neoplasia and results of their presence in the human body. Material and methods: Articles in the Google Scholar, Pub Med database have been analysed using keywords: cancer, neoplasia, genetic factors, driver genes, cancer transformation, older people. Results: The aim of several genes is repairing damaged or dysfunctional DNA and preventing cells from abnormal or excessive. Mutations in selected genes cause inhibited production of the gene protein product or a change in its function, which increase the risk of neoplasia. The presence of mutated genes results in the initiation of the process leading to expansion of cancer cells in selected tissue. Moreover, a genetic mutation can lead to syndrome of tumors occurring in several organs. However, the increasement of cancer risk is related to numerous mutations, whereas the minority of carcinomas occur because of congenital gene defect and the majority is caused by environmental factors which contribute to creating various mutations. Conclusions: Thanks to the development of genetics in the field of medicine and introduction of genetic tests, the process of diagnosis of several tumors and syndromes is more efficient than in the past. The occurrence of mutation in genes, such as BRCA1, BRCA2, VHL, MSH2 and MLH1 and many more, relates to presence of selected tumors. By the introduction of pharmacogenetics, dozens of molecular-targeted drugs are used in the treatment of several types of cancers leading the achievement of therapeutic success. Nevertheless, the genetic background of many types of cancers is unknown and needs further study, as well as drugs targeting at selected genes mutations requires more development and guidelines in the treatment process. Keywords: cancer; neoplasia; genetic factors; driver genes; cancer transformation; older people;

Objective To screen the core-driving pathogenic genes in the occurrence and development of ependymoma (EPN), explore the involved signal pathways of its pathogenesis, and to clarify the relationship of prognosis with expression of these driver genes. Methods The differentially expressed gene (DEGs) were identified after comparing between gene expression profiles of the EPN tissues and normal tissues based on gene chip, microarray and bioinformatics. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and protein-protein interaction (PPI) network analyses were conducted to find out the enrichment functions, pathways and hub genes. After hub genes were identified, the survival analysis of 325 patients, which data obtained from Chinese Glioma Genome Atlas (CCGA), were performed to clarify the relationship between prognosis and the expression levels of the hub genes. Results Genes TP53, TOP2A, CDK1, PCNA and ACTA2 were found as core driver genes, and their aberrant expression promoted the occurrence and development of EPN; Hedgehog signaling pathway, Notch signal pathway and mismatch repair signal pathway were the high-risk signal pathways for the development of EPN. And the results of survival analysis showed that the patients with lower expression of TOP2A, CDK1, PCNA and ACTA2 had favorable prognosis and longer progression-free survival (PFS) and overall survival (OS) (P < 0.05). Conclusion In this study, TP53, TOP2A, CDK1, PCNA and ACTA2 were selected as the core driver genes of ependymoma. At the same time, we explored the high-risk signaling pathways that cause EPN, and provided new targets for clinical diagnosis and treatment of EPN, which is of great significance.

Between 2006 and 2015, the U.S. Food and Drug Administration’s (FDA) overall likelihood of approval (LOA) from Phase I clinical trials for all therapeutic candidates was 9.6%, with the highest LOA in haematology (26.1%) and the lowest in oncology (5.1%). Two critical features attributed to the success of advancing trials were i) targeting driver genes responsible for disease, and ii) use of human disease-relevant animal models during preclinical studies. For decades, conventional wisdom has been that growth factors are the drivers of wound healing, but few have either advanced to clinical applications or proven effective. The purpose of this paper is to explore heat shock protein 90-alpha (Hsp90α)’s role as a potential driver of wound healing and as a possible future therapeutic entity through a review of recent literature, including studies with human disease-relevant animal models. Of the approximately 7,000 gene products generated by a given mammalian cell type, the Hsp90 family of proteins (Hsp90α and Hsp90β) accounts for 2–3% of them. Hsp90β fulfils the role of an intracellular chaperone, but Hsp90α’s intracellular function is surprisingly dispensable. Instead, the abundancy of Hsp90α appears to have been prepared for extracellular purposes. When secreted via exosomes by cells under environmental stress, such as injury, Hsp90α protects cells from hypoxia-induced cell death, reduces local inflammation, and subsequently promotes cell migration to repair the injured tissue. Unlike conventional growth factors, secreted Hsp90α stimulates all major cell types involved in wound healing equally, resists microenvironmental inhibitors like TGFβ and hyperglycaemia, and is highly stable. Inhibition of exosome-mediated Hsp90α secretion, neutralisation of Hsp90α’s ATPase-independent extracellular functions, or interruption of Hsp90α-LRP-1 signalling blocks wound closure in vivo. Topical application of Hsp90α’s therapeutic entity, F-5 (a 115-amino acid peptide), has shown great promise for healing acute burn and diabetic wounds in mice and pigs.

