Mutation Data Research Articles

A novel disulfidptosis-related LncRNA prognostic risk model: predicts the prognosis, tumor microenvironment and drug sensitivity in esophageal squamous cell carcinoma

BackgroundDisulfidptosis is a newly discovered type of cell death that differs from apoptosis, necrosis, ferroptosis and other death modes and is closely related to the occurrence and progression of tumors. However, the predictive potential and biological characteristics of disulfidptosis-related lncRNAs (DRGs-lncRNAs) in esophageal squamous cell carcinoma (ESCC) are unclear.MethodsRNA transcriptome data, clinical information and mutation data for ESCC patients were obtained from The Cancer Genome Atlas (TCGA) database. Pearson correlation and Cox regression analyses were used to identify the DRGs-lncRNAs associated with overall survival (OS). LASSO regression analysis was used to construct the prognostic model. A nomogram was created to predict the prognosis of patients with ESCC. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were used to identify the signaling pathways associated with the model. TIMER, CIBERSORT, ESTIMATE and other methods were used to analyze immune infiltration, immune function, immune checkpoints and drug sensitivity. The tumor mutation burden (TMB) were assessed between different risk groups. Real-time polymerase chain reaction (RT‒PCR) was used to detect the expression of DRGs-lncRNAs in ESCC cell lines.ResultsA total of 155 lncRNAs significantly associated with disulfidptosis were identified. Through univariate Cox regression analysis, LASSO regression analysis and multivariate Cox regression analysis, 9 lncRNAs with independent prognostic significance were selected, and a prognosis model was established. Survival analysis with the prognostic model revealed that there were obvious differences in survival between the high- and low-risk groups. Further analysis revealed that the immune microenvironment, immune infiltration, immune function, immune checkpoints, and drug sensitivity significantly differed between the high-risk and low-risk groups. Patients who exhibited both high risk and high tumor mutation burden (TMB) survived shorter, while those who fell into the low risk and low TMB categories survived longer. In addition, RT‒PCR analysis revealed differential expression of DRG lncRNAs between ESCC cell lines and esophageal epithelial cell lines.ConclusionsWe established a DRG-lncRNA prognostic model that can be used to predict the prognosis, tumor mutation burden, immune cell infiltration, and drug sensitivity of ECSS patients. The results of this study provide valuable insights into the understanding of ESCC and provide valuable assistance for the individualized treatment of ESCC patients.

Survival prediction and molecular subtyping of squamous cell lung cancer based on network embedding

Squamous cell lung cancer (SQCLC), which is fatal to humans, is heterogeneous with different genetic and histological features. We used SBMOI, a multi-omics data integration method from previous study, to integrate clinical, gene expression, and somatic mutation data of SQCLC to construct new patient features. Next, random survival forest (RSF) model and SimpleMKL model were constructed to predict the survival of SQCLC patients, and K-means model was constructed to perform molecular subtyping. The results of the RSF model showed that when the dimension of the patient features were 11 × 364 and the hard threshold was 0.2, we obtained the best results, and the AUC value of the 1-year time-dependent ROC curve was 0.706. The SimpleMKL model, constructed using the same patient features, performed exceptionally well, with 1-year, 5-year, and 10-year survival prediction AUC values of 0.944, 0.947 and 0.950, respectively. We used K-means analysis to identify three SQCLC molecular subtypes with significant survival differences. The patient features constructed by SBMOI were used to effectively predict the survival and molecular subtyping of SQCLC patients. In addition, our study further confirmed the effectiveness in multi-omics data integration task and broad applicability of SBMOI.

PartIES: a disease subtyping framework with Partition-level Integration using diffusion-Enhanced Similarities from multi-omics Data.

