Mutation Data Research Articles

Improving risk stratification for 2022 European LeukemiaNet favorable-risk patients with acute myeloid leukemia

Assignment of patients diagnosed with acute myeloid leukemia (AML) to the 2022 European LeukemiaNet (ELN) Favorable genetic-risk group has important clinical implications, as allogeneic stem-cell transplantation in first complete remission (CR) is not advised due to a relatively good outcome of patients receiving chemotherapy alone and transplant-associated mortality. However, not all Favorable genetic-risk patients experience long-term relapse-free survival (RFS), making recognition of patients who would most likely be cured of high importance. We analyzed 297 patients aged <60 years with de novo AML classified as 2022 ELN Favorable genetic-risk who achieved a CR and had RNA-seq and gene mutation data from diagnostic samples available (Alliance trial A152010). To identify prognostically relevant transcripts that can distinguish patients cured from patients susceptible to lower- or higher-risk of relapse or death, we fit a regularized mixture cure model (MCM), where RNA-seq expression values were our candidate covariates. To validate the identified transcripts, we analyzed 75 patients with de novo AML aged <60 years included in the 2022 ELN Favorable genetic-risk group who achieved a CR in an independent test set from Gene Expression Omnibus (GSE37642). Our MCM identified 145 transcripts associated with cure, or long-term RFS, and 149 transcripts associated with latency, or shorter-term time-to-relapse. The area under the curve and C-statistic were, respectively, 0.946 and 0.856 for our training and 0.877 and 0.857 for our test sets. Our results suggest that the Favorable-risk group includes distinct transcriptionally defined subgroups with different biological properties, which may be useful for refining this genetic-risk category.

HATZFS predicts pancreatic cancer driver biomarkers by hierarchical reinforcement learning and zero-forcing set

The search for cancer biomarkers is of great significance for the early diagnosis and clinical treatment planning of pancreatic cancer. RNA transcription networks can effectively represent the interactions between RNA molecules, and the irregular perturbations in these interactions can lead to pathological states, making them commonly used for identifying cancer biomarkers. However, traditional network-based methods for identifying cancer biomarkers often rely on prior information and mutation data. Furthermore, most network controllability-based methods face the limitation of high computational complexity, preventing their integration with deep learning methods. In this paper, we propose a novel deep learning framework, called HATZFS, for identifying driver biomarkers in pancreatic cancer using RNA differential expression data. The proposed model combines hierarchical deep Q-network (HDQN), graph attention network (GAT), and zero-forcing set (ZFS). Firstly, a pancreatic cancer RNA transcription regulatory network is constructed to embed the complex relationship among Long non-coding RNA (lncRNA), microRNA (miRNA) and messenger RNA (mRNA). Then, we transform it into multiple subgraphs using graph sampling method, and apply GAT to learn the node features of the subgraphs. Finally, the HATZFS performs HDQN to select subgraphs and identify important RNA as driver nodes, and determining the controllability of the subgraphs based on ZFS. More important, the paper theoretically proves that when all subgraphs are controllable, the original graph is also controllable, and the union of the driver node sets of all subgraphs is equal to the driver node set of the original graph. To confirm the effectiveness of the proposed model, comprehensive experiments are conducted on the benchmark dataset consisting of 14 databases, comparing it with eight other algorithms. The experimental results demonstrate that the proposed model outperforms other methods. Overall, HATZFS not only provides the importance of all RNA molecules in the pancreatic cancer RNA regulatory network, but also identifies the driver node set of the network as driver biomarkers. Source code can be accessed at https://github.com/HongJieTongXue/HATZFS.

Mechanism of Nucleic Acid Phosphodiester Bond Cleavage by Human Endonuclease V: MD and QM/MM Calculations Reveal a Versatile Metal Dependence.

Human endonuclease V (EndoV) catalytically removes deaminated nucleobases by cleaving the phosphodiester bond as part of RNA metabolism. Despite being implicated in several diseases (cancers, cardiovascular diseases, and neurological disorders) and potentially being a useful tool in biotechnology, details of the human EndoV catalytic pathway remain unclear due to limited experimental information beyond a crystal structure of the apoenzyme and select mutational data. Since a mechanistic understanding is critical for further deciphering the central roles and expanding applications of human EndoV in medicine and biotechnology, molecular dynamics (MD) simulations and quantum mechanics/molecular mechanics (QM/MM) calculations were used to unveil the atomistic details of the catalytic pathway. Due to controversies surrounding the number of metals required for nuclease activity, enzyme-substrate models with different numbers of active site metals and various metal-substrate binding configurations were built based on structural data for other nucleases. Subsequent MD simulations revealed the structure and stability of the human EndoV-substrate complex for a range of active site metal binding architectures. Four unique pathways were then characterized using QM/MM that vary in metal number (one versus two) and modes of substrate coordination [direct versus indirect (water-mediated)], with several mechanisms being fully consistent with experimental structural, kinetic, and mutational data for related nucleases, including members of the EndoV family. Beyond uncovering key roles for several active site amino acids (D240 and K155), our calculations highlight that while one metal is essential for human EndoV activity, the enzyme can benefit from using two metals due to the presence of two suitable metal binding sites. By directly comparing one- versus two-metal-mediated P-O bond cleavage reactions within the confines of the same active site, our work brings a fresh perspective to the "number of metals" controversy.

