Interpreting pathways to discover cancer driver genes with Moonlight

Knockdown of HGH1 in breast cancer cell lines can inhibit the viability, invasion and migration of tumor cells

BACKGROUND

Research on the function of HGH1 in breast cancer remains lacking.

METHODS

TCGAand GEO (GSE45827) datasets investigated discrepancies in HGH1 expression in BC. An aggregate of 106 clinical samples were gathered through immunohistochemistry, KM curves were drawn for prognostic analysis, and the function of HGH1 of BC was predicted. Finally, the effects of HGH1 knockdown on MDA-MB-231 and MCF-7 BC cells were verified via CCK8, invasion, wound healing and colony formation assays.

RESULTS

HGH1 is highly expressed in BC and is linked to unfavorable prognosis. HGH1 overexpression is connected to keratinization and the cell cycle and is closely related to ER and PR expression and tumor stage in BC patients. Knocking down HGH1 in BC cells inhibited the viability, invasion and migration.

CONCLUSION

Knockdown of HGH1 in breast cancer cell lines can inhibit the viability, invasion and migration of tumor cells.

Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight

Cancer involves dynamic changes caused by (epi)genetic alterations such as mutations or abnormal DNA methylation patterns which occur in cancer driver genes. These driver genes are divided into oncogenes and tumor suppressors depending on their function and mechanism of action. Discovering driver genes in different cancer (sub)types is important not only for increasing current understanding of carcinogenesis but also from prognostic and therapeutic perspectives. We have previously developed a framework called Moonlight which uses a systems biology multi-omics approach for prediction of driver genes. Here, we present an important development in Moonlight2 by incorporating a DNA methylation layer which provides epigenetic evidence for deregulated expression profiles of driver genes. To this end, we present a novel functionality called Gene Methylation Analysis (GMA) which investigates abnormal DNA methylation patterns to predict driver genes. This is achieved by integrating the tool EpiMix which is designed to detect such aberrant DNA methylation patterns in a cohort of patients and further couples these patterns with gene expression changes. To showcase GMA, we applied it to three cancer (sub)types (basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma) where we discovered 33, 190, and 263 epigenetically driven genes, respectively. A subset of these driver genes had prognostic effects with expression levels significantly affecting survival of the patients. Moreover, a subset of the driver genes demonstrated therapeutic potential as drug targets. This study provides a framework for exploring the driving forces behind cancer and provides novel insights into the landscape of three cancer sub(types) by integrating gene expression and methylation data.

Association between dietary fructose and human colon DNA methylation: implication for racial disparities in colorectal cancer risk using a cross-sectional study

BACKGROUND

An increasing body of evidence has linked fructose intake to colorectal cancer (CRC). African-American (AA) adults consume greater quantities of fructose and are more likely to develop right-side colon cancer than European American (EA) adults.

OBJECTIVES

We examined the hypothesis that fructose consumption leads to epigenomic and transcriptomic differences associated with CRC tumor biology.

METHODS

Deoxyribonucleic acid methylation data from this cross-sectional study was obtained using the Illumina Infinium MethylationEPIC kit (GSE151732). Right and left colon differentially methylated regions (DMRs) were identified using DMRcate through analysis of Food Frequency Questionnaire data on fructose consumption in normal colon biopsies (n = 79) of AA adults undergoing screening colonoscopy. Secondary analysis of CRC tumors was carried out using data derived from The Cancer Genome Atlas Colon Adenocarcinoma, GSE101764, and GSE193535. Right colon organoids derived from AA (n = 5) and EA (n = 5) adults were exposed to 4.4 mM of fructose for 72 h. Differentially expressed genes (DEGs) were identified using DESeq2.

RESULTS

We identified 4263 right colon fructose-associated DMRs [false-discovery rates (FDR) < 0.05]. In contrast, only 24 DMRs survived multiple testing corrections (FDR < 0.05) in matched, left colon. Almost 50% of right colon fructose-associated DMRs overlapped regions implicated in CRC in ≥1 of 3 data sets. Highly significant enrichment was also observed between genes corresponding to right colon fructose-associated DMRs and DEGs associated with fructose exposure in right colon organoids of AA individuals (P = 3.28E-30). Overlapping and significant enrichments for fatty acid metabolism, glycolysis, and cell proliferation pathways were also found. Cross-referencing genes within these pathways to DEGs in CRC tumors reveal potential roles for ankyrin repeat domain containing protein 23 and phosphofructokinase, platelet in fructose-mediated CRC risk for AA individuals.

CONCLUSIONS

Our data support that dietary fructose exerts a greater CRC risk-related effect in the right than left colon among AA adults, alluding to its potential role in contributing to racial disparities in CRC.

Comprehensive Analysis of Multi-Omics Data on RNA Polymerase as an Adverse Factor in Head and Neck Squamous Cell Carcinoma

Background

High transcription levels are essential for cancer cells to maintain their malignant phenotype. While RNA polymerases (POLRs) have been implicated in various transcriptional mechanisms, their impact on the tumor microenvironment (TME) remains poorly understood.

Methods

We analyzed publicly available pan-cancer cohorts to evaluate the expression and genomic alterations of POLRs. Focusing on head and neck squamous cell carcinoma (HNSC), we integrated bulk RNA sequencing, single-cell, and spatial transcriptome data to identify POLR2C expression patterns and its potential regulation by Yin Yang 1 (YY1). In vitro and in vivo experiments were conducted to validate the functional role of the YY1-POLR2C axis in cancer proliferation and immune modulation.

Results

POLRs were found to be aberrantly expressed in cancers and associated with genomic alterations. In HNSC, POLR up-regulation was linked to poor prognostic features. POLR2C was significantly up-regulated in malignant cells, and its expression appeared to be transcriptionally regulated by YY1. Functional studies demonstrated that the YY1-POLR2C axis drives cell-cycle dysregulation and malignant proliferation in HNSC. Additionally, high POLR expression negatively correlated with immune cell infiltration and facilitated immune evasion. Mechanistically, POLRs mediated frequent interactions between malignant and immune cells, potentially contributing to resistance to immunotherapy.

Conclusion

This study highlights the dual role of POLRs in promoting malignant proliferation and shaping an immunosuppressive TME. POLR2C, regulated by YY1, emerges as a critical mediator in HNSC and a promising target for precision therapies.