Abstract Background Cancer as a worldwide problem is driven by genomic alterations. With the advent of high-throughput sequencing technology, a huge amount of genomic data generates at every second which offer many valuable cancer information and meanwhile throw a big challenge to those investigators. As the major characteristic of cancer is heterogeneity and most of alterations are supposed to be useless passenger mutations that make no contribution to the cancer progress. Hence, how to dig out driver genes that have effect on a selective growth advantage in tumor cells from those tremendously and noisily data is still an urgent task. Results Considering previous network-based method ignoring some important biological properties of driver genes and the low reliability of gene interactive network, we proposed a random walk method named as Subdyquency that integrates the information of subcellular localization, variation frequency and its interaction with other dysregulated genes to improve the prediction accuracy of driver genes. We applied our model to three different cancers: lung, prostate and breast cancer. The results show our model can not only identify the well-known important driver genes but also prioritize the rare unknown driver genes. Besides, compared with other existing methods, our method can improve the precision, recall and fscore to a higher level for most of cancer types. Conclusions The final results imply that driver genes are those prone to have higher variation frequency and impact more dysregulated genes in the common significant compartment. Availability The source code can be obtained at https://github.com/weiba/Subdyquency.

Abstract Background Identifying cancer driver genes (CDG) is a crucial step in cancer genomic toward the advancement of precision medicine. However, driver gene discovery is a very challenging task because we are not only dealing with huge amount of data; but we are also faced with the complexity of the disease including the heterogeneity of background somatic mutation rate in each cancer patient. It is generally accepted that CDG harbor variants conferring growth advantage in the malignant cell and they are positively selected, which are critical to cancer development; whereas, non-driver genes harbor random mutations with no functional consequence on cancer. Based on this fact, function prediction based approaches for identifying CDG have been proposed to interrogate the distribution of functional predictions among mutations in cancer genomes (eLS 1–16, 2016). Assuming most of the observed mutations are passenger mutations and given the quantitative predictions for the functional impact of the mutations, genes enriched of functional or deleterious mutations are more likely to be drivers. The promises of these methods have been continually refined and can therefore be applied to increase accuracy in detecting new candidate CDGs. However, current function prediction based approaches only focus on coding mutations and lack a systematic way to pick the best mutation deleteriousness prediction algorithms for usage. Results In this study, we propose a new function prediction based approach to discover CDGs through a gene-based permutation approach. Our method not only covers both coding and non-coding regions of the genes; but it also accounts for the heterogeneous mutational context in cohort of cancer patients. The permutation model was implemented independently using seven popular deleteriousness prediction scores covering splicing regions (SPIDEX), coding regions (MetaLR, and VEST3) and pan-genome (CADD, DANN, Fathmm-MKL coding and Fathmm-MKL noncoding). We applied this new approach to somatic single nucleotide variants (SNVs) from whole-genome sequences of 119 breast and 24 lung cancer patients and compared the seven deleteriousness prediction scores for their performance in this study. Conclusion The new function prediction based approach not only predicted known cancer genes listed in the Cancer Gene Census (CGC), but also new candidate CDGs that are worth further investigation. The results showed the advantage of utilizing pan-genome deleteriousness prediction scores in function prediction based methods. Although VEST3 score, a deleteriousness prediction score for missense mutations, has the best performance in breast cancer, it was topped by CADD and Fathmm-MKL coding, two pan-genome deleteriousness prediction scores, in lung cancer.

Cushing’s disease represents 60–70% of all cases of Cushing’s syndrome, presenting with a constellation of clinical features associated with sustained hypercortisolism. Molecular alterations in corticotrope cells lead to the formation of ACTH-secreting adenomas, with subsequent excessive production of endogenous glucocorticoids. In the last few years, many authors have contributed to analyzing the etiopathogenesis and pathophysiology of corticotrope adenomas, which still need to be fully clarified. New molecular modifications such as somatic mutations of USP8 and other genes have been identified, and several case series and case reports have been published, highlighting new molecular alterations that need to be explored. To investigate the current knowledge of the genetics of ACTH-secreting adenomas, we performed a bibliographic search of the recent scientific literature to identify all pertinent articles. This review presents the most recent updates on somatic and germline mutations underlying Cushing’s disease. The prognostic implications of these mutations, in terms of clinical outcomes and therapeutic scenarios, are still debated. Further research is needed to define the clinical features associated with the different genotypes and potential pharmacological targets.

Recent advances in sequencing technologies and the discovery of non-coding RNAs (ncRNAs) have provided new insights in the molecular pathogenesis of cancers. Several studies have implicated the role of ncRNAs, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and recently discovered circular RNAs (circRNAs) in tumorigenesis and metastasis. Unlike linear RNAs, circRNAs are highly stable and closed-loop RNA molecules. It has been established that circRNAs regulate gene expression by controlling the functions of miRNAs and RNA-binding protein (RBP) or by translating into proteins. The circRNA–miRNA–mRNA regulatory axis is associated with human diseases, such as cancers, Alzheimer’s disease, and diabetes. In this study, we explored the interaction among circRNAs, miRNAs, and their target genes in various cancers using state-of-the-art bioinformatics tools. We identified differentially expressed circRNAs, miRNAs, and mRNAs on multiple cancers from publicly available data. Furthermore, we identified many crucial drivers and tumor suppressor genes in the circRNA–miRNA–mRNA regulatory axis in various cancers. Together, this study data provide a deeper understanding of the circRNA–miRNA–mRNA regulatory mechanisms in cancers.