Mut-Map: Comprehensive Computational Pipeline for Structural Mapping and Analysis of Cancer-Associated Mutations.

Understanding the functional impact of genetic mutations on protein structures is essential for advancing cancer research and developing targeted therapies. The main challenge lies in accurately mapping these mutations to protein structures and analysing their effects on protein function. To address this, Mut-Map (https://genemutation.org/) is a comprehensive computational pipeline designed to integrate mutation data from the Catalogue Of Somatic Mutations In Cancer database with protein structural data from the Protein Data Bank and AlphaFold models. The pipeline begins by taking a UniProt ID and proceeds through mapping corresponding Protein Data Bank structures, renumbering residues, and assessing disorder percentages. It then overlays mutation data, categorizes mutations based on structural context, and visualizes them using advanced tools like MolStar. This approach allows for a detailed analysis of how mutations may disrupt protein function by affecting key regions such as DNA interfaces, ligand-binding sites, and dimer interactions. To validate the pipeline, a case study on the TP53 gene, a critical tumour suppressor often mutated in cancers, was conducted. The analysis highlighted the most frequent mutations occurring at the DNA-binding interface, providing insights into their potential role in cancer progression. Mut-Map offers a powerful resource for elucidating the structural implications of cancer-associated mutations, paving the way for more targeted therapeutic strategies and advancing our understanding of protein structure-function relationships.

Impact of phylogeny on the inference of functional sectors from protein sequence data.

Statistical analysis of multiple sequence alignments of homologous proteins has revealed groups of coevolving amino acids called sectors. These groups of amino-acid sites feature collective correlations in their amino-acid usage, and they are associated to functional properties. Modeling showed that nonlinear selection on an additive functional trait of a protein is generically expected to give rise to a functional sector. These modeling results motivated a principled method, called ICOD, which is designed to identify functional sectors, as well as mutational effects, from sequence data. However, a challenge for all methods aiming to identify sectors from multiple sequence alignments is that correlations in amino-acid usage can also arise from the mere fact that homologous sequences share common ancestry, i.e. from phylogeny. Here, we generate controlled synthetic data from a minimal model comprising both phylogeny and functional sectors. We use this data to dissect the impact of phylogeny on sector identification and on mutational effect inference by different methods. We find that ICOD is most robust to phylogeny, but that conservation is also quite robust. Next, we consider natural multiple sequence alignments of protein families for which deep mutational scan experimental data is available. We show that in this natural data, conservation and ICOD best identify sites with strong functional roles, in agreement with our results on synthetic data. Importantly, these two methods have different premises, since they respectively focus on conservation and on correlations. Thus, their joint use can reveal complementary information.

Engineering bacteriophages through deep mining of metagenomic motifs.

Bacteriophages can adapt to new hosts by altering sequence motifs through recombination or convergent evolution. Where such motifs exist and what fitness advantage they confer remains largely unknown. We report a new method, Metagenomic Sequence Informed Functional Scoring (Meta-SIFT), to discover sequence motifs in metagenomic datasets that can be used to engineer phage activity. Meta-SIFT uses experimental deep mutational scanning data to create sequence profiles to enable deep mining of metagenomes for functional motifs which are otherwise invisible to searches. We experimentally tested over 17,000 Meta-SIFT derived sequence motifs in the receptor-binding protein of the T7 phage. The screen revealed thousands of T7 variants with novel host specificity with functional motifs sourced from distant families. Position, substitution and location preferences dictated specificity across a panel of 20 hosts and conditions. To demonstrate therapeutic utility, we engineered active T7 variants against foodborne pathogen E. coli O121. Meta-SIFT is a powerful tool to unlock the functional potential encoded in phage metagenomes to engineer bacteriophages.