MONet: cancer driver gene identification algorithm based on integrated analysis of multi-omics data and network models

Cancer progression is orchestrated by the accrual of mutations in driver genes, which endow malignant cells with a selective proliferative advantage. Identifying cancer driver genes is crucial for elucidating the molecular mechanisms of cancer, advancing targeted therapies, and uncovering novel biomarkers. Based on integrated analysis of Multi-Omics data and Network models, we present MONet, a novel cancer driver gene identification algorithm. Our method utilizes two graph neural network algorithms on protein-protein interaction (PPI) networks to extract feature vector representations for each gene. These feature vectors are subsequently concatenated and fed into a multi-layer perceptron model (MLP) to perform semi-supervised identification of cancer driver genes. For each mutated gene, MONet assigns the probability of being potential driver, with genes identified in at least two PPI networks selected as candidate driver genes. When applied to pan-cancer datasets, MONet demonstrated robustness across various PPI networks, outperforming baseline models in terms of both the area under the receiver operating characteristic curve and the area under the precision-recall curve. Notably, MONet identified 37 novel driver genes that were missed by other methods, including 29 genes such as APOBEC2, GDNF, and PRELP, which are corroborated by existing literature, underscoring their critical roles in cancer development and progression. Through the MONet framework, we successfully identified known and novel candidate cancer driver genes, providing biologically meaningful insights into cancer mechanisms.

Enhancing chemotherapy response prediction via matched colorectal tumor-organoid gene expression analysis and network-based biomarker selection

BACKGROUND

Colorectal cancer (CRC) presents significant challenges in chemotherapy response prediction due to its molecular heterogeneity. Current methods often fail to account for the complexity and variability inherent in individual tumors.

METHODS

We developed a novel approach using matched CRC tumor and organoid gene expression data. We applied Consensus Weighted Gene Co-expression Network Analysis (WGCNA) across three datasets: CRC tumors, matched organoids, and an independent organoid dataset with IC50 drug response values, to identify key gene modules and hub genes linked to chemotherapy response, particularly 5-fluorouracil (5-FU).

FINDINGS

Our integrative analysis identified significant gene modules and hub genes associated with CRC chemotherapy response. The predictive model built from these findings demonstrated superior accuracy over traditional methods when tested on independent datasets. The matched tumor-organoid data approach proved effective in capturing relevant biomarkers, enhancing prediction reliability.

INTERPRETATION

This study provides a robust framework for improving CRC chemotherapy response predictions by leveraging matched tumor and organoid gene expression data. Our approach addresses the limitations of previous methods, offering a promising strategy for personalized treatment planning in CRC. Future research should aim to validate these findings and explore the integration of more comprehensive drug response data.

FUNDING

This research was supported by US National Cancer Institute grant R37CA248289, and Sylvester Comprehensive Cancer Center. which receives funding from the National Cancer Institute award P30CA240139. This work was supported by National Institutes of Health (NIH) under the following grants: T32CA009501-31A1 and R37CA248289. This work was also supported by the MSK P30CA008748 grant.

Unleashing the future: The revolutionary role of machine learning and artificial intelligence in drug discovery

Drug discovery is a complex and multifaceted process aimed at identifying new therapeutic compounds with the potential to treat various diseases. Traditional methods of drug discovery are often time-consuming, expensive, and characterized by low success rates. Because of this, there is an urgent need to improve the drug development process using new technologies. The integration of the current state-of-art of artificial intelligence (AI) and machine learning (ML) approaches with conventional methods will enhance the efficiency and effectiveness of pharmaceutical research. This review highlights the transformative impact of AI and ML in drug discovery, discussing current applications, challenges, and future directions in harnessing these technologies to accelerate the development of innovative therapeutics. We have discussed the latest developments in AI and ML technologies to streamline several stages of drug discovery, from target identification and validation to lead optimization and preclinical studies.

Advancing cancer driver gene identification through an integrative network and pathway approach

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.

Extrachromosomal circular DNA orchestrates genome heterogeneity in urothelial bladder carcinoma

Rationale: Extrachromosomal circular DNA is a hallmark of cancer, but its role in shaping the genome heterogeneity of urothelial bladder carcinoma (UBC) remains poorly understood. Here, we comprehensively analyzed the features of extrachromosomal circular DNA in 80 UBC patients. Methods: We performed whole-genome/exome sequencing (WGS/WES), Circle-Seq, single-molecule real-time (SMRT) long-read sequencing of circular DNA, and RNA sequencing (RNA-Seq) on 80 pairs of tumor and AT samples. We used our newly developed circular DNA analysis software, Circle-Map++ to detect small extrachromosomal circular DNA from Circle-Seq data. Results: We observed a high load and significant heterogeneity of extrachromosomal circular DNAs in UBC, including numerous single-locus and complex chimeric circular DNAs originating from different chromosomes. This includes highly chimeric circular DNAs carrying seven oncogenes and circles from nine chromosomes. We also found that large tumor-specific extrachromosomal circular DNAs could influence genome-wide gene expression, and are detectable in time-matched urinary sediments. Additionally, we found that the extrachromosomal circular DNA correlates with hypermutation, copy number variation, oncogene amplification, and clinical outcome. Conclusions: Overall, our study provides a comprehensive extrachromosomal circular DNA map of UBC, along with valuable data resources and bioinformatics tools for future cancer and extrachromosomal circular DNA research.

Predicting regulatory mutations and their target genes by new computational integrative analysis: A study of follicular lymphoma

Mutations in DNA regulatory regions are increasingly being recognized as important drivers of cancer and other complex diseases. These mutations can regulate gene expression by affecting DNA-protein binding and epigenetic profiles, such as DNA methylation in genome regulatory elements. However, identifying mutation hotspots associated with expression regulation and disease progression in non-coding DNA remains a challenge. Unlike most existing approaches that assign a mutation score to individual single nucleotide polymorphisms (SNP), a mutation block (MB)-based approach was introduced in this study to assess the collective impact of a cluster of SNPs on transcription factor-DNA binding affinity, differential gene expression (DEG), and nearby DNA methylation. Moreover, the long-distance target genes of functional MBs were identified using a new permutation-based algorithm that assessed the significance of correlations between DNA methylation at regulatory regions and target gene expression. Two new Python packages were developed. The Differential Methylation Region (DMR-analysis) analysis tool was used to detect DMR and map them to regulatory elements. The second tool, an integrated DMR, DEG, and SNP analysis tool (DDS-analysis), was used to combine the omics data to identify functional MBs and long-distance target genes. Both tools were validated in follicular lymphoma (FL) cohorts, where not only known functional MBs and their target genes (BCL2 and BCL6) were recovered, but also novel genes were found, including CDCA4 and JAG2, which may be associated with FL development. These genes are linked to target gene expression and are significantly correlated with the methylation of nearby DNA sequences in FL. The proposed computational integrative analysis of multiomics data holds promise for identifying regulatory mutations in cancer and other complex diseases.

