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Background: The molecular mechanisms and signaling pathways involved in tooth morphogenesis have been the research focus in the fields of tooth and bone development. However, the cell population in molars at the late bell stage and the mechanisms of hard tissue formation and mineralization remain limited knowledge. Results: Here, we used the rat mandibular first and second molars as models to perform single-cell RNA sequencing (scRNA-seq) analysis to investigate cell identity and driver genes related to dental mesenchymal cell differentiation during the late bell hard tissue formation stage. We identified seven main cell types and investigated the heterogeneity of mesenchymal cells. Subsequently, we identified novel cell marker genes, including Pclo in dental follicle cells, Wnt10a in pre-odontoblasts, Fst and Igfbp2 in periodontal ligament cells, and validated the expression of Igfbp3 in the apical pulp. The dynamic model revealed three differentiation trajectories within mesenchymal cells, originating from two types of dental follicle cells and apical pulp cells. Apical pulp cell differentiation is associated with the genes Ptn and Satb2, while dental follicle cell differentiation is associated with the genes Tnc, Vim, Slc26a7, and Fgfr1. Cluster-specific regulons were analyzed by pySCENIC. In addition, the odontogenic function of driver gene TNC was verified in the odontoblastic differentiation of human dental pulp stem cells. The expression of osteoclast differentiation factors was found to be increased in macrophages of the mandibular first molar. Conclusions: Our results revealed the cell heterogeneity of molars in the late bell stage and identified driver genes associated with dental mesenchymal cell differentiation. These findings provide potential targets for diagnosing dental hard tissue diseases and tooth regeneration. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion Patients with non‐small cell lung cancer (NSCLC) with liver metastasis have a poor prognosis, and there are no reliable biomarkers for predicting disease progression. Currently, no recognized and reliable prediction model exists to anticipate liver metastasis in NSCLC, nor have the risk factors influencing its onset time been thoroughly explored.This study conducted a retrospective analysis of 434 NSCLC patients from two hospitals to assess the association between the risk and timing of liver metastasis, as well as several variables.The patients were divided into two groups: those without liver metastasis and those with liver metastasis. We constructed a nomogram model for predicting liver metastasis in NSCLC, incorporating elements such as T stage, N stage, M stage, lack of past radical lung cancer surgery, and programmed death ligand 1 (PD‐L1) levels. Furthermore, NSCLC patients with wild‐type EGFR, no prior therapy with tyrosine kinase inhibitors (TKIs), and no prior radical lung cancer surgery showed an elevated risk of early liver metastasis.In conclusion, the nomogram model developed in this study has the potential to become a simple, intuitive, and customizable clinical tool for assessing the risk of liver metastasis in NSCLC patients following validation. Furthermore, it provides a framework for investigating the timing of metachronous liver metastasis. [ABSTRACT FROM AUTHOR]

Abstract Background The molecular mechanisms and signaling pathways involved in tooth morphogenesis have been the research focus in the fields of tooth and bone development. However, the cell population in molars at the late bell stage and the mechanisms of hard tissue formation and mineralization remain limited knowledge. Results Here, we used the rat mandibular first and second molars as models to perform single-cell RNA sequencing (scRNA-seq) analysis to investigate cell identity and driver genes related to dental mesenchymal cell differentiation during the late bell hard tissue formation stage. We identified seven main cell types and investigated the heterogeneity of mesenchymal cells. Subsequently, we identified novel cell marker genes, including Pclo in dental follicle cells, Wnt10a in pre-odontoblasts, Fst and Igfbp2 in periodontal ligament cells, and validated the expression of Igfbp3 in the apical pulp. The dynamic model revealed three differentiation trajectories within mesenchymal cells, originating from two types of dental follicle cells and apical pulp cells. Apical pulp cell differentiation is associated with the genes Ptn and Satb2, while dental follicle cell differentiation is associated with the genes Tnc, Vim, Slc26a7, and Fgfr1. Cluster-specific regulons were analyzed by pySCENIC. In addition, the odontogenic function of driver gene TNC was verified in the odontoblastic differentiation of human dental pulp stem cells. The expression of osteoclast differentiation factors was found to be increased in macrophages of the mandibular first molar. Conclusions Our results revealed the cell heterogeneity of molars in the late bell stage and identified driver genes associated with dental mesenchymal cell differentiation. These findings provide potential targets for diagnosing dental hard tissue diseases and tooth regeneration.

The hyperplasia–carcinoma sequence is a stepwise tumourigenic programme towards endometrial cancer in which normal endometrial epithelium becomes neoplastic through non‐atypical endometrial hyperplasia (NAEH) and atypical endometrial hyperplasia (AEH), under the influence of unopposed oestrogen. NAEH and AEH are known to exhibit polyclonal and monoclonal cell growth, respectively; yet, aside from focal PTEN protein loss, the genetic and epigenetic alterations that occur during the cellular transition remain largely unknown. We sought to explore the potential molecular mechanisms that promote the NAEH–AEH transition and identify molecular markers that could help to differentiate between these two states. We conducted target‐panel sequencing on the coding exons of 596 genes, including 96 endometrial cancer driver genes, and DNA methylome microarrays for 48 NAEH and 44 AEH lesions that were separately collected via macro‐ or micro‐dissection from the endometrial tissues of 30 cases. Sequencing analyses revealed acquisition of the PTEN mutation and the clonal expansion of tumour cells in AEH samples. Further, across the transition, alterations to the DNA methylome were characterised by hypermethylation of promoter/enhancer regions and CpG islands, as well as hypo‐ and hyper‐methylation of DNA‐binding regions for transcription factors relevant to endometrial cell differentiation and/or tumourigenesis, including FOXA2, SOX17, and HAND2. The identified DNA methylation signature distinguishing NAEH and AEH lesions was reproducible in a validation cohort with modest discriminative capability. These findings not only support the concept that the transition from NAEH to AEH is an essential step within neoplastic cell transformation of endometrial epithelium but also provide deep insight into the molecular mechanism of the tumourigenic programme. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