Integrating multi-omics data helps identify disease subtypes. Many similarity-based methods were developed for disease subtyping using multi-omics data, with many of them focusing on extracting common clustering structures across multiple types of omics data, but not preserving data-type-specific clustering structures. Moreover, clustering performance of similarity-based methods is affected when similarity measures are noisy. Here we proposed PartIES, a Partition-level Integration using diffusion-Enhanced Similarities to perform disease subtyping using multi-omics data. PartIES uses diffusion to reduce noises in individual similarity/kernel matrices from individual omics data types first, and then extract partition information from diffusion-enhanced similarity matrices and integrate the partition-level similarity through a weighted average iteratively. Simulation studies showed that (1) the diffusion step enhances clustering accuracy, and (2) PartIES outperforms competing methods, particularly when omics data types provide different clustering structures. Using mRNA, long noncoding RNAs, microRNAs expression data, DNA methylation data, and somatic mutation data from The Cancer Genome Atlas project, PartIES identified subtypes in bladder urothelial carcinoma, liver hepatocellular carcinoma, and thyroid carcinoma that are most significantly associated with patient survival across all methods. Further investigations suggested that among subtype-associated genes, many of those that are highly interacting with other genes are known important cancer genes. The identified cancer subtypes also have different activity levels for some known cancer-related pathways. The R code can be accessed at https://github.com/yuqimiao/PartIES.git.

GLN2 as a key biomarker and therapeutic target: evidence from a comprehensive pan-cancer study using molecular, functional, and bioinformatic analyses

BackgroundGNL2, a nuclear protein, is involved in ribosome production and cell cycle regulation. However, its expression and function in different types of tumors are not well understood. Comprehensive studies across multiple cancer types are needed to assess the potential of GNL2 as a diagnostic, prognostic, and immunological marker.MethodsmRNA expression data, copy number alteration threshold data, masked copy number segmentation data, and DNA methylation 450 K data from The Cancer Genome Atlas (TCGA) pan-cancer cohort were obtained from the Firehose database. Additional data, including miRNA, The Cancer Proteome Atlas (TCPA), mutation data, and clinical information, were sourced from the University of California Santa Cruz (UCSC) Xena database. The cBioPortal database facilitates the examination of GNL2 mutation frequency, location, and 3D structure in the TCGA database. Gene Expression Omnibus (GEO) data verified the transcriptome level expression in the TCGA cohort. Protein expression levels were analyzed via the Human Protein Atlas (HPA) database and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Gene set enrichment analysis (GSEA) was employed to investigate the biological role of GNL2 across cancers. Multiple immune infiltration algorithms from the TIMER2.0 database were utilized to examine the correlation between GNL2 expression and the tumor immune microenvironment. The transcriptome-wide immune infiltration results were validated using 72 single-cell datasets from the Tumor Immune Single-cell Hub (TISCH) database. Pan-cancer survival maps were constructed, and GNL2 expression in different molecular subtypes across cancers was examined. The relationship between GNL2 and drug resistance was investigated using data from CellMiner, GDSC, and CTRP. The Comparative Toxicogenomics Database (CTD) was used to identify chemicals affecting GNL2 expression.ResultsGNL2 is located primarily in the nucleus, and its expression is regulated mainly through somatic copy number alteration (SCNA) and aberrant DNA methylation, according to TCGA data. Database analysis and immunohistochemical results from clinical samples revealed high GNL2 expression in most tumors, which was correlated with diagnostic significance. High GNL2 expression often indicates a poor prognosis with pan-cancer prognostic value. Gene set enrichment analysis (GSEA) suggested that GNL2 is involved in tumor development through cell proliferation-related pathways. GNL2 expression is correlated with the expression of immune-related genes and the infiltration levels of multiple immune cells. The relationships between GNL2 and various drugs and chemicals were examined, revealing its influence on drug sensitivity and identifying five chemicals countering GNL2-mediated pro-cancer effects.ConclusionComprehensive bioinformatics analysis of GNL2 in pan-cancer tissues, combined with experimental validation, elucidated the pan-cancer expression pattern of GNL2, determined its diagnostic and prognostic value, and explored the biological functions of GNL2. GNL2 may be involved in the regulation of cell cycle progression and remodeling of the tumor microenvironment and is associated with poor prognosis as a risk factor in most tumors. The potential of GNL2-based cancer therapies is emphasized, assisting in predicting the response to chemotherapy.