Abstract A014: Protein-informed gene methylation signatures to predict treatment response and outcomes in pancreatic ductal adenocarcinoma

Abstract Pancreatic ductal adenocarcinoma (PDA) is a deadly cancer with dismal outcomes. We need new biomarkers to optimize the limited treatment combinations used in current clinical practice. Many machine-learning models have been presented based on mutational or transcriptome data claiming to predict outcomes. Still, they do not provide actionable targets (genes) that could improve treatment response. Moreover, DNA-methylation changes that influence gene/protein expression have been explored for this purpose. From the literature, we curated a 122-gene panel (January 1970 to April 2024) constructed on the proteins with preclinical (on cell lines or mouse models) or clinical (tissue-based studies) evidence suggesting their individual roles in influencing response to drugs often used in managing PDA such as gemcitabine (Gem), nab-paclitaxel, oxaliplatin, 5-fluorouracil, irinotecan, and cisplatin. We hypothesized that methylation changes in these genes are surrogates for gene/protein expression and tested their clinical significance in The Cancer Genome Atlas (TCGA) PDA population (n=184). The goal was to develop methylation signatures composed of specific cytosines followed by guanine residue (CpG) sites that aid in identifying patients at high risk of treatment failure and poor outcomes. We used elastic net multivariate analysis to calculate survival and plot Kaplan Meier plots based on our gene panel. The alpha (α) used for this script was calculated based on the lowest mean error and the minimum lambda (λ) value accordingly for each iteration (100 iterations to calculate the best values for α and λ). Our analysis yielded 28 methylation signatures that could identify high-risk PDA patients with poor overall survival (OS). The number of CpG sites in these signatures ranged from 1 to 1933. The differences in OS for these signatures were better than those available in the literature. The best signature had 18 CpG sites (17 months (m) vs. not evaluable) followed by a 53-CpG site signature (16 m vs. 67m) and a 6-CpG site signature (17m vs. 67m). A signature with the highest CpG sites (1933) was not better than those with smaller ones (16m vs. 21m). Multiple signatures had &amp;gt;1 CpG sites from one gene. Early growth response 1 (EGR1) coding the KCNH2 gene has featured in most signatures. One CpG site in this gene had a clinically significant prognostic value (15m vs. 30m). Other frequently featured genes were CCDC85A, EGFR, SST, and HMGA1. The main drawbacks for this study were that Gem-related genes (&amp;gt; 60) dominated the panel, and those with Gem treatment dominated the patient population. This panel should be further refined to make it more relevant to current clinical practice. We provide preliminary evidence suggesting the clinical significance of our panel and lay the foundation for future studies that would enable us to personalize prognostication and treatment selection in PDA patients. Our work can provide novel therapeutic targets that, alone or in combination with chemotherapy, can improve the outcomes for PDA. Citation Format: Ashish Manne, Harshitha Puram, Deepak Sherpally, Sravan Jeepalyam, Upender Manne. Protein-informed gene methylation signatures to predict treatment response and outcomes in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A014.

Sialylation-associated long non-coding RNA signature predicts the prognosis, tumor microenvironment, and immunotherapy and chemotherapy options in uterine corpus endometrial carcinoma

BackgroundSialylation in uterine corpus endometrial carcinoma (UCEC) differs significantly from apoptotic and ferroptosis pathways. It plays a crucial role in cancer progression and immune response modulation. Exploring how sialylation affects tumor behavior and its link with long non-coding RNAs (lncRNAs) may provide new insights into UCEC prognosis and treatment.MethodsWe obtained RNA transcriptome, clinical, and mutation data of UCEC samples from the TCGA database. Our approach involved developing a risk model based on the co-expression patterns of sialylation genes and lncRNAs. Prognostic lncRNAs were identified through Cox regression and further refined using LASSO analysis. To understand the biological functions and pathways of model-associated differentially expressed genes (MADEGs), we conducted enrichment analyses. We also assessed the immune infiltration status of MADEGs using eight different algorithms, which helped in evaluating the potential for immunotherapy. Additionally, we validated the expression of these lncRNAs in UCEC using cell lines and clinical samples.ResultsWe developed a UCEC risk model using five sialylation-related lncRNAs (AC004884.2, AC026202.2, LINC01579, LINC00942, SLC16A1-AS1). This model, confirmed through Cox analysis and clinical evaluation, effectively predicted patient outcomes. Survival data analysis across entire cohort, as well as within training and test groups, indicated better survival in low-risk UCEC patients. Enrichment analyses linked MADEGs to sialylation functions and cancer pathways. High-risk patients showed increased responsiveness to immune checkpoint inhibitors (ICIs), as indicated by immunological assessments. Subgroup C2 patients showed superior outcomes and a robust response to immunotherapy and chemotherapy. Notably, LINC01579, LINC00942, and SLC16A1-AS1 were significantly overexpressed in UCEC clinical tumor samples as well as in Ishikawa and HEC-1-B cell lines, compared to the normal groups.ConclusionsThis lncRNA signature associated with sialylation could guide prognosis, enhance the understanding of molecular mechanisms, and inform treatment strategies in UCEC. It highlights the potential for the use of ICIs and chemotherapy.