In recent years, rapid advancement in gene/protein analysis technology has resulted in target molecule identification that may be useful in cancer treatment. Therefore, "Clinical Practice Guidelines for Molecular Tumor Marker, Second Edition" was published in Japan in September 2021. These guidelines were established to align the clinical usefulness of external diagnostic products with the evaluation criteria of the Pharmaceuticals and Medical Devices Agency. The guidelines were scoped for each tumor, and a clinical questionnaire was developed based on a serious clinical problem. This guideline was based on a careful review of the evidence obtained through a literature search, and recommendations were identified following the recommended grades of the Medical Information Network Distribution Services (Minds). Therefore, this guideline can be a tool for cancer treatment in clinical practice. We have already reported the review portion of "Clinical Practice Guidelines for Molecular Tumor Marker, Second Edition" as Part 1. Here, we present the English version of each part of the Clinical Practice Guidelines for Molecular Tumor Marker, Second Edition. [ABSTRACT FROM AUTHOR]

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.

Objective: To evaluate the incidence, clinical laboratory characteristics, and gene mutation spectrum of Ph‐negative MPN patients with atypical variants of JAK2, MPL, or CALR. Methods: We collected a total of 359 Ph‐negative MPN patients with classical mutations in driver genes JAK2, MPL, or CALR, and divided them into two groups based on whether they had additional atypical variants of driver genes JAK2, MPL, or CALR: 304 patients without atypical variants of driver genes and 55 patients with atypical variants of driver genes. We analyzed the relevant characteristics of these patients. Results: This study included 359 patients with Ph‐negative MPNs with JAK2, MPL, or CALR classical mutations and found that 55 (15%) patients had atypical variants of JAK2, MPL, or CALR. Among them, 28 cases (51%) were male, and 27 (49%) were female, with a median age of 64 years (range, 21–83). The age of ET patients with atypical variants was higher than that of ET patients without atypical variants [70 (28–80) vs. 61 (19–82), p = 0.03]. The incidence of classical MPL mutations in ET patients with atypical variants was higher than in ET patients without atypical variants [13.3% (2/15) vs. 0% (0/95), p = 0.02]. The number of gene mutations in patients with atypical variants of driver genes PV, ET, and Overt‐PMF is more than in patients without atypical variants of PV, ET, and Overt‐PMF [PV: 3 (2–6) vs. 2 (1–7), p < 0.001; ET: 4 (2–8) vs. 2 (1–7), p < 0.05; Overt‐PMF: 5 (2–9) vs. 3 (1–8), p < 0.001]. The incidence of SH2B3 and ASXL1 mutations were higher in MPN patients with atypical variants than in those without atypical variants (SH2B3: 16% vs. 6%, p < 0.01; ASXL1: 24% vs. 13%, p < 0.05). Conclusion: These data indicate that classical mutations of JAK2, MPL, and CALR may not be completely mutually exclusive with atypical variants of JAK2, MPL, and CALR. In this study, 30 different atypical variants of JAK2, MPL, and CALR were identified, JAK2 G127D being the most common (42%, 23/55). Interestingly, JAK2 G127D only co‐occurred with JAK2V617F mutation. The incidence of atypical variants of JAK2 in Ph‐negative MPNs was much higher than that of the atypical variants of MPL and CALR. The significance of these atypical variants will be further studied in the future. [ABSTRACT FROM AUTHOR]

Simple Summary: Identifying cancer driver genes plays a significant role in cancer diagnosis and treatment. With the advancement of next-generation sequencing technologies, a wealth of multi-omics cancer data, including genomic, epigenomic, and transcriptomic data, are now available for cancer research. Integrating these data to effectively identify cancer driver genes causally associated with cancer is a computational challenge. Methods for identifying cancer driver genes are mainly based on population levels. Considering the trend of precision medicine and the heterogeneity of patients, it is challenging but crucial to identify cancer driver genes at the individual level. We developed a method called PDGCN (Personalized Drivers of GCN), which constructs sample–gene interaction networks by integrating multiple types of data features and using network structural features extracted from Node2vec. Then, a graphical convolutional neural network model with a conditional random field layer is used to prioritize candidate driver genes in the network. The results show that PDGCN can identify driver genes at the individual level, providing a new perspective for predicting driver genes in individual samples. Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with high heterogeneity. Thus, the discovery of driver genes at the individual level is becoming more valuable but is a great challenge. Although there have been some computational methods proposed to tackle this challenge, few can cover all patient samples well, and there is still room for performance improvement. In this study, to identify individual-level driver genes more efficiently, we propose the PDGCN method. PDGCN integrates multiple types of data features, including mutation, expression, methylation, copy number data, and system-level gene features, along with network structural features extracted using Node2vec in order to construct a sample–gene interaction network. Prediction is performed using a graphical convolutional neural network model with a conditional random field layer, which is able to better combine the network structural features with biological attribute features. Experiments on the ACC (Adrenocortical Cancer) and KICH (Kidney Chromophobe) datasets from TCGA (The Cancer Genome Atlas) demonstrated that the method performs better compared to other similar methods. It can identify not only frequently mutated driver genes, but also rare candidate driver genes and novel biomarker genes. The results of the survival and enrichment analyses of these detected genes demonstrate that the method can identify important driver genes at the individual level. [ABSTRACT FROM AUTHOR]

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks. [ABSTRACT FROM AUTHOR]

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.