Copy number losses of oncogenes and gains of tumor suppressor genes generate common driver mutations

Cancer driver genes can undergo positive selection for various types of genetic alterations, including gain-of-function or loss-of-function mutations and copy number alterations (CNA). We investigated the landscape of different types of alterations affecting driver genes in 17,644 cancer exomes and genomes. We find that oncogenes may simultaneously exhibit signatures of positive selection and also negative selection in different gene segments, suggesting a method to identify additional tumor types where an oncogene is a driver or a vulnerability. Next, we characterize the landscape of CNA-dependent selection effects, revealing a general trend of increased positive selection on oncogene mutations not only upon CNA gains but also upon CNA deletions. Similarly, we observe a positive interaction between mutations and CNA gains in tumor suppressor genes. Thus, two-hit events involving point mutations and CNA are universally observed regardless of the type of CNA and may signal new therapeutic opportunities. An analysis with focus on the somatic CNA two-hit events can help identify additional driver genes relevant to a tumor type. By a global inference of point mutation and CNA selection signatures and interactions thereof across genes and tissues, we identify 9 evolutionary archetypes of driver genes, representing different mechanisms of (in)activation by genetic alterations.

Enhancing Chemotherapy Response Prediction via Matched Colorectal Tumor-Organoid Gene Expression Analysis and Network-Based Biomarker Selection

Colorectal cancer (CRC) poses significant challenges in chemotherapy response prediction due to its molecular heterogeneity. This study introduces an innovative methodology that leverages gene expression data generated from matched colorectal tumor and organoid samples to enhance prediction accuracy. By applying Consensus Weighted Gene Co-expression Network Analysis (WGCNA) across multiple datasets, we identify critical gene modules and hub genes that correlate with patient responses, particularly to 5-fluorouracil (5-FU). This integrative approach advances precision medicine by refining chemotherapy regimen selection based on individual tumor profiles. Our predictive model demonstrates superior accuracy over traditional methods on independent datasets, illustrating significant potential in addressing the complexities of high-dimensional genomic data for cancer biomarker research.

Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction

Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at: https://github.com/mxqmxqmxq/SCIS-CDG.

GLIMS: A two-stage gradual-learning method for cancer genes prediction using multi-omics data and co-splicing network

Identifying cancer genes is vital for cancer diagnosis and treatment. However, because of the complexity of cancer occurrence and limited cancer genes knowledge, it is hard to identify cancer genes accurately using only a few omics data, and the overall performance of existing methods is being called for further improvement. Here, we introduce a two-stage gradual-learning strategy GLIMS to predict cancer genes using integrative features from multi-omics data. Firstly, it uses a semi-supervised hierarchical graph neural network to predict the initial candidate cancer genes by integrating multi-omics data and protein-protein interaction (PPI) network. Then, it uses an unsupervised approach to further optimize the initial prediction by integrating the co-splicing network in post-transcriptional regulation, which plays an important role in cancer development. Systematic experiments on multi-omics cancer data demonstrated that GLIMS outperforms the state-of-the-art methods for the identification of cancer genes and it could be a useful tool to help advance cancer analysis.

Precision cancer classification using liquid biopsy and advanced machine learning techniques

Cancer presents a significant global health burden, resulting in millions of annual deaths. Timely detection is critical for improving survival rates, offering a crucial window for timely medical interventions. Liquid biopsy, analyzing genetic variations, and mutations in circulating cell-free, circulating tumor DNA (cfDNA/ctDNA) or molecular biomarkers, has emerged as a tool for early detection. This study focuses on cancer detection using mutations in plasma cfDNA/ctDNA and protein biomarker concentrations. The proposed system initially calculates the correlation coefficient to identify correlated features, while mutual information assesses each feature's relevance to the target variable, eliminating redundant features to improve efficiency. The eXtrem Gradient Boosting (XGBoost) feature importance method iteratively selects the top ten features, resulting in a 60% dataset dimensionality reduction. The Light Gradient Boosting Machine (LGBM) model is employed for classification, optimizing its performance through a random search for hyper-parameters. Final predictions are obtained by ensembling LGBM models from tenfold cross-validation, weighted by their respective balanced accuracy, and averaged to get final predictions. Applying this methodology, the proposed system achieves 99.45% accuracy and 99.95% AUC for detecting the presence of cancer while achieving 93.94% accuracy and 97.81% AUC for cancer-type classification. Our methodology leads to enhanced healthcare outcomes for cancer patients.

Nuclear Phosphoproteome Reveals Prolyl Isomerase PIN1 as a Modulator of Oncogene-Induced Senescence

Mammalian cells possess intrinsic mechanisms to prevent tumorigenesis upon deleterious mutations, including oncogene-induced senescence (OIS). The molecular mechanisms underlying OIS are, however, complex and remain to be fully characterized. In this study, we analyzed the changes in the nuclear proteome and phosphoproteome of human lung fibroblast IMR90 cells during the progression of OIS induced by oncogenic RASG12V activation. We found that most of the differentially regulated phosphosites during OIS contained prolyl isomerase PIN1 target motifs, suggesting PIN1 is a key regulator of several promyelocytic leukemia nuclear body proteins, specifically regulating several proteins upon oncogenic Ras activation. We showed that PIN1 knockdown promotes cell proliferation, while diminishing the senescence phenotype and hallmarks of senescence, including p21, p16, and p53 with concomitant accumulation of the protein PML and the dysregulation of promyelocytic leukemia nuclear body formation. Collectively, our data demonstrate that PIN1 plays an important role as a tumor suppressor in response to oncogenic ER:RasG12V activation.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks

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.