A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma

Background and ObjectiveIn Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging to answer unmet clinical needs to derive novel quantitative prognostic factors. We realized a pipeline that relies on survival machine-learning (SML) classifiers and explainability based on patients’ follow-up (FU) to stratify prognosis from the public-available multi-omic datasets of the CPTAC-PDA project. Materials and MethodsAnalyzed datasets included tumor-annotated radiologic images, clinical, and mutational data. A feature selection was based on univariate (UV) and multivariate (MV) survival analyses according to Overall Survival (OS) and recurrence (REC). In this study, we considered seven multi-omic datasets and compared four SML classifiers: Cox, survival random forest, generalized boosted, and support vector machines (SVM). For each classifier, we assessed the concordance (C) index on the validation set. The best classifiers for the validation set on both OS and REC underwent explainability analyses using SurvSHAP(t), which extends SHapley Additive exPlanations (SHAP). ResultsAccording to OS, after UV and MV analyses we selected 18/37 and 10/37 multi-omic features, respectively. According to REC, based on UV and MV analyses we selected 10/35 and 5/35 determinants, respectively. Generally, SML classifiers including radiomics outperformed those modelled on clinical or mutational predictors. For OS, the Cox model encompassing radiomic, clinical, and mutational features reached 75 % of C index, outperforming other classifiers. On the other hand, for REC, the SVM model including only radiomics emerged as the best-performing, with 68 % of C index. For OS, SurvSHAP(t) identified the first order Median Gray Level (GL) intensities, the gender, the tumor grade, the Joint Energy GL Co-occurrence Matrix (GLCM), and the GLCM Informational Measures of Correlations of type 1 as the most important features. For REC, the first order Median GL intensities, the GL size zone matrix Small Area Low GL Emphasis, and first order variance of GL intensities emerged as the most discriminative. ConclusionsIn this work, radiomics showed the potential for improving patients’ risk stratification in PDA. Furthermore, a deeper understanding of how radiomics can contribute to prognosis in PDA was achieved with a time-dependent explainability of the top multi-omic predictors.

Improving Individualized Rhabdomyosarcoma Prognosis Predictions Using Somatic Molecular Biomarkers.

Molecular markers, such as FOXO1 fusion genes and TP53 and MYOD1 mutations, increasingly influence risk-stratified treatment selection for pediatric rhabdomyosarcoma (RMS). This study aims to integrate molecular and clinical data to produce individualized prognosis predictions that can further improve treatment selection. Clinical variables and somatic mutation data for 20 genes from 641 RMS patients in the United Kingdom and the United States were used to develop three Cox proportional hazard models for predicting event-free survival (EFS). The 'Baseline Clinical' (BC) model included treatment location, age, fusion status, and risk group. The 'Gene Enhanced 2' (GE2) model added TP53 and MYOD1 mutations to the BC predictors. The 'Gene Enhanced 6' (GE6) model further included NF1, MET, CDKN2A, and MYCN mutations, selected through LASSO regression. Model performance was assessed using likelihood ratio (LR) tests and optimism-adjusted, bootstrapped validation and calibration metrics. The GE6 model demonstrated superior predictive performance, offering 39% more predictive information than the BC model (LR p<0.001) and 15% more than the GE2 model (LR p<0.001). The GE6 model achieved the highest discrimination with a C-index of 0.7087, a Nagalkerke R2 of 0.205, and appropriate calibration. Mutations in TP53, MYOD1, CDKN2A, MET, and MYCN were associated with higher hazards, while NF1 mutation correlated with lower hazard. Individual prognosis predictions varied between models in ways that may suggest different treatments for the same patient. For example, the 5-year EFS for a 10-year-old patient with high-risk, fusion-negative, NF1-positive disease was 50.0% (95% confidence interval: 39-64%) from BC but 76% (64-90%) from GE6. Incorporating molecular markers into RMS prognosis models improves prognosis predictions. Individualized prognosis predictions may suggest alternative treatment regimens compared to traditional risk-classification schemas. Improved clinical variables and external validation are required prior to implementing these models into clinical practice.

Construction of a cuproptosis‑related lncRNA signature to predict biochemical recurrence of prostate cancer.

Biochemical recurrence (BCR) is common in prostate cancer (PCa), and patients with BCR usually have a poor prognosis. Cuproptosis is a unique type of cell death, and copper homeostasis is crucial to the occurrence and development of malignancies. The present study aimed to explore the prognostic value of cuproptosis-related long non-coding RNAs (lncRNAs; CRLs) in PCa and to develop a predictive signature for forecasting BCR in patients with PCa. Using The Cancer Genome Atlas database, transcriptomic, mutation and clinical data were collected from patients with PCa. A total of 121 CRLs were identified using Pearson's correlation coefficient. Subsequently, a 6-CRL signature consisting of AC087276.2, CNNM3-DT, AC090198.1, AC138207.5, METTL14-DT and LINC01515 was created to predict the BCR of patients with PCa through Cox and least absolute shrinkage and selection operator regression analyses. Kaplan-Meier curve analysis demonstrated that high-risk patients had a low BCR-free survival rate. In addition, there was a substantial difference between the high- and low-risk groups in the immune microenvironment, immune therapy, drug sensitivity and tumor mutational burden. A nomogram integrating the Gleason score, 6-CRL signature and clinical T-stage was established and evaluated. Finally, the expression of signature lncRNAs in PCa cells was verified through reverse transcription-quantitative PCR. In conclusion, the 6-CRL signature may be a potential tool for making predictions regarding BCR in patients with PCa, and the prognostic nomogram may be considered a practical tool for clinical decision-making.