The paper describes a method for predicting genes associated with the development of cancer. The method applies the convolutional neural network for the purpose of predicting disease driver genes. Distinctive features of the method are the use of gene expression data to determine the topological structure of the network, the efficiency of prediction with limited information about genes associated with the disease, and the possibility of jointly including information on mutations and similarity of gene expression profiles to improve the accuracy of prediction.

According to the Cancer Genome Atlas (TCGA), gastric cancers are classified into four molecular subtypes: Epstein–Barr virus-positive (EBV+), tumors with microsatellite instability (MSI), tumors with chromosomal instability (CIN), and genomically stable (GS) tumors. However, the gastric cancer (GC) with chromosomal instability remains insufficiently described and does not have effective markers for molecular and histological verification and diagnosis. The CIN subtype of GC is characterized by chromosomal instability, which is manifested by an increased frequency of aneuploidies and/or structural chromosomal rearrangements in tumor cells. Structural rearrangements in the CIN subtype of GC are not accidental and are commonly detected in chromosomal loci, being abnormal because of specific structural organization. The causes of CIN are still being discussed; however, according to recent data, aberrations in the TP53 gene may cause CIN development or worsen its phenotype. Clinically, patients with the CIN subtype of GC demonstrate poor survival, but receive the maximum benefit from adjuvant chemotherapy. In the review, we consider the molecular mechanisms and possible causes of chromosomal instability in GC, the common rearrangements of chromosomal loci and their impact on the development and clinical course of the disease, as well as the driver genes, their functions, and perspectives on their targeting in the CIN subtype of GC. [ABSTRACT FROM AUTHOR]

Cancer is a disease with a complex genome of altered functions. However, most existing driver gene identification approaches rarely consider driver genes may have the same functional properties. To overcome this issue, we propose the gamma distribution test for the driver gene identification based on similarity networks, termed GASN, which identifies driver genes by combining machine learning and distributional statistics methods. Similarity networks are able to learn gene similarities and key features that represent the functional impact of genes. In addition, we classify genes into different cellular compartments and use the gamma distribution test within cellular compartments to identify significant driver genes. The experimental results show that our method outperforms the other 17 comparative methods. [ABSTRACT FROM AUTHOR]

Abstract Objective To evaluate the incidence, clinical laboratory characteristics, and gene mutation spectrum of Ph‐negative MPN patients with atypical variants of JAK2, MPL, or CALR. Methods We collected a total of 359 Ph‐negative MPN patients with classical mutations in driver genes JAK2, MPL, or CALR, and divided them into two groups based on whether they had additional atypical variants of driver genes JAK2, MPL, or CALR: 304 patients without atypical variants of driver genes and 55 patients with atypical variants of driver genes. We analyzed the relevant characteristics of these patients. Results This study included 359 patients with Ph‐negative MPNs with JAK2, MPL, or CALR classical mutations and found that 55 (15%) patients had atypical variants of JAK2, MPL, or CALR. Among them, 28 cases (51%) were male, and 27 (49%) were female, with a median age of 64 years (range, 21–83). The age of ET patients with atypical variants was higher than that of ET patients without atypical variants [70 (28–80) vs. 61 (19–82), p = 0.03]. The incidence of classical MPL mutations in ET patients with atypical variants was higher than in ET patients without atypical variants [13.3% (2/15) vs. 0% (0/95), p = 0.02]. The number of gene mutations in patients with atypical variants of driver genes PV, ET, and Overt‐PMF is more than in patients without atypical variants of PV, ET, and Overt‐PMF [PV: 3 (2–6) vs. 2 (1–7), p

Lung cancer is the leading cause of cancer death all over the world. The majority (80-85 %) of lung cancer cases are classified as non-small cell lung cancer (NSCLC). Within NSCLC, adenocarcinoma (AC) and squamous cell carcinoma (SCC) are the most often recognized. The histological and immunohistochemical examination of NSCLC is a basic diagnostic tool, but insufficient for comprehensive therapeutic decisions. In some NSCLC patients, mainly adenocarcinoma, molecular alterations in driver genes, like EGFR, KRAS, HER2, ALK, MET, BRAF, RET, ROS1, and NTRK are recognized. The frequency of some of those changes is different depending on race, and between smokers and non-smokers. The molecular diagnostics of NSCLC using modern methods, like next-generation sequencing, is essential in estimating targeted, personalized therapy. In recent years, a breakthrough in understanding the importance of molecular studies for the precise treatment of NSCLC has been observed. Many new drugs were approved, including tyrosine kinase and immune checkpoint inhibitors. Clinical trials testing novel molecules like miRNAs and trials with CAR-T cells (chimeric antigen receptor – T cells) dedicated to NSCLC patients are ongoing.

High‐risk endometrial cancer has poor prognosis and is increasing in incidence. However, understanding of the molecular mechanisms which drive this disease is limited. We used genetically engineered mouse models (GEMM) to determine the functional consequences of missense and loss of function mutations in Fbxw7, Pten and Tp53, which collectively occur in nearly 90% of high‐risk endometrial cancers. We show that Trp53 deletion and missense mutation cause different phenotypes, with the latter a substantially stronger driver of endometrial carcinogenesis. We also show that Fbxw7 missense mutation does not cause endometrial neoplasia on its own, but potently accelerates carcinogenesis caused by Pten loss or Trp53 missense mutation. By transcriptomic analysis, we identify LEF1 signalling as upregulated in Fbxw7/FBXW7‐mutant mouse and human endometrial cancers, and in human isogenic cell lines carrying FBXW7 mutation, and validate LEF1 and the additional Wnt pathway effector TCF7L2 as novel FBXW7 substrates. Our study provides new insights into the biology of high‐risk endometrial cancer and suggests that targeting LEF1 may be worthy of investigation in this treatment‐resistant cancer subgroup. Synopsis: The consequences of common endometrial driver mutations were studied in mice, and found to exert different functional effects alone and in combination.FBXW7 mutation requires additional alterations to exert oncogenic effect in endometriaWnt pathway effectors LEF1 and TCF7L2 are novel FBXW7 substrates and potential therapeutic targets in FBXW7‐mutant endometrial cancer [ABSTRACT FROM AUTHOR]