Analysis of shared variants between cancer biospecimens.

Mutational data from multiple solid and liquid biospecimens of a single patient is often integrated to track cancer evolution. However, there is no accepted framework to resolve if individual samples from the same individual share variants due to common identity versus coincidence. Utilizing 8,000 patient tumors from The Cancer Genome Atlas (TCGA) across 33 cancer types, we estimated background rates of co-occurrence rates of mutations between discrete pairs of samples across cancers and by cancer type. We developed a mutational profile similarity score (MPS) that uses a large background database to produce confidence estimates that two tumors share a unique, related molecular profile. The MPS algorithm was applied to randomly paired tumor profiles, including patients who underwent repeat solid tumor biopsies sequenced with MSK-IMPACT (n=53,113). We also evaluated the MPS in sample pairs from single patients with multiple cancers (n=2,012), as well as patients with plasma and solid-tumor variant profiles (n=884 patients). In unrelated tumors, nucleotide-specific variants are shared in 1.3% (cancer-type agnostic) and in 10-13% (cancer-type specific) of cases. The mutational profile similarity (MPS) method contextualized shared variants to specify whether patients had a single cancer versus multiple distinct cancers. When multiple tumors were compared from the same patient, and an initial clinicopathologic diagnosis was discordant with molecular findings, the MPS anticipated future diagnosis changes in 28% of examined cases. Use of a novel shared variant framework can provide information to clarify the molecular relationship between compared biospecimens with minimal required input.

Network-based meta-analysis of gene expression reveals novel prognostic biomarkers for the progression of hepatocellular carcinoma from non-alcoholic fatty liver disease

Liver steatosis, also known as non-alcoholic fatty liver disease (NAFLD), is a condition marked by the buildup of fat in the liver. It is frequently linked to obesity, diabetes, and other risk factors such as hypertension, dyslipidemia, and a sedentary lifestyle. It has the potential to progress to non-alcoholic steatohepatitis (NASH), a condition characterized by liver inflammation and fibrosis. Without intervention, NASH can progress, eventually resulting in cirrhosis and, ultimately, hepatocellular carcinoma (HCC). Gaining a greater understanding of the molecular pathways that drive the progression of the disease could facilitate the development of more effective prognostic and monitoring tools. This study utilized a network-based methodology to do a meta-analysis on a large set of gene expression data from three studies, following the guidelines outlined by PRISMA. A study network was created, and differential gene expression (DGE) analysis was conducted to compare disease states. The common differentially expressed genes (DEGs) were identified, and mutual information network analysis unveiled associations among genes such as COL1A1, COL1A2, C8B, and AAMP across several stages. Further, GO and KEGG analyses identified 23 genes, and 21 pathways linked to fatty acid metabolism, inflammation, insulin signaling, cell cycle regulation, growth, apoptosis, and angiogenesis. Furthermore, the Gene Set Enrichment Analysis (GSEA) revealed that there were enriched transcriptional events in pathways such as Nucleotide Excision Repair, Type 2 Diabetes Mellitus, Cell Cycle, and Apoptosis. Nine prognostic genes, namely PGM2L1, ADA, INF2, COL1A1, RPL18A, SLC25A6, SLC39A7, TXN, and ALDH1A1, were identified using the Kaplan-Meier technique in survival analysis. The survival prediction probability was evaluated using regularization regression approaches- LASSO, Ridge, and Elastic-Net. The analysis of the tissue-specific expression data revealed notable expression of SLC39A7, SLC25A6, ALDH1A1, and INF2 in liver hepatocytes. The mutational data indicates alterations in the COL1A1, SLC39A1, INF2, RPL18A, and SLC25A6 genes in cases of hepatocellular carcinoma.