Advances in computational methods for identifying cancer driver genes

Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.

Signaling-induced systematic repression of miRNAs uncovers cancer vulnerabilities and targeted therapy sensitivity

Targeted therapies are effective in treating cancer, but success depends on identifying cancer vulnerabilities. In our study, we utilize small RNA sequencing to examine the impact of pathway activation on microRNA (miRNA) expression patterns. Interestingly, we discover that miRNAs capable of inhibiting key members of activated pathways are frequently diminished. Building on this observation, we develop an approach that integrates a low-miRNA-expression signature to identify druggable target genes in cancer. We train and validate our approach in colorectal cancer cells and extend it to diverse cancer models using patient-derived in vitro and in vivo systems. Finally, we demonstrate its additional value to support genomic and transcriptomic-based drug prediction strategies in a pan-cancer patient cohort from the National Center for Tumor Diseases (NCT)/German Cancer Consortium (DKTK) Molecularly Aided Stratification for Tumor Eradication (MASTER) precision oncology trial. In conclusion, our strategy can predict cancer vulnerabilities with high sensitivity and accuracy and might be suitable for future therapy recommendations in a variety of cancer subtypes.

A workflow to study mechanistic indicators for driver gene prediction with Moonlight

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.

Proteogenomic data and resources for pan-cancer analysis

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

PyInteraph2 and PyInKnife2 to Analyze Networks in Protein Structural Ensembles

Due to the complex nature of noncovalent interactions and their long-range effects, analyzing protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) provide a convenient formalism to study protein structures in relation to essential properties such as key residues for structural stability, allosteric communication, and the effects of modifications of the protein. PSNs can be defined according to very different principles, and the available tools have limitations in input formats, supported models, and version control. Other outstanding problems are related to the definition of network cutoffs and the assessment of the stability of the network properties. The protein science community could benefit from a common framework to carry out these analyses and make them easier to reproduce, reuse, and evaluate. We here provide two open-source software packages, PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a reproducible and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and incorporates different network models with the possibility of integrating them into a macronetwork and performing various downstream analyses, including hubs, connected components, and several other centrality measures, and visualizes the networks or further analyzes them thanks to compatibility with Cytoscape.PyInKnife2 that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. We foresee that the modular structure of the code and the supported version control system will promote the transition to a community-driven effort, boost reproducibility, and establish common protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities and maintenance, assistance, and training of new contributors.

LSM2 is associated with a poor prognosis and promotes cell proliferation, migration, and invasion in skin cutaneous melanoma

BACKGROUND

Skin cutaneous melanoma (SKCM) is an extremely malignant tumor that is associated with a poor prognosis. LSM2 has been found to be related to different types of tumors; however, its role in SKCM is poorly defined. We aimed to determine the value of LSM2 as a prognostic biomarker for SKCM.

METHODS

The expression profile of LSM2 mRNA was compared between tumor and normal tissues in public databases, such as TCGA, GEO, and BioGPS. LSM2 protein expression was explored using immunohistochemistry (IHC) on a tissue microarray containing 44 SKCM tissues and 8 normal samples collected at our center. Kaplan-Meier analysis was performed to assess the prognostic value of LSM2 expression in patients with SKCM. SKCM cell lines with LSM2 knockdown were used to determine the effects of LSM2. Cell counting kit-8 (CCK8) and colony formation assays were conducted to assess cell proliferation, whereas wound healing and transwell assays were carried out to assess the migration and invasion abilities of SKCM cells.

RESULTS

LSM2 was more highly expressed at the mRNA and protein levels in SKCM than that in normal skin. Moreover, elevated expression of LSM2 was associated with shorter survival time and early recurrence in patients with SKCM. The in vitro results revealed that the silencing of LSM2 in SKCM cells significantly inhibited cell proliferation, migration, and invasion.

CONCLUSION

Overall, LSM2 contributes to malignant status and poor prognosis in patients with SKCM and may be identified as a novel prognostic biomarker and therapeutic target.

Dual role of ANGPTL4 in inflammation

BACKGROUND

Angiopoietin-like 4 (ANGPTL4) belongs to the angiopoietin-like protein family and mediates the inhibition of lipoprotein lipase activity. Emerging evidence suggests that ANGPTL4 has pleiotropic functions with anti- and pro-inflammatory properties.

METHODS

A thorough search on PubMed related to ANGPTL4 and inflammation was performed.

RESULTS

Genetic inactivation of ANGPTL4 can significantly reduce the risk of developing coronary artery disease and diabetes. However, antibodies against ANGPTL4 result in several undesirable effects in mice or monkeys, such as lymphadenopathy and ascites. Based on the research progress on ANGPTL4, we systematically discussed the dual role of ANGPTL4 in inflammation and inflammatory diseases (lung injury, pancreatitis, heart diseases, gastrointestinal diseases, skin diseases, metabolism, periodontitis, and osteolytic diseases). This may be attributed to several factors, including post-translational modification, cleavage and oligomerization, and subcellular localization.

CONCLUSION

Understanding the potential underlying mechanisms of ANGPTL4 in inflammation in different tissues and diseases will aid in drug discovery and treatment development.

A novel tumor 4-driver gene signature for the prognosis of hepatocellular carcinoma

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.

Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools

Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.

An integrated tumor, immune and microbiome atlas of colon cancer

The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further microbiome characterization. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications. Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by Ruminococcus bromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches.

COMMD3 loss drives invasive breast cancer growth by modulating copper homeostasis

BACKGROUND

Despite overall improvement in breast cancer patient outcomes from earlier diagnosis and personalised treatment approaches, some patients continue to experience recurrence and incurable metastases. It is therefore imperative to understand the molecular changes that allow transition from a non-aggressive state to a more aggressive phenotype. This transition is governed by a number of factors.

METHODS

As crosstalk with extracellular matrix (ECM) is critical for tumour cell growth and survival, we applied high throughput shRNA screening on a validated '3D on-top cellular assay' to identify novel growth suppressive mechanisms.