Cancer driver genes are critical in driving tumor cell growth, and precisely identifying these genes is crucial in advancing our understanding of cancer pathogenesis and developing targeted cancer drugs. Despite the current methods for discovering cancer driver genes that mainly rely on integrating multi-omics data, many existing models are overly complex, and it is difficult to interpret the results accurately. This study aims to address this issue by introducing InDEP, an interpretable machine learning framework based on cascade forests. InDEP is designed with easy-to-interpret features, cascade forests based on decision trees and a KernelSHAP module that enables fine-grained post-hoc interpretation. Integrating multi-omics data, InDEP can identify essential features of classified driver genes at both the gene and cancer-type levels. The framework accurately identifies driver genes, discovers new patterns that make genes as driver genes and refines the cancer driver gene catalog. In comparison with state-of-the-art methods, InDEP proved to be more accurate on the test set and identified reliable candidate driver genes. Mutational features were the primary drivers for InDEP's identifying driver genes, with other omics features also contributing. At the gene level, the framework concluded that substitution-type mutations were the main reason most genes were identified as driver genes. InDEP's ability to identify reliable candidate driver genes opens up new avenues for precision oncology and discovering new biomedical knowledge. This framework can help advance cancer research by providing an interpretable method for identifying cancer driver genes and their contribution to cancer pathogenesis, facilitating the development of targeted cancer drugs. [ABSTRACT FROM AUTHOR]

Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2 , SF3B4 , KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4 , KIF26B , ARL15 , ARHGAP25 , EMCN , GMFG , TPK1 , NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer research has been increasingly focusing on identifying genes and molecular pathways that drive the disease. In this context, protein–protein interaction networks, which provide insights into the interactions among proteins within a cell, have proven particularly useful. However, the effectiveness of existing approaches can be influenced by the specific network used, as different networks can have different topological structures. In addition, newer context-specific networks often come with incomplete structures, which complicates the analysis. To address these challenges, we propose a new method, called MultiFDRnet, that can identify driver genes and pathways using multiple protein–protein interaction (PPI) networks. Here, the false discovery rate (FDR) refers to the proportion of non-cancer genes within identified subnetworks. Our method, tested on both simulated and real cancer data, has been able to identify important subnetworks that are supported by multiple PPI networks and reveal novel modular structures in context-specific PPI networks. The software that we developed to implement this method is freely available for other researchers to use. Background: The identification of cancer driver genes and key molecular pathways has been the focus of large-scale cancer genome studies. Network-based methods detect significantly perturbed subnetworks as putative cancer pathways by incorporating genomics data with the topological information of PPI networks. However, commonly used PPI networks have distinct topological structures, making the results of the same method vary widely when applied to different networks. Furthermore, emerging context-specific PPI networks often have incomplete topological structures, which pose serious challenges for existing subnetwork detection algorithms. Methods: In this paper, we propose a novel method, referred to as MultiFDRnet, to address the above issues. The basic idea is to model a set of PPI networks as a multiplex network to preserve the topological structure of individual networks, while introducing dependencies among them, and, then, to detect significantly perturbed subnetworks on the modeled multiplex network using all the structural information simultaneously. Results: To illustrate the effectiveness of the proposed approach, an extensive benchmark analysis was conducted on both simulated and real cancer data. The experimental results showed that the proposed method is able to detect significantly perturbed subnetworks jointly supported by multiple PPI networks and to identify novel modular structures in context-specific PPI networks. [ABSTRACT FROM AUTHOR]

Abstract Background TP53 is a master tumor suppressor gene, mutated in approximately half of all human cancers. Given the many regulatory roles of the corresponding p53 protein, it is possible to infer loss of p53 activity – which may occur due to alterations in trans – from gene expression patterns. Several such alterations that phenocopy p53 loss are known, however additional ones may exist, but their identity and prevalence among human tumors are not well characterized. Results We perform a large-scale statistical analysis on transcriptomes of ~ 7,000 tumors and ~ 1,000 cell lines, estimating that 12% and 8% of tumors and cancer cell lines, respectively, phenocopy TP53 loss: they are likely deficient in the activity of the p53 pathway, while not bearing obvious TP53 inactivating mutations. While some of these cases are explained by amplifications in the known phenocopying genes MDM2, MDM4 and PPM1D, many are not. An association analysis of cancer genomic scores jointly with CRISPR/RNAi genetic screening data identified an additional common TP53-loss phenocopying gene, USP28. Deletions in USP28 are associated with a TP53 functional impairment in 2.9–7.6% of breast, bladder, lung, liver and stomach tumors, and have comparable effect size to MDM4 amplifications. Additionally, in the known copy number alteration (CNA) segment harboring MDM2, we identify an additional co-amplified gene (CNOT2) that may cooperatively boost the TP53 functional inactivation effect of MDM2. An analysis of cancer cell line drug screens using phenocopy scores suggests that TP53 (in)activity commonly modulates associations between anticancer drug effects and various genetic markers, such as PIK3CA and PTEN mutations, and should thus be considered as a drug activity modifying factor in precision medicine. As a resource, we provide the drug-genetic marker associations that differ depending on TP53 functional status. Conclusions Human tumors that do not bear obvious TP53 genetic alterations but that phenocopy p53 activity loss are common, and the USP28 gene deletions are one likely cause.

Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with high heterogeneity. Thus, the discovery of driver genes at the individual level is becoming more valuable but is a great challenge. Although there have been some computational methods proposed to tackle this challenge, few can cover all patient samples well, and there is still room for performance improvement. In this study, to identify individual-level driver genes more efficiently, we propose the PDGCN method. PDGCN integrates multiple types of data features, including mutation, expression, methylation, copy number data, and system-level gene features, along with network structural features extracted using Node2vec in order to construct a sample–gene interaction network. Prediction is performed using a graphical convolutional neural network model with a conditional random field layer, which is able to better combine the network structural features with biological attribute features. Experiments on the ACC (Adrenocortical Cancer) and KICH (Kidney Chromophobe) datasets from TCGA (The Cancer Genome Atlas) demonstrated that the method performs better compared to other similar methods. It can identify not only frequently mutated driver genes, but also rare candidate driver genes and novel biomarker genes. The results of the survival and enrichment analyses of these detected genes demonstrate that the method can identify important driver genes at the individual level.

Abstract High‐risk endometrial cancer has poor prognosis and is increasing in incidence. However, understanding of the molecular mechanisms which drive this disease is limited. We used genetically engineered mouse models (GEMM) to determine the functional consequences of missense and loss of function mutations in Fbxw7, Pten and Tp53, which collectively occur in nearly 90% of high‐risk endometrial cancers. We show that Trp53 deletion and missense mutation cause different phenotypes, with the latter a substantially stronger driver of endometrial carcinogenesis. We also show that Fbxw7 missense mutation does not cause endometrial neoplasia on its own, but potently accelerates carcinogenesis caused by Pten loss or Trp53 missense mutation. By transcriptomic analysis, we identify LEF1 signalling as upregulated in Fbxw7/FBXW7‐mutant mouse and human endometrial cancers, and in human isogenic cell lines carrying FBXW7 mutation, and validate LEF1 and the additional Wnt pathway effector TCF7L2 as novel FBXW7 substrates. Our study provides new insights into the biology of high‐risk endometrial cancer and suggests that targeting LEF1 may be worthy of investigation in this treatment‐resistant cancer subgroup.

The development of high throughput sequencing technologies i.e. Next Generation Sequencing (NGS) is revolutionizing the exploration of cancer. Though sequence datasets are highly complex, mutation can occur randomly in DNA or RNA sequences that can make cells sicker or less fit. The unusual growth and behavior of genes in cells cause cancer. Cancer-driver gene cells grow when mutation occurs. Identification of cancer driver genes is a critical and challenging issue for researchers. In the proposed work, initially, robust features are extracted from the sequence dataset through Position Relative Incidence Matrix (PRIM) integrated with Accumulative Absolute Position Incidence Vector (AAPIV) generation. PRIM and AAPIV convert the single-dimensional sequence data into 2-dimensional numeric data. Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) are used to train the model. The proposed model is validated with different validation methods i.e., independent testing, k-fold cross-validation, self-consistency, and jackknife testing. The proposed model predicts whether the given primary structure corresponds to cancer driver genes or not. Results analyses show 95%, 92%, and 69% accuracy on RF, Artificial Neural Networks (ANN), and SVM respectively. The comparative analysis with existing state-of-the-art models i.e., 20/20+ and Multimodal Deep Neural Network by integrating Multi-dimensional Data (NDNNMD) shows that the proposed model outperforms the existing techniques.

Abstract 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.

Background: TP53 is a master tumor suppressor gene, mutated in approximately half of all human cancers. Given the many regulatory roles of the corresponding p53 protein, it is possible to infer loss of p53 activity – which may occur due to alterations in trans – from gene expression patterns. Several such alterations that phenocopy p53 loss are known, however additional ones may exist, but their identity and prevalence among human tumors are not well characterized. Results: We perform a large-scale statistical analysis on transcriptomes of ~ 7,000 tumors and ~ 1,000 cell lines, estimating that 12% and 8% of tumors and cancer cell lines, respectively, phenocopy TP53 loss: they are likely deficient in the activity of the p53 pathway, while not bearing obvious TP53 inactivating mutations. While some of these cases are explained by amplifications in the known phenocopying genes MDM2, MDM4 and PPM1D, many are not. An association analysis of cancer genomic scores jointly with CRISPR/RNAi genetic screening data identified an additional common TP53-loss phenocopying gene, USP28. Deletions in USP28 are associated with a TP53 functional impairment in 2.9–7.6% of breast, bladder, lung, liver and stomach tumors, and have comparable effect size to MDM4 amplifications. Additionally, in the known copy number alteration (CNA) segment harboring MDM2, we identify an additional co-amplified gene (CNOT2) that may cooperatively boost the TP53 functional inactivation effect of MDM2. An analysis of cancer cell line drug screens using phenocopy scores suggests that TP53 (in)activity commonly modulates associations between anticancer drug effects and various genetic markers, such as PIK3CA and PTEN mutations, and should thus be considered as a drug activity modifying factor in precision medicine. As a resource, we provide the drug-genetic marker associations that differ depending on TP53 functional status. Conclusions: Human tumors that do not bear obvious TP53 genetic alterations but that phenocopy p53 activity loss are common, and the USP28 gene deletions are one likely cause. [ABSTRACT FROM AUTHOR]