Pan-cancer analysis of STAT3 indicates its potential prognostic value and correlation with immune cell infiltration in prostate cancer

BackgroundTargeting the STAT3 signaling pathway is a promising therapeutic approach for cancer patients. However, the association between STAT3 expression, the tumor immune microenvironment, and genetic variation remains unclear across human cancers, especially prostate cancer.MethodsWe used R software and other tools to analyze pan-cancer and mutation data from publicly available databases statistically. A comprehensive investigation was performed to assess the genetic heterogeneity and clinical relevance of STAT3 in various malignancies, with a specific focus on its role in the immune landscape and prognostic significance in prostate cancer. The findings were validated through immunohistochemistry (IHC) and multiplex immunofluorescence (mIF).ResultsSTAT3 expression is abnormal in the majority of cancer tissues, which is strongly correlated with these patients' prognosis. Eight measures of tumor heterogeneity and six measures of tumor stemness of multiple tumor types showed a strong correlation with STAT3 expression. Furthermore, in individuals with prostate cancer, STAT3 expression indicated the degree of immune cell infiltration and the advancement of the disease. IHC analysis revealed that STAT3 was down-regulated in prostate tumor tissues, while mIF analysis demonstrated that STAT3 signaling (p-STAT3) was extensively active in tumor tissues and positive lymph node tissues.ConclusionSTAT3 may serve as a valuable prognostic biomarker and therapeutic target across various cancers, with particular relevance to prostate cancer.

PATH-04. THE PROGNOSTIC IMPACT OF CDKN2A/B HETEROZYGOUS DELETIONS IN MENINGIOMA- INSIGHTS OF A MULTICENTER ANALYSIS

Abstract BACKGROUND Meningiomas are one of the most common primary brain tumors. Homozygous deletions of CDKN2A/B have reliably been shown to confer an increased risk of progression. However, the role of heterozygous CDKN2A/B deletions remains less clear. MATERIAL AND METHODS DNA methylation data, copy-number information and mutation data were generated on a multicenter cohort of meningiomas for a total of 970 samples. The CDKN2A/B locus was determined manually for each individual case in relation to whole chromosomal or larger segmental losses and gains in the chromosomal profile. Survival probabilities were assessed using the Kaplan–Meier method. RESULTS A total of 970 meningioma cases were identified with 30 homozygous and 114 heterozygous CDKN2A/B deletion cases, 826 cases had balanced CDKN2A/B. Heterozygous CDKN2A/B deletions in general did not confer an increased risk of progression compared to CDKN2A/B balanced cases (p= 0.074). However, segmental heterozygous losses of 9p, encompassing CDKN2A/B, yielded a significantly worse prognosis when compared to meningiomas CDKN2A/B-balanced (p= 0.002) and in contrast to focal heterozygous deletions (p= 0.523). Segmental heterozygous 9p deletions encompassing CDKN2A/B were significantly more frequently associated with a higher amount of copy number variations (CNVs) than focal losses. CONCLUSION Segmental heterozygous CDKN2A/B deletions do confer an increased risk of progression, but focal heterozygous CDKN2A/B deletions do not. The signal for segmental deletions may not be locus-specific but just one representation of the generally instable genome of aggressive meningiomas.

Clinicopathological Analysis of a European Cohort of MYOD1 Mutant Rhabdomyosarcomas in Children and Young Adults.