RESULTS

A number of novel candidate genes were identified. We focused on COMMD3, a previously poorly characterised gene that suppressed invasive growth of ER + breast cancer cells in the cellular assay. Analysis of published expression data suggested that COMMD3 is normally expressed in the mammary ducts and lobules, that expression is lost in some tumours and that loss is associated with lower survival probability. We performed immunohistochemical analysis of an independent tumour cohort to investigate relationships between COMMD3 protein expression, phenotypic markers and disease-specific survival. This revealed an association between COMMD3 loss and shorter survival in hormone-dependent breast cancers and in particularly luminal-A-like tumours (ER+/Ki67-low; 10-year survival probability 0.83 vs. 0.73 for COMMD3-positive and -negative cases, respectively). Expression of COMMD3 in luminal-A-like tumours was directly associated with markers of luminal differentiation: c-KIT, ELF5, androgen receptor and tubule formation (the extent of normal glandular architecture; p < 0.05). Consistent with this, depletion of COMMD3 induced invasive spheroid growth in ER + breast cancer cell lines in vitro, while Commd3 depletion in the relatively indolent 4T07 TNBC mouse cell line promoted tumour expansion in syngeneic Balb/c hosts. Notably, RNA sequencing revealed a role for COMMD3 in copper signalling, via regulation of the Na+/K+-ATPase subunit, ATP1B1. Treatment of COMMD3-depleted cells with the copper chelator, tetrathiomolybdate, significantly reduced invasive spheroid growth via induction of apoptosis.

CONCLUSION

Overall, we found that COMMD3 loss promoted aggressive behaviour in breast cancer cells.

Plakophilin 3 facilitates G1/S phase transition and enhances proliferation by capturing RB protein in the cytoplasm and promoting EGFR signaling

Plakophilin 3 (PKP3) is a component of desmosomes and is frequently overexpressed in cancer. Using keratinocytes either lacking or overexpressing PKP3, we identify a signaling axis from ERK to the retinoblastoma (RB) protein and the E2F1 transcription factor that is controlled by PKP3. RB and E2F1 are key components controlling G1/S transition in the cell cycle. We show that PKP3 stimulates the activity of ERK and its target RSK1. This inhibits expression of the transcription factor RUNX3, a positive regulator of the CDK inhibitor CDKN1A/p21, which is also downregulated by PKP3. Elevated CDKN1A prevents RB phosphorylation and E2F1 target gene expression, leading to delayed S phase entry and reduced proliferation in PKP3-depleted cells. Elevated PKP3 expression not only increases ERK activity but also captures phosphorylated RB (phospho-RB) in the cytoplasm to promote E2F1 activity and cell-cycle progression. These data identify a mechanism by which PKP3 promotes proliferation and acts as an oncogene.

DNMT3b affects colorectal cancer development by regulating FLI1 through DNA hypermethylation

Friend leukemia integration 1 (FLI1) is an ETS transcription factor family member. Here, we identified cg11017065 as the most hyper-methylated cytosine and guanine (CpG) in colorectal cancer (CRC), which belongs to the FLI1 gene. Moreover, integrated bioinformatics prediction and analysis of our cohort showed that FLI1 expression was downregulated and DNA methylation was elevated in CRC. Bioinformatics prediction also indicated that patients overexpressing FLI1 had higher survival rates than those with low FLI1 expression. CRC cells with ectopic expression of FLI1 had reduced invasion, migration, cloning ability and increased apoptosis. Furthermore, DNA-methyltransferase 3b (DNMT3b) was found to be significantly overexpressed in CRC, and low DNMT3b expression predicted a prolonged survival. DNMT3b bound to the FLI1 promoter. Inhibition of DNMT3b increased FLI1 expression and inhibited the malignant phenotype of CRC cells. Inhibition of FLI1 reversed the phenotypic modulation by DNMT3b depletion in vitro and in vivo. In conclusion, our data indicate that DNMT3b potentiates CRC cell proliferation, migration, and invasion through downregulating FLI1.

Experimental analysis of bladder cancer-associated mutations in EP300 identifies EP300-R1627W as a driver mutation

BACKGROUND

Bladder cancer (BCa) is the most common malignant tumor of the urinary system, with transitional cell carcinoma (TCC) being the predominant type. EP300 encodes a lysine acetyltransferase that regulates a large subset of genes by acetylating histones and non-histone proteins. We previously identified several bladder cancer-associated mutations in EP300 using high-throughput sequencing; however, the functional consequences of these mutations remain unclear.

METHODS

Bladder cancer cells T24 and TCC-SUP were infected with shEP300 lentiviruses to generate stable EP300 knockdown cell lines. The expression levels of EP300, p16 and p21 were detected by real-time PCR and western blots. The transcriptional activity of p16 and p21 were detected by dual luciferase assay. Cell proliferation assay, flow cytometric analyses of cell cycle, invasion assay and xenograft tumor model were used to measure the effect of EP300-R1627W mutation in bladder cancer. Immunoprecipitation was used to explore the relationship between EP300-R1627W mutation and p53. Structural analysis was used to detect the structure of EP300-R1627W protein compared to EP300-wt protein.

RESULTS

we screened the mutations of EP300 and found that the EP300-R1627W mutation significantly impairs EP300 transactivation activity. Notably, we demonstrated that the R1627W mutation impairs EP300 acetyltransferase activity, potentially by interfering with substrate binding. Finally, we show that EP300-R1627W is more aggressive in growth and invasion in vitro and in vivo compared to cells expressing EP300-wt. We also found that the EP300-R1627W mutation occurs frequently in seven different types of cancers.

CONCLUSION

In summary, our work defines a driver role of EP300-R1627W in bladder cancer development and progression.

Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness

Clear cell renal cell carcinomas (ccRCCs) represent ∼75% of RCC cases and account for most RCC-associated deaths. Inter- and intratumoral heterogeneity (ITH) results in varying prognosis and treatment outcomes. To obtain the most comprehensive profile of ccRCC, we perform integrative histopathologic, proteogenomic, and metabolomic analyses on 305 ccRCC tumor segments and 166 paired adjacent normal tissues from 213 cases. Combining histologic and molecular profiles reveals ITH in 90% of ccRCCs, with 50% demonstrating immune signature heterogeneity. High tumor grade, along with BAP1 mutation, genome instability, increased hypermethylation, and a specific protein glycosylation signature define a high-risk disease subset, where UCHL1 expression displays prognostic value. Single-nuclei RNA sequencing of the adverse sarcomatoid and rhabdoid phenotypes uncover gene signatures and potential insights into tumor evolution. In vitro cell line studies confirm the potential of inhibiting identified phosphoproteome targets. This study molecularly stratifies aggressive histopathologic subtypes that may inform more effective treatment strategies.