Exploration of Key Genes Combining with Immune Infiltration Level andTumor Mutational Burden in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a lethal malignancy due to its heterogeneity and aggressive behavior. Recently, somatic mutations and tumor cell interactions with the surrounding tumor immune microenvironment (TIME) have been reported to participate in HCC carcinogenesis and predict HCC progression. In this study, we aimed to investigate the association between tumor mutational burden (TMB) and TIME in HCC. Additionally, we sought to identify differentially expressed genes (DEGs) associated with HCC prognosis and progression. The expression, clinical, and mutational data were downloaded from the cancer genome atlas (TCGA) database. The immune infiltration levels and TMB levels of the HCC samples were estimated and the samples were divided into immune cluster (ICR)-1 and 2 based on immune infiltration score and high and low TMB groups based on TMB score. Thereafter, differential gene expression analysis was conducted to identify the DEGs in the ICR1/2 and high/low TMB groups, and the intersecting DEGs were selected. Thereafter, Cox regression analysis was performed on 89 significant DEGs, among which 19 were associated with prognosis. These 19 DEGs were then used to construct a prognostic model based on their expression levels and regression coefficients. Thereafter, we analyzed the DEGs in mutant and wildtype TP53 HCC samples and identified high BCL10 and TRAF3 expression in the mutant TP53 samples. BCL10 and TRAF3 expression was detected by real-time quantitative reverse transcription PCR and immunohistochemistry, and their clinical correlation, biological function, and immune infiltration levels were analyzed by chi-square analyses, Gene Set Enrichment Analysis (GSEA), and "ssGSEA", respectively. The results of our study revealed that immune infiltration level was correlated with TMB and that they synergistically predicted poor prognosis of HCC patients. DEGs enriched in immune-related pathways could serve as indicators of immunotherapy response in HCC. Among these DEGs, BCL10 and TRAF3 were highly expressed in HCC tissues, especially in the mutant TP53 group, and they co-operatively exhibited immunological function, thereby affecting HCC progression and prognosis. In this study, we identified BCL10 and TRAF3 as potential prognostic indicators in HCC patients. Additionally, we found that BCL10 and TRAF3 influence TMB and TIME in HCC patients and can be used for the development of immune-based therapies for improving the long-term survival of HCC patients.

Development of the TP53 mutation associated hypopharyngeal squamous cell carcinoma prognostic model through bulk multi-omics sequencing and single-cell sequencing

ObjectiveThe aim of this study was to construct a prognostic model based on the TP53 mutation to calculate prognostic risk scores of patients with HPSCC. MethodsTP53 mutation and transcriptome data were downloaded from the TCGA databases. Gene expression data from GSE65858, GSE41613, GSE3292, GSE31056, GSE39366, and GSE227156 datasets were downloaded from the GEO database. GSEA, univariate, multivariate Cox analyses, and LASSO analysis were employed to identify key genes and construct the prognostic model. ROC curves were utilized to validate the OS and RFS results obtained from the model. The associations between risk scores with various clinicopathological characteristics and immune scores were analyzed via ggplot2, corrplot package, and GSVA, respectively. Single-cell sequencing data was analyzed via unbiased clustering and SingleR cell annotations. ResultsInitially, two key genes, POLD2 and POLR2G, were identified and utilized to construct the prognostic model. Samples were divided into different risk groups via the risk scores obtained from the model, with high-risk group samples exhibiting poorer prognosis. Furthermore, the risk score exhibited a positive correlation with lymphatic metastasis in patients and the immune scores of CD4+ T, CD8+ T, dendritic cell, macrophage, and neutrophil. The immune responses also exhibited notable disparities between the high- and low-risk groups. The results of single-cell sequencing analysis demonstrated that epithelial cells and macrophages were relatively abundant in HPSCC samples. POLD2 and POLR2G exhibited higher expressions in epithelial cells, with most of the identified pathways also enriched in epithelial cells. ConclusionThe prognostic model exhibited a significant capacity for predicting the prognosis of HSPCC samples based on the TP53 mutation conditions and may also predict the cancer characteristics and immune infiltration scores of samples via different risk scores obtained from the model. Level of evidenceLevel 5.