Lung adenocarcinoma is one of the most common cancers in the world, and accurate diagnosis of lung nodules is an important factor in reducing its mortality. In the diagnosis of pulmonary nodules, artificial intelligence (AI) assisted diagnosis technology has been rapidly developed, so testing its effectiveness is conducive to promoting its important role in clinical practice. This paper introduces the background of early lung adenocarcinoma and lung nodule AI medical imaging, and then makes academic research on early lung adenocarcinoma and AI medical imaging, and finally summarizes the biological information. In the experimental part, the relationship analysis of 4 driver genes in group X and group Y showed that there were more abnormal invasive lung adenocarcinoma genes, and the maximum uptake value and uptake function of metabolic value were also higher. However, there was no significant correlation between mutations in the four driver genes and metabolic values, and the average accuracy of AI-based medical images was 3.88% higher than that of traditional images. [ABSTRACT FROM AUTHOR]

Simple Summary: Nasopharyngeal carcinoma (NPC) is extremely frequent in several regions of the world, particularly Southern China (known as the Cantonese cancer). Previous studies were often limited to small cohorts, and systematic comparison with other cancer types has not been explored. In this study, we collected the largest NPC cohort (n = 431) and provided the first integrative analysis of NPC genomes. We identified many novel drivers and mutational signatures in NPC. By comparing NPC with other cancer types, we discovered many unique processes driving a viral-positive cancer type. As one of few viral-positive cancers, nasopharyngeal carcinoma (NPC) is extremely rare across the world but very frequent in several regions of the world, including Southern China (known as the Cantonese cancer). Even though several genomic studies have been conducted for NPC, their sample sizes are relatively small and systematic comparison with other cancer types has not been explored. In this study, we collected four-hundred-thirty-one samples from six previous studies and provided the first integrative analysis of NPC genomes. Combining several statistical methods for detecting driver genes, we identified 25 novel drivers for NPC, including ATG14 and NLRC5. Many of these novel drivers are enriched in several important pathways, such as autophagy and immunity. By comparing NPC with many other cancer types, we found NPC is a unique cancer type in which a high proportion of patients (45.2%) do not have any known driver mutations (termed as "missing driver events") but have a preponderance of deletion events, including chromosome 3p deletion. Through signature analysis, we identified many known and novel signatures, including single-base signatures (n = 12), double-base signatures (n = 1), indel signatures (n = 9) and copy number signatures (n = 8). Many of these new signatures are involved in DNA repair and have unknown etiology and genome instability, implying an unprecedented dynamic mutational process possibly driven by complex interactions between viral and host genomes. By combining clinical, molecular and intra-tumor heterogeneity features, we constructed the first integrative survival model for NPC, providing a strong basis for patient prognosis and stratification. Taken together, we have performed one of the first integrative analyses of NPC genomes and brought unique genomic insights into tumorigenesis of a viral-driven cancer. [ABSTRACT FROM AUTHOR]

Background: Sputum cell‐free DNA (cfDNA) has been confirmed to be a valued surrogate sample for detection of EGFR mutations in patients with lung adenocarcinoma (LAC). Whether it is suitable for detection of mutations of multiple driver genes has not been reported. Methods: A total of 83 patients with LAC were enrolled and their sputum and paired tumor samples were collected. A next‐generation sequencing (NGS)‐based 10‐gene panel was used to test sputum supernatant‐derived cfDNA and paired tumor DNA. The sputum sediments were used for cytological evaluation. Results: The total positive rates of hotspot mutations of the 10 driver genes in sputum cfDNA and matched tissue samples were 65.1% and 77.1%, respectively. The overall detection sensitivity of variants in sputum cfDNA was 81.3% (95% confidence interval [CI], 69.2%, 89.5%) and the specificity was 100% (95% CI, 79.1%, 100%). The sensitivities of testing sputum cfDNA from patients with stage IIIB‐IV was 87.0% (95% CI, 74.5%, 94.1%); the sensitivities of testing sputum cfDNA from patients with malignant sputum was 92.3% (95% CI, 78.0%, 98.0%); and the sensitivity of testing sputum cfDNA from patients with malignant sputum in stage IIIB‐IV were 94.1% (95% CI, 78.9%, 99.0%). Conclusions: This study demonstrated that sputum cfDNA were successfully used for the detection of multiple driver genes by NGS. Sputum cfDNA could be a valuable surrogate clinical sample for all‐in‐one test of mutations to guide target therapies, especially for patients with advanced LAC and malignant sputum. This study demonstrated that sputum cell‐free DNA (cfDNA) can be successfully used for the detection of multiple driver genes by next‐generation sequencing. Sputum cfDNA could be a valuable surrogate clinical sample for an all‐in‐one test of mutations to guide target therapies, especially for patients with advanced lung adenocarcinoma and malignant sputum. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC), the main type of liver cancer, is the second most lethal tumor worldwide, with a 5-year survival rate of only 18%. Driver genes facilitate cancer cell growth and spread in the tumor microenvironment. Here, a comprehensive driver gene signature for the prognosis of HCC was developed. Methods: HCC driver genes were analyzed comprehensively to develop a better prognostic signature. The dataset of HCC patients included mRNA sequencing data and clinical information from the TCGA, the ICGC, and the Guangxi Medical University Cancer Hospital cohorts. First, LASSO was performed to develop a prognostic signature for differentially expressed driver genes in the TCGA cohort. Then, the robustness of the signature was assessed using survival and time-dependent ROC curves. Furthermore, independent predictors were determined using univariate and multivariate Cox regression analyses. Stepwise multi-Cox regression analysis was employed to identify significant variables for the construction of a nomogram that predicts survival rates. Functional analysis by Spearman correlation analysis, enrichment analysis (GO, KEGG, and GSEA), and immunoassay (ssGSEA and xCell) were performed. Result: A 4-driver gene signature (CLTC, DNMT3A, GMPS, and NRAS) was successfully constructed and showed excellent predictive efficiency in three cohorts. The nomogram indicated high predictive accuracy for the 1-, 3-, and 5-year prognoses of HCC patients, which included clinical information and risk score. Enrichment analysis revealed that driver genes were involved in regulating oncogenic processes, including the cell cycle and metabolic pathways, which were associated with the progression of HCC. ssGSEA and xCell showed differences in immune infiltration and the immune microenvironment between the two risk groups. Conclusion: The 4-driver gene signature is closely associated with the survival prediction of HCC and is expected to provide new insights into targeted therapy for HCC patients.