Cancer fitness genes: emerging therapeutic targets for metastasis

Development of cancer therapeutics has traditionally focused on targeting driver oncogenes. Such an approach is limited by toxicity to normal tissues and treatment resistance. A class of 'cancer fitness genes' with crucial roles in metastasis have been identified. Elevated or altered activities of these genes do not directly cause cancer; instead, they relieve the stresses that tumor cells encounter and help them adapt to a changing microenvironment, thus facilitating tumor progression and metastasis. Importantly, as normal cells do not experience high levels of stress under physiological conditions, targeting cancer fitness genes is less likely to cause toxicity to noncancerous tissues. Here, we summarize the key features and function of cancer fitness genes and discuss their therapeutic potential.

Revealing the functions of clonal driver gene mutations in patients based on evolutionary dependencies

The clonal mutations in driver genes enable cells to gradually acquire growth advantage in tumor development. Therefore, revealing the functions of clonal driver gene mutations is important. Here, we proposed the method FCMP that considered evolutionary dependencies to analyze the functions of clonal driver gene mutations in a single patient. Applying our method to five cancer types from The Cancer Genome Atlas, we identified specific functions and common functions of clonal driver gene mutations. We found that the clonal driver gene mutations in the same patient played multiple functions. We also found that clonal mutations in the same driver gene performed different functions in different patients. These findings suggested that the clonal driver gene mutations showed strong tumor heterogeneity. In the pan-cancer analysis, the immune-related functions for clonal driver gene mutations were shared by multiple cancer types. In addition, clonal mutations in some driver genes predicted the survival of patients in cancers.

Chemokines network in bone metastasis: Vital regulators of seeding and soiling

Chemokines are well equipped with chemo-attractive signals that can regulate cancer cell trafficking to specific organ sites. Currently, updated concepts have revealed the diverse role of chemokines in the biology of cancer initiation and progression. Genomic instabilities and alterations drive tumor heterogeneity, providing more options for the selection and metastatic progression to cancer cells. Tumor heterogeneity and acquired drug resistance are the main obstacles in managing cancer therapy and the primary root cause of metastasis. Studies emphasize that multiple chemokine/receptor axis are involved in cancer cell-mediated organ-specific distant metastasis. One of the persuasive mechanisms for heterogeneity and subsequent events is sturdily interlinked with the crosstalk between chemokines and their receptors on cancer cells and tissue-specific microenvironment. Among different metastatic niches, skeletal metastasis is frequently observed in the late stages of prostate, breast, and lung cancer and significantly reduces the survival of cancer patients. Therefore, it is crucial to elucidate the role of chemokines and their receptors in metastasis and bone remodeling. Here, we review the potential chemokine/receptor axis in tumorigenesis, tumor heterogeneity, metastasis, and vicious cycle in bone microenvironment.

Leader gene identification for digestive system cancers based on human subcellular location and cancer-related characteristics in protein-protein interaction networks

Stomach, liver, and colon cancers are the most common digestive system cancers leading to mortality. Cancer leader genes were identified in the current study as the genes that contribute to tumor initiation and could shed light on the molecular mechanisms in tumorigenesis. An integrated procedure was proposed to identify cancer leader genes based on subcellular location information and cancer-related characteristics considering the effects of nodes on their neighbors in human protein-protein interaction networks. A total of 69, 43, and 64 leader genes were identified for stomach, liver, and colon cancers, respectively. Furthermore, literature reviews and experimental data including protein expression levels and independent datasets from other databases all verified their association with corresponding cancer types. These final leader genes were expected to be used as diagnostic biomarkers and targets for new treatment strategies. The procedure for identifying cancer leader genes could be expanded to open up a window into the mechanisms, early diagnosis, and treatment of other cancer types.

Pan-cancer gene expression analysis: Identification of deregulated autophagy genes and drugs to target them

Identifying suitable deregulated targets in autophagy pathway is essential for developing autophagy modulating cancer therapies. With this aim, we systematically analyzed the expression levels of genes that contribute to the execution of autophagy in 21 cancers. Deregulated genes for 21 cancers were analyzed using the level 3 mRNA data from TCGAbiolinks. A total of 574 autophagy genes were mapped to the deregulated genes across 21 cancers. PPI network, cluster analysis, gene enrichment, gene ontology, KEGG pathway, patient survival, protein expression and cMap analysis were performed. Among the autophagy genes, 260 were upregulated, and 43 were downregulated across pan-cancer. The upregulated autophagy genes - CDKN2A and BIRC5 - were the most frequent signatures in cancers and could be universal cancer biomarkers. Significant involvement of autophagy process was found in 8 cancers (CHOL, HNSC, GBM, KICH, KIRC, KIRP, LIHC and SARC). Fifteen autophagy hub genes (ATP6V0C, BIRC5, HDAC1, IL4, ITGB1, ITGB4, MAPK3, mTOR, cMYC, PTK2, SRC, TCIRG1, TP63, TP73 and ULK1) were found to be linked with patients survival and also expressed in cancer patients tissue samples, making them as potential drug targets for these cancers. The deregulated autophagy genes were further used to identify drugs Losartan, BMS-345541, Embelin, Abexinostat, Panobinostat, Vorinostat, PD-184352, PP-1, XMD-1150, Triplotide, Doxorubicin and Ouabain, which could target one or more autophagy hub genes. Overall, our findings shed light on the most frequent cancer-associated autophagy genes, potential autophagy targets and molecules for cancer treatment. These findings can accelerate autophagy modulation in cancer therapy.