Prognostic signature and immunotherapeutic relevance of Focal adhesion signaling pathway-related genes in osteosarcoma

BackgroundAs the most common primary malignant bone tumor in children and adolescents, osteosarcoma currently lacks an effective clinical cure. Focal adhesion plays a crucial role in tumor invasion, migration, and drug resistance by mediating communication between the extracellular matrix and tumor cells. This study investigated the prognostic features and immunotherapeutic relevance of focal adhesion pathway-related genes in osteosarcoma to aid in the development of new therapeutic options. MethodsWe obtained mutational, transcriptomic, gene expression, and clinical data of osteosarcoma patients from the Gene Expression Omnibus (GEO) and Therapeutically Applicable Research to Generate Effective (TARGET) databases. Differentially expressed genes were screened, followed by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. Kaplan-Meier survival analysis was performed for genes related to the focal adhesion pathway, and multivariate Cox regression analysis was employed to construct a prognostic signature model. Genes such as SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, and LAG3 were extracted from the TARGET and CCLE databases for osteosarcoma patients and osteosarcoma cell lines, respectively，to observe the expression of immune checkpoint-related genes. Finally, qRT-PCR was used to verify the expression of these immune checkpoint-related genes in osteosarcoma cell lines. ResultsIn our study, 376 samples were analyzed, including 369 osteosarcoma samples and 7 normal tissue samples. We identified 50 up-regulated and 28 down-regulated differentially expressed genes. Among these, 10 Candidate genes relative to focal Adhesion were selected， and CAV1, ZYX, and ITGA5 were found to have a significant prognostic role based on survival analysis of osteosarcoma samples from the TARGET database. A predictive signature model related to the focal adhesion signaling pathway was constructed using these genes, and the AUCs of the 1-year, 3-year, and 5-year ROC curves were 0. 647, 0. 712, and 0. 717, respectively. The overall survival (OS) rate of osteosarcoma patients with high-risk scores was poorer than those with low-risk scores. Then, samples were divided into two subgroups based on the expression of the three genes, revealing significant differences in the expression of certain immune checkpoint-related genes between the subgroups. Additionally, above three genes and immune checkpoint-related genes in osteosarcoma cell lines were extracted from the CCLE database, showing high expression levels in eight osteosarcoma cell lines. We observed that CD274 and PDCD1LG2 were highly expressed in some osteosarcoma cell lines. Finally, the expression of CAV1, ZYX, ITGA5, CD80, CD274, and PDCD1LG2 in osteosarcoma cell lines was verified by qRT-PCR. ConclusionsOur study validated the prognostic role of three focal adhesion pathway-related genes (ZYX, CAV1, and ITGA5) in patients with osteosarcoma and constructed a prognostic signature model associated with the focal adhesion signaling pathway. We identified significant differences in the expression of multiple immune checkpoint-related genes among subgroups defined by the three genes. Additionally, CD274 and PDCD1LG2 showed higher expression in osteosarcoma cell lines characterized by these genes. These findings may aid in the selection of effective immunotherapy for specific osteosarcoma patients.

Anoikis-related subtype and prognosis analyses based on bioinformatics, and an expression verification of ANGPTL4 based on experiments of lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high mortality. Anoikis resistance is an important mechanism of tumor cell proliferation and migration. Our research is devoted to exploring the role of anoikis in the diagnosis, classification, and prognosis of LUAD. We downloaded the expression profile, mutation, and clinical data of LUAD from The Cancer Genome Atlas (TCGA) database. The "ConsensusClusterPlus" package was then used for the cluster analysis, and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were used to establish the prognostic model. We verified the reliability of the model using a Gene Expression Omnibus (GEO) data set. A gene set variation analysis (GSVA) was conducted to investigate the functional enrichment differences in the different clusters and risk groups. The CIBERSORT algorithm and a single-sample gene set enrichment analysis (ssGSEA) were used to analyze immune cell infiltration. The tumor mutation burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) scores were used to evaluate the patients' sensitivity to immunotherapy. Immunohistochemical staining of tissue microarrays was used to verify the correlation between ANGPTL4 expression and the clinicopathological characteristics and prognosis of LUAD patients. First, we screened 135 differentially expressed anoikis-related genes (ARGs) and 23 prognosis-related ARGs from TCGA-LUAD data set. Next, 494 LUAD samples were allocated to cluster A and cluster B based on the 23 prognosis-related ARGs. The Kaplan-Meier (K-M) analysis showed the overall survival (OS) of cluster B was better than that of cluster A. The clinicopathological characteristics and functional enrichment analyses revealed significant differences between clusters A and B. The tumor microenvironment (TME) analysis showed that cluster B had more immune cell infiltration and a higher TME score than cluster A. Subsequently, a LASSO Cox regression model of LUAD was constructed with ten ARGs. The K-M analysis showed that the low-risk patients had longer OS than the high-risk patients. The receiver operating characteristic curve, nomogram, and GEO data set verification results showed that the model had high accuracy and reliability. The level of immune cell infiltration and TME score were higher in the low-risk group than the high-risk group. The high-risk group had stronger sensitivity to immune checkpoint block therapy and weaker sensitivity to chemotherapy drugs than the low-risk group. ANGPTL4 expression was correlated with stage, tumor differentiation, tumor size, lymph node metastasis, and OS. We discovered novel molecular subtypes and constructed a novel prognostic model of LUAD. Our findings provide important insights into subtype classification and the accurate survival prediction of LUAD. We also identified ANGPTL4 as a prognostic indicator of LUAD.