According to the Cancer Genome Atlas (TCGA), gastric cancers are classified into four molecular subtypes: Epstein–Barr virus-positive (EBV+), tumors with microsatellite instability (MSI), tumors with chromosomal instability (CIN), and genomically stable (GS) tumors. However, the gastric cancer (GC) with chromosomal instability remains insufficiently described and does not have effective markers for molecular and histological verification and diagnosis. The CIN subtype of GC is characterized by chromosomal instability, which is manifested by an increased frequency of aneuploidies and/or structural chromosomal rearrangements in tumor cells. Structural rearrangements in the CIN subtype of GC are not accidental and are commonly detected in chromosomal loci, being abnormal because of specific structural organization. The causes of CIN are still being discussed; however, according to recent data, aberrations in the TP53 gene may cause CIN development or worsen its phenotype. Clinically, patients with the CIN subtype of GC demonstrate poor survival, but receive the maximum benefit from adjuvant chemotherapy. In the review, we consider the molecular mechanisms and possible causes of chromosomal instability in GC, the common rearrangements of chromosomal loci and their impact on the development and clinical course of the disease, as well as the driver genes, their functions, and perspectives on their targeting in the CIN subtype of GC.

Abstract 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 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.

Lung cancer is the leading cause of cancer death all over the world. The majority (80-85 %) of lung cancer cases are classified as non-small cell lung cancer (NSCLC). Within NSCLC, adenocarcinoma (AC) and squamous cell carcinoma (SCC) are the most often recognized. The histological and immunohistochemical examination of NSCLC is a basic diagnostic tool, but insufficient for comprehensive therapeutic decisions. In some NSCLC patients, mainly adenocarcinoma, molecular alterations in driver genes, like EGFR, KRAS, HER2, ALK, MET, BRAF, RET, ROS1, and NTRK are recognized. The frequency of some of those changes is different depending on race, and between smokers and non-smokers. The molecular diagnostics of NSCLC using modern methods, like nextgeneration sequencing, is essential in estimating targeted, personalized therapy. In recent years, a breakthrough in understanding the importance of molecular studies for the precise treatment of NSCLC has been observed. Many new drugs were approved, including tyrosine kinase and immune checkpoint inhibitors. Clinical trials testing novel molecules like miRNAs and trials with CAR-T cells (chimeric antigen receptor – T cells) dedicated to NSCLC patients are ongoing. [ABSTRACT FROM AUTHOR]

Cancer is often driven by the accumulation of genetic variants, and effectively identifying the driver mutations in cancer is a great challenge. The current methods of identifying driver genes mainly include comparing observed mutation rates in regions of the genome with those predicted from background mutation rates (BMR) or conducting functional impact tests, and the genes are essentially statistically abnormal genes. Besides, driver genes between subclasses of well-defined cancers have not been studied. In this study, an association rule algorithm was introduced to explore the effective rules for the occurrence of this gene mutation that induces patients to suffer from this subtype of low-grade glioma, and the relationship between the mutation data and the results of cancer was established through the algorithm. Then, three metrics of support, confidence, and lift were used to screen and evaluate the obtained rules to predict candidate driver genes as well as between-class driver gene differences. Finally, using the somatic mutation data of 491 cases of low-grade gliomas, we obtained 22 driver genes associated with the results and their subclasses. The sensitivity and false-positive results were better than the existing single algorithm, and the 22 genes had important biological functions. At the same time, a subclass identification method of low-grade glioma based on the 22 genes was established. The overall model accuracy rate was 98.99%, and the method could effectively distinguish three subclasses. [ABSTRACT FROM AUTHOR]

Background Cancer is widely regarded as a complex disease primarily driven by genetic mutations. A critical concern and significant obstacle lies in discerning driver genes amid an extensive array of passenger genes. Findings We present a new method termed DriverMP for effectively prioritizing altered genes on a cancer-type level by considering mutated gene pairs. It is designed to first apply nonsilent somatic mutation data, protein‒protein interaction network data, and differential gene expression data to prioritize mutated gene pairs, and then individual mutated genes are prioritized based on prioritized mutated gene pairs. Application of this method in 10 cancer datasets from The Cancer Genome Atlas demonstrated its great improvements over all the compared state-of-the-art methods in identifying known driver genes. Then, a comprehensive analysis demonstrated the reliability of the novel driver genes that are strongly supported by clinical experiments, disease enrichment, or biological pathway analysis. Conclusions The new method, DriverMP, which is able to identify driver genes by effectively integrating the advantages of multiple kinds of cancer data, is available at https://github.com/LiuYangyangSDU/DriverMP. In addition, we have developed a novel driver gene database for 10 cancer types and an online service that can be freely accessed without registration for users. The DriverMP method, the database of novel drivers, and the user-friendly online server are expected to contribute to new diagnostic and therapeutic opportunities for cancers. [ABSTRACT FROM AUTHOR]