In silico development and experimental validation of a novel 7-gene signature based on PI3K pathway-related genes in bladder cancer

Although bladder cancer (BLCA) is the 10th most common tumor worldwide, particularly practical markers and prognostic models that might guide therapy are needed. We used a non-negative matrix factorization algorithm to classify PI3K pathway-related genes into molecular subtypes. A weighted gene co-expression network analysis (WGCNA) was generated to identify co-expression modules. Univariate Cox regression, least absolute shrinkage sum selection operator-Cox regression, and multivariate Cox regression were utilized to develop a prognostic score model. Kaplan-Meier analysis and receiver operating characteristics were utilized to measure the model's effectiveness. A nomogram was constructed to improve the predictive ability of the model based on clinical parameters and risk. Decision curve analysis (DCA) was used to evaluate the nomogram. To evaluate the immune microenvironment, an estimate algorithm was used. Drug sensitivity was identified using the R package "pRRophetic." UM-UC-3 cell line was used to measure the effect of CDK6 in Western blotting, proliferation assay, and 5-ethynyl-20-deoxyuridine assay. Based on PI3K pathway-related genes, The Cancer Genome Atlas (TCGA)-BLCA and GSE32894 patients were divided into two subtypes. Twenty-five co-expression modules were established using the WGCNA algorithm. A seven-gene signature (CDK6, EGFR, IGF1, ITGB7, PDGFRA, RPS6, and VWF) demonstrated robustness in TCGA and GSE32894 datasets. Expression levels of CDK6 and risk positively correlated with M2 macrophages and IgG. Cisplatin, gemcitabine, methotrexate, mitomycin C, paclitaxel, and vinblastine are sensitive to different groups based on the expression of CDK6 and risk. Functional experiments suggested that CDK6 promotes the proliferation of UM-UC-3 cells. We constructed a seven-gene prognostic signature as an effective marker to predict the outcomes of BLCA patients and guide individual treatment.

SYSMut: decoding the functional significance of rare somatic mutations in cancer

Current tailored-therapy efforts in cancer are largely focused on a small number of highly recurrently mutated driver genes but therapeutic targeting of these oncogenes remains challenging. However, the vast number of genes mutated infrequently across cancers has received less attention, in part, due to a lack of understanding of their biological significance. We present SYSMut, an extendable systems biology platform that can robustly infer the biologic consequences of somatic mutations by integrating routine multiomics profiles in primary tumors. We establish SYSMut's improved performance vis-à-vis state-of-the-art driver gene identification methodologies by recapitulating the functional impact of known driver genes, while additionally identifying novel functionally impactful mutated genes across 29 cancers. Subsequent application of SYSMut on low-frequency gene mutations in head and neck squamous cell (HNSC) cancers, followed by molecular and pharmacogenetic validation, revealed the lipidogenic network as a novel therapeutic vulnerability in aggressive HNSC cancers. SYSMut is thus a robust scalable framework that enables the discovery of new targetable avenues in cancer.

Secreted miR-153 Controls Proliferation and Invasion of Higher Gleason Score Prostate Cancer

Prostate cancer (PC) is a male common neoplasm and is the second leading cause of cancer death in American men. PC is traditionally diagnosed by the evaluation of prostate secreted antigen (PSA) in the blood. Due to the high levels of false positives, digital rectal examination and transrectal ultrasound guided biopsy are necessary in uncertain cases with elevated PSA levels. Nevertheless, the high mortality rate suggests that new PC biomarkers are urgently needed to help clinical diagnosis. In a previous study, we have identified a network of genes, altered in high Gleason Score (GS) PC (GS ≥ 7), being regulated by miR-153. Until now, no publication has explained the mechanism of action of miR-153 in PC. By in vitro studies, we found that the overexpression of miR-153 in high GS cell lines is required to control cell proliferation, migration and invasion rates, targeting Kruppel-like factor 5 (KLF5). Moreover, miR-153 could be secreted by exosomes and microvesicles in the microenvironment and, once entered into the surrounding tissue, could influence cellular growth. Being upregulated in high GS human PC, miR-153 could be proposed as a circulating biomarker for PC diagnosis.

Cancer-related Mutations with Local or Long-range Effects on an Allosteric Loop of p53

The tumor protein 53 (p53) is involved in transcription-dependent and independent processes. Several p53 variants related to cancer have been found to impact protein stability. Other variants, on the contrary, might have little impact on structural stability and have local or long-range effects on the p53 interactome. Our group previously identified a loop in the DNA binding domain (DBD) of p53 (residues 207-213) which can recruit different interactors. Experimental structures of p53 in complex with other proteins strengthen the importance of this interface for protein-protein interactions. We here characterized with structure-based approaches somatic and germline variants of p53 which could have a marginal effect in terms of stability and act locally or allosterically on the region 207-213 with consequences on the cytosolic functions of this protein. To this goal, we studied 1132 variants in the p53 DBD with structure-based approaches, accounting also for protein dynamics. We focused on variants predicted with marginal effects on structural stability. We then investigated each of these variants for their impact on DNA binding, dimerization of the p53 DBD, and intramolecular contacts with the 207-213 region. Furthermore, we identified variants that could modulate long-range the conformation of the region 207-213 using a coarse-grain model for allostery and all-atom molecular dynamics simulations. Our predictions have been further validated using enhanced sampling methods for 15 variants. The methodologies used in this study could be more broadly applied to other p53 variants or cases where conformational changes of loop regions are essential in the function of disease-related proteins.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

Survival-related genes are diversified across cancers but generally enriched in cancer hallmark pathways

Background

Pan-cancer studies have disclosed many commonalities and differences in mutations, copy number variations, and gene expression alterations among cancers. Some of these features are significantly associated with clinical outcomes, and many prognosis-predictive biomarkers or biosignatures have been proposed for specific cancer types. Here, we systematically explored the biological functions and the distribution of survival-related genes (SRGs) across cancers.

Results

We carried out two different statistical survival models on the mRNA expression profiles in 33 cancer types from TCGA. We identified SRGs in each cancer type based on the Cox proportional hazards model and the log-rank test. We found a large difference in the number of SRGs among different cancer types, and most of the identified SRGs were specific to a particular cancer type. While these SRGs were unique to each cancer type, they were found mostly enriched in cancer hallmark pathways, e.g., cell proliferation, cell differentiation, DNA metabolism, and RNA metabolism. We also analyzed the association between cancer driver genes and SRGs and did not find significant over-representation amongst most cancers.

Conclusions

In summary, our work identified all the SRGs for 33 cancer types from TCGA. In addition, the pan-cancer analysis revealed the similarities and the differences in the biological functions of SRGs across cancers. Given the potential of SRGs in clinical utility, our results can serve as a resource for basic research and biotech applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08581-x.