Rapid discrimination between deleterious and benign missense mutations in the CAGI 6 experiment

We describe the machine learning tool that we applied in the CAGI 6 experiment to predict whether single residue mutations in proteins are deleterious or benign. This tool was trained using only single sequences, i.e., without multiple sequence alignments or structural information. Instead, we used global characterizations of the protein sequence. Training and testing data for human gene mutations was obtained from ClinVar (ncbi.nlm.nih.gov/pub/ClinVar/), and for non-human gene mutations from Uniprot (www.uniprot.org). Testing was done on post-training data from ClinVar. This testing yielded high AUC and Matthews correlation coefficient (MCC) for well trained examples but low generalizability. For genes with either sparse or unbalanced training data, the prediction accuracy is poor. The resulting prediction server is available online at http://www.mamiris.com/Shoni.cagi6.

UBR1 is a prognostic biomarker and therapeutic target associated with immune cell infiltration in gastric cancer.

Ubiquitination is a targeted protein modification process mediated by intracellular molecules. UBR1 encodes a protein that binds to unstable N-terminal residues of substrate proteins and contributes to the formation of substrate-linked polyubiquitin chains. However, the function and cellular pathways of UBR1 in tumors have received inadequate attention. This study aimed to investigate the potential of UBR1 as a prognostic biomarker and immunotherapy target for stomach adenocarcinoma (STAD) as well as its biological function and molecular mechanism in relation to the disease. Differential expression and pan-cancer gene set enrichment analysis (GSEA) were conducted using The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Genotype-Tissue Expression (GTEx) datasets. The Human Protein Atlas (HPA) database was utilized to identify UBR1-enriched pathways in AGS cells and to compare immunohistochemical differences between cancerous and adjacent non-cancerous tissues in gastric cancer. Quantitative Polymerase Chain Reaction (QPCR) and Western blot (WB) analyses were employed to validate these findings in both cancerous and adjacent non-cancerous tissues of gastric cancer. UBR1 expression in GES-1 and four gastric cancer cell lines was assessed using QPCR and WB. Kaplan-Meier curves, univariate and multivariate Cox regression analyses, and receiver operating characteristic (ROC) curve analyses were performed to evaluate the prognostic and diagnostic roles of UBR1. Additionally, the correlation between UBR1 expression and clinical parameters was analyzed using TCGA and GEO databases. UBR1 mutation data were obtained from the cBioPortal database. The mutation landscape, mutation-associated genes, protein structure, tumor mutation burden (TMB), and microsatellite instability (MSI) correlations were analyzed and illustrated. The biological functions of UBR1 were investigated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. The correlation between UBR1 and immune infiltration was assessed using TIMER and EPIC computational methods. Protein expression levels of UBR1 in gastric cancer cell lines were determined by immunohistochemistry (IHC) and WB analysis. Quantitative real-time PCR (qRT-PCR) was employed to analyze mRNA expression. Immunoprecipitation (IP) assays were conducted to detect protein-protein interactions between UBR1 and PDL1, while cellular immunofluorescence was used to observe the co-localization of these proteins. Cell proliferation was evaluated using CCK8 and colony formation assays. Cell migration was assessed using Transwell and wound healing assays. Finally, apoptosis was analyzed using flow cytometry, and WB was used to detect changes in apoptotic proteins and NF-κB P65 pathway proteins. UBR1 was upregulated in 28 cancer types, including STAD, and its overexpression was validated in gastric cancer cell lines and tissues. UBR1 expression was associated with advanced pathological characteristics. High UBR1 expression was linked to poor prognostic outcomes, including overall survival (OS), progression-free interval (PFI), disease-specific survival (DSS), as well as responses to surgery, chemotherapy, and HER2 expression. UBR1 expression showed significant correlations with clinical parameters such as age, gender, TNM stage, pathological stage, tumor resection, and anti-reflux therapy. Amplifications and deletions were the most frequent genetic alterations associated with UBR1. According to KEGG and GSEA analyses, UBR1 was significantly associated with several cancer pathways, oxidative phosphorylation, and the TNF-NFκB pathway. UBR1 also exhibited a significant correlation with immune cell infiltration and immunotherapy, including a direct interaction with PDL1. Knockdown of UBR1 inhibited the proliferation, migration, and invasion of STAD cells and promoted apoptosis. UBR1 is overexpressed in STAD, promoting its progression and positively correlating with immune cell infiltration and immunotherapeutic responses. Therefore, UBR1 could be a promising biomarker for the prognosis and immunotherapy of STAD.

A mutational signature and ARID1A mutation associated with outcome in hepatocellular carcinoma.