Melanoma is a heterogenous malignancy with an unpredictable clinical course. Most patients who present in the clinic are diagnosed with primary melanoma, yet large‐scale sequencing efforts have focused primarily on metastatic disease. In this study we sequence‐profiled 524 American Joint Committee on Cancer Stage I–III primary tumours. Our analysis of these data reveals recurrent driver mutations, mutually exclusive genetic interactions, where two genes were never or rarely co‐mutated, and an absence of co‐occurring genetic events. Further, we intersected copy number calls from our primary melanoma data with whole‐genome CRISPR screening data to identify the transcription factor interferon regulatory factor 4 (IRF4) as a melanoma‐associated dependency. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

The paper describes a method for predicting genes associated with the development of cancer. The method applies the convolutional neural network for the purpose of predicting disease driver genes. Distinctive features of the method are the use of gene expression data to determine the topological structure of the network, the efficiency of prediction with limited information about genes associated with the disease, and the possibility of jointly including information on mutations and similarity of gene expression profiles to improve the accuracy of prediction. [ABSTRACT FROM AUTHOR]

Simple Summary: We integrated three distinct methods (driver nodes, core module, and core nodes) to produce different HNSs for identifying hub genes involved in epithelial ovarian cancer (EOC). Immunohistochemical (IHC), qRT-PCR, and Western blotting were performed to validate the expression of hub genes and proteins. The results of the clinical experiment and the other data sets analyses confirmed the performance of the OHNS. Finally, the expression levels and diagnostic performance of OHNS showed statistical significance in evaluating external databases. This study also characterizes the critical genetic and transcriptomic features and their mutual regulatory relationships in EOC, providing valuable resources for identifying new molecular mechanisms and potential therapeutic targets for EOC. 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. [ABSTRACT FROM AUTHOR]

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]

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.

Hepatocellular carcinoma (HCC) is a widespread primary liver cancer with a high fatality rate. Despite several genes with oncogenic effects in HCC have been identified, many remain undiscovered. In this study, we conducted a comprehensive computational analysis to explore the involvement of genes within the same families as known driver genes in HCC. Specifically, we expanded the concept beyond single-gene mutations to encompass gene families sharing homologous structures, integrating various omics data to comprehensively understand gene abnormalities in cancer. Our analysis identified 74 domains with an enriched mutation burden, 404 domain mutation hotspots, and 233 dysregulated driver genes. We observed that specific low-frequency somatic mutations may contribute to HCC occurrence, potentially overlooked by single-gene algorithms. Furthermore, we systematically analyzed how abnormalities in the ubiquitinated proteasome system (UPS) impact HCC, finding that abnormal genes in E3, E2, DUB families, and Degron genes often result in HCC by affecting the stability of oncogenic or tumor suppressor proteins. In conclusion, expanding the exploration of driver genes to include gene families with homologous structures emerges as a promising strategy for uncovering additional oncogenic alterations in HCC. [Display omitted] • Utilized gene families for enhanced computational analysis. • Discovered rare oncogenic mutations in HCC via domain and hotspot analysis. • Integrated omics-data to analyze ubiquitin-proteasome system abnormalities in HCC. [ABSTRACT FROM AUTHOR]

Background: The identification of cancer driver genes and key molecular pathways has been the focus of large-scale cancer genome studies. Network-based methods detect significantly perturbed subnetworks as putative cancer pathways by incorporating genomics data with the topological information of PPI networks. However, commonly used PPI networks have distinct topological structures, making the results of the same method vary widely when applied to different networks. Furthermore, emerging context-specific PPI networks often have incomplete topological structures, which pose serious challenges for existing subnetwork detection algorithms. Methods: In this paper, we propose a novel method, referred to as MultiFDRnet, to address the above issues. The basic idea is to model a set of PPI networks as a multiplex network to preserve the topological structure of individual networks, while introducing dependencies among them, and, then, to detect significantly perturbed subnetworks on the modeled multiplex network using all the structural information simultaneously. Results: To illustrate the effectiveness of the proposed approach, an extensive benchmark analysis was conducted on both simulated and real cancer data. The experimental results showed that the proposed method is able to detect significantly perturbed subnetworks jointly supported by multiple PPI networks and to identify novel modular structures in context-specific PPI networks.

Lung adenocarcinoma is a malignancy with a low overall survival and a poor prognosis. Studies have shown that lung adenocarcinoma progression relates to locus-specific/global changes in histone modifications. To explore the relationship between histone modification and gene expression changes, we focused on 11 histone modifications and quantitatively analyzed their influences on gene expression. We found that, among the studied histone modifications, H3K79me2 displayed the greatest impact on gene expression regulation. Based on the Shannon entropy, 867 genes with differential H3K79me2 levels during tumorigenesis were identified. Enrichment analyses showed that these genes were involved in 16 common cancer pathways and 11 tumors and were target-regulated by trans-regulatory elements, such as Tp53 and WT1. Then, an open-source computational framework was presented (https://github.com/zlq-imu/Identification-of-potential-LUND-driver-genes). Twelve potential driver genes were extracted from the genes with differential H3K79me2 levels during tumorigenesis. The expression levels of these potential driver genes were significantly increased/decreased in tumor cells, as assayed by RT–qPCR. A risk score model comprising these driver genes was further constructed, and this model was strongly negatively associated with the overall survival of patients in different datasets. The proportional hazards assumption and outlier test indicated that this model could robustly distinguish patients with different survival rates. Immune analyses and responses to immunotherapeutic and chemotherapeutic agents showed that patients in the high and low-risk groups may have distinct tendencies for clinical selection. Finally, the regions with clear H3K79me2 signal changes on these driver genes were accurately identified. Our research may offer potential molecular biomarkers for lung adenocarcinoma treatment.

Abstract Background Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development. Results Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation. Conclusions Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/ .

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.