Clinically actionable cancer somatic variants (CACSV): a tumor interpreted dataset for analytical workflows

BACKGROUND

The recent development and enormous application of parallel sequencing technology in oncology has produced immense amounts of cell-specific genetic information. However, publicly available cell-specific genetic variants are not explained by well-established guidelines. Additionally, cell-specific variants interpretation and classification has remained a challenging task and lacks standardization. The Association for Molecular Pathology (AMP), the American Society of Clinical Oncology (ASCO), and the College of American Pathologists (CAP) published the first consensus guidelines for cell-specific variants cataloging and clinical annotations.

METHODS

AMP-ASCO-CAP recommended sources and information were downloaded and used as follows: relative knowledge in oncology clinical practice guidelines; approved, investigative or preclinical drugs; supporting literature and each gene-tumor site correlation. All information was homogenized into a single knowledgebase. Finally, we incorporated the consensus recommendations into a new computational method.

RESULTS

A subset of cancer genetic variants was manually curated to benchmark our method and well-known computational algorithms. We applied the new method on freely available tumor-specific databases to produce a clinically actionable cancer somatic variants (CACSV) dataset in an easy-to-integrate format for most clinical analytical workflows. The research also showed the current challenges and limitations of using different classification systems or computational methods.

CONCLUSION

CACSV is a step toward cell-specific genetic variants standardized interpretation as it is readily adaptable by most clinical laboratory pipelines for somatic variants clinical annotations. CACSV is freely accessible at ( https://github.com/tsobahytm/CACSV/tree/main/dataset ).

EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants

Discovering rare cancer driver genes is difficult because their mutational frequency is too low for statistical detection by computational methods. EPIMUTESTR is an integrative nearest-neighbor machine learning algorithm that identifies such marginal genes by modeling the fitness of their mutations with the phylogenetic Evolutionary Action (EA) score. Over cohorts of sequenced patients from The Cancer Genome Atlas representing 33 tumor types, EPIMUTESTR detected 214 previously inferred cancer driver genes and 137 new candidates never identified computationally before of which seven genes are supported in the COSMIC Cancer Gene Census. EPIMUTESTR achieved better robustness and specificity than existing methods in a number of benchmark methods and datasets.

Machine learning methods for prediction of cancer driver genes: a survey paper

Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.

Comprehensive evaluation of computational methods for predicting cancer driver genes

Optimal methods could effectively improve the accuracy of predicting and identifying candidate driver genes. Various computational methods based on mutational frequency, network and function approaches have been developed to identify mutation driver genes in cancer genomes. However, a comprehensive evaluation of the performance levels of network-, function- and frequency-based methods is lacking. In the present study, we assessed and compared eight performance criteria for eight network-based, one function-based and three frequency-based algorithms using eight benchmark datasets. Under different conditions, the performance of approaches varied in terms of network, measurement and sample size. The frequency-based driverMAPS and network-based HotNet2 methods showed the best overall performance. Network-based algorithms using protein-protein interaction networks outperformed the function- and the frequency-based approaches. Precision, F1 score and Matthews correlation coefficient were low for most approaches. Thus, most of these algorithms require stringent cutoffs to correctly distinguish driver and non-driver genes. We constructed a website named Cancer Driver Catalog (http://159.226.67.237/sun/cancer_driver/), wherein we integrated the gene scores predicted by the foregoing software programs. This resource provides valuable guidance for cancer researchers and clinical oncologists prioritizing cancer driver gene candidates by using an optimal tool.

LncRNAs and Rheumatoid Arthritis: From Identifying Mechanisms to Clinical Investigation

Rheumatoid arthritis (RA) is a systemic chronic autoinflammatory disease, and the synovial hyperplasia, pannus formation, articular cartilage damage and bone matrix destruction caused by immune system abnormalities are the main features of RA. The use of Disease Modifying Anti-Rheumatic Drugs (DMARDs) has achieved great advances in the therapy of RA. Yet there are still patients facing the problem of poor response to drug therapy or drug intolerance. Current therapy methods can only moderate RA progress, but cannot stop or reverse the damage it has caused. Recent studies have reported that there are a variety of long non-coding RNAs (LncRNAs) that have been implicated in mediating many aspects of RA. Understanding the mechanism of LncRNAs in RA is therefore critical for the development of new therapy strategies and prevention strategies. In this review, we systematically elucidate the biological roles and mechanisms of action of LncRNAs and their mechanisms of action in RA. Additionally, we also highlight the potential value of LncRNAs in the clinical diagnosis and therapy of RA.

Transcriptional and Epigenetic Bioinformatic Analysis of Claudin-9 Regulation in Gastric Cancer

Gastric cancer is a heterogeneous disease that represents 5% to 10% of all new cancer cases worldwide. Advances in histological diagnosis and the discovery of new genes have admitted new genomic classifications. Nevertheless, the bioinformatic analysis of gastric cancer databases has favored the detection of specific differentially expressed genes with biological significance. Claudins, a family of proteins involved in tight junction physiology, have emerged as the key regulators of cellular processes, such as growth, proliferation, and migration, associated with cancer progression. The expression of Claudin-9 in the gastric cancer tissue has been linked to poor prognosis, however, its transcriptional and epigenetic regulations demand a more comprehensive analysis. Using the neural network promoter prediction, TransFact, Uniprot-KB, Expasy-SOPMA, protein data bank, proteomics DB, Interpro, BioGRID, String, and the FASTA protein sequence databases and software, we found the following: (1) the promoter sequence has an unconventional structure, including different transcriptional regulation elements distributed throughout it, (2) GATA 4, GATA 6, and KLF5 are the key regulators of Claudin-9 expression, (3) Oct1, NF-κB, AP-1, c-Ets-1, and HNF-3β have the higher binding affinity to the CLDN9 promoter, (4) Claudin-9 interacts with cell differentiation and development proteins, (5) CLDN9 is highly methylated, and (6) Claudin-9 expression is associated with poor survival. In conclusion, Claudin-9 is a protein that should be considered a diagnostic marker as its gene promoter region binds to the transcription factors associated with the deregulation of cell control, enhanced cell proliferation, and metastasis.