The prognosis of hepatocellular carcinoma (HCC) is poor and there is no stable and reliable molecular biomarker for evaluation. This study attempted to find reliable prognostic markers from tumor mutational profiles. A total of 362 HCC samples with whole-exome sequencing were collected as discovery datasets, and 200 samples with targeted sequencing were used for validation of the relevant results. All HCC samples were obtained from previously published studies. Bayesian non-negative matrix factorization was used to extract mutational signatures, and multivariate Cox regression models were utilized to identify the prognostic role of mutational factors. Gene set enrichment analysis was employed to discover potential signaling pathways associated with specific mutational groups. In the HCC discovery dataset, a total of four mutational signatures (i.e., signatures 4, 6, 16, and 22) were extracted, of which signature 16 characterized by T>C mutations was observed to be associated with favorable HCC prognosis, and this correlation was also found in the validation dataset. Further analysis showed that patients with ARID1A mutations exhibited inferior survival outcomes in both discovery and validation datasets. Mechanistic exploration revealed that the presence of signature 16 was associated with better immune infiltration and tumor immunogenicity, while patients with ARID1A mutations were away from these favorable immunological features. By integrating somatic mutation data and clinical information of HCC, this study identified that signature 16 and ARID1A mutations were associated with better and worse outcomes respectively, providing a basis for prognosis prediction and clinical treatment strategies of HCC.

DRN-CDR: A cancer drug response prediction model using multi-omics and drug features

Cancer drug response (CDR) prediction is an important area of research that aims to personalize cancer therapy, optimizing treatment plans for maximum effectiveness while minimizing potential negative effects. Despite the advancements in Deep learning techniques, the effective integration of multi-omics data for drug response prediction remains challenging. In this paper, a regression method using Deep ResNet for CDR (DRN-CDR) prediction is proposed. We aim to explore the potential of considering sole cancer genes in drug response prediction. Here the multi-omics data such as gene expressions, mutation data, and methylation data along with the molecular structural information of drugs were integrated to predict the IC50 values of drugs. Drug features are extracted by employing a Uniform Graph Convolution Network, while Cell line features are extracted using a combination of Convolutional Neural Network and Fully Connected Networks. These features are then concatenated and fed into a deep ResNet for the prediction of IC50 values between Drug – Cell line pairs. The proposed method yielded higher Pearson’s correlation coefficient (rp) of 0.7938 with lowest Root Mean Squared Error (RMSE) value of 0.92 when compared with similar methods of tCNNS, MOLI, DeepCDR, TGSA, NIHGCN, DeepTTA, GraTransDRP and TSGCNN. Further, when the model is extended to a classification problem to categorize drugs as sensitive or resistant, we achieved AUC and AUPR measures of 0.7623 and 0.7691, respectively. The drugs such as Tivozanib, SNX-2112, CGP-60474, PHA-665752, Foretinib etc., exhibited low median IC50 values and were found to be effective anti-cancer drugs. The case studies with different TCGA cancer types also revealed the effectiveness of SNX-2112, CGP-60474, Foretinib, Cisplatin, Vinblastine etc. This consistent pattern strongly suggests the effectiveness of the model in predicting CDR.

GTF2H5 Identified as a crucial synthetic lethal target to counteract chemoresistance in colorectal cancer

BackgroundSynthetic lethality (SL) emerges as a novel concept being explored to combat cancer progression and resistance to conventional therapy. Despite the efficacy of chemotherapy in select cases of colorectal cancer (CRC), a substantial proportion of patients encounter challenges, leading to an adverse prognosis of CRC patients. CRC-related SL genes offer a potential avenue for identifying therapeutic targets. MethodsCRC-related SL genes were obtained from the SynLethDB database. The bulk RNA sequencing data, mutation data, and clinical information for treated and untreated CRC patients were enrolled from the UCSC and GEO databases. The Tumor Immunology Single Cell Center database served as the repository for collecting and analyzing single-cell RNA sequencing data. The synergistic killing effect of SL genes and chemotherapeutic drugs on resistant cells was experimentally verified. ResultsIn the present study, pivotal SL genes associated with chemoresistance identified by using WGCNA and CRC patients categorized into two groups based on these genes. Variations between the groups were most pronounced in pathways associated with extracellular matrix remodeling. Further by integrating mutation data, five potential SL genes were discerned, which were highly expressed in the presence of TP53 or KRAS mutations, leading to a severely poor prognosis. Subsequent time series analysis revealed that the expression of GTF2H5 was gradually elevated at different stages of the transition from sensitive to resistant in CRC cells. Finally, it was preliminarily verified by experiments that GTF2H5 may play a key role in driving the drug-resistant transition within CRC cells. ConclusionsThe identification of SL genes that collaboratively interact with chemotherapeutic agents could provide new insights into solving the issue of chemotherapy resistance in CRC patients. And GTF2H5 wields a fundamental influence in inducing chemoresistance in CRC, which provided a potential therapeutic target for CRC.