Patients with PAX3/7-FOXO1 fusion-negative rhabdomyosarcomas (fnRMS) harbouring the rare L122R MYOD1 mutation have significantly poorer prognosis than other fnRMS. We undertook a detailed clinicopathological evaluation of a cohort of patients with MYOD1 mutated fnRMS in order to improve risk stratification and treatment options. Histological, mutational and clinical data from a cohort of patients with MYOD1 mutant RMS treated in Europe were analysed. Thirty-two cases with mutant MYOD1 RMS were identified from patients enrolled in sequential European rhabdomyosarcoma clinical trials from 1992 to 2022 (n = 22) and non-trial cohorts (n = 10). Thirty cases had the recurrent L122R missense mutation, one case harboured a K124E mutation and one case had a truncating mutation (S63X). Increased MyoD1 and reduced MYF4 immunostaining were consistent features of MYOD1L122R-mutated RMS. Applying the risk stratification of the European paediatric Soft tissue sarcoma Study Group (EpSSG) RMS2005 trial, among 20 localised RMS cases that could be assigned a risk category, one was Very High Risk, 13 were High Risk and six were Standard Risk. Eight patients had distant metastases at diagnosis. Of the 25 patients with adequate clinical follow-up data, 15/25 (60%) patients had an event at a median time of 9months (12/15 included failure of local control) and 13/25 (52%) died of disease. This MYOD1 mutant cohort demonstrates increased MYOD and reduced MYF4 immunostaining, high risk of local failure and poor survival in agreement with other studies. Increased treatment intensity and improved local control should be considered for these patients.

Breast cancer genomic analyses reveal genes, mutations, and signaling networks.

Breast cancer (BC) is the most commonly diagnosed cancer and the predominant cause of death in women. BC is a complex disorder, and the exploration of several types of BC omic data, highlighting genes, perturbations, signaling and cellular mechanisms, is needed. We collected mutational data from 9,555 BC samples using cBioPortal. We classified 1174 BC genes (mutated ≥ 40 samples) into five tiers (BCtier_I-V) and subjected them to pathway and protein‒protein network analyses using EnrichR and STRING 11, respectively. BCtier_I possesses 12 BC genes with mutational frequencies > 5%, with only 5 genes possessing > 10% frequencies, namely, PIK3CA (35.7%), TP53 (34.3%), GATA3 (11.5%), CDH1 (11.4%) and MUC16 (11%), and the next seven BC genes are KMT2C (8.8%), TTN (8%), MAP3K1 (8%), SYNE1 (7.2%), AHNAK2 (7%), USH2A (5.5%), and RYR2 (5.4%). Our pathway analyses revealed that the five top BC pathways were the PI3K-AKT, TP53, NOTCH, HIPPO, and RAS pathways. We found that BC panels share only seven genes. These findings show that BC arises from genetic disruptions evident in BC signaling and protein networks.

Understanding and Predicting Ligand Efficacy in the μ-Opioid Receptor through Quantitative Dynamical Analysis of Complex Structures.

The μ-opioid receptor (MOR) is a G-protein coupled receptor involved in nociception and the primary target of opioid drugs. Understanding the relationships among the ligand structure, receptor dynamics, and efficacy in activating MOR is crucial for drug discovery and development. Here, we use coarse-grained normal-mode analysis to predict ligand-induced changes in receptor dynamics with the Quantitative Dynamics Activity Relationship (QDAR) DynaSig-ML methodology, training a LASSO regression model on the entropic signatures (ESs) computed from ligand-receptor complexes. We train and validate the methodology using a data set of 179 MOR ligands with experimentally measured efficacies split into strictly chemically different cross-validation sets. By analyzing the coefficients of the ES LASSO model, we identified key residues involved in MOR activation, several of which have mutational data supporting their role in MOR activation. Additionally, we explored a contact-only LASSO model based on ligand-protein interactions. While the model showed predictive power, it failed at predicting efficacy for ligands with low structural similarity to the training set, emphasizing the importance of receptor dynamics for predicting ligand-induced receptor activation. Moreover, the low computational cost of our approach, at 3 CPU s per ligand-receptor complex, opens the door to its application in large-scale virtual screening contexts. Our work contributes to a better understanding of dynamics-function relationships in the μ-opioid receptor and provides a framework for predicting ligand efficacy based on ligand-induced changes in receptor dynamics.

Identification of oxidative stress-related hub genes for predicting prognosis in diffuse large B-cell lymphoma

BackgroundOxidative stress is a cellular characteristic that might induce the proliferation and differentiation of tumor cells and promote tumor progression in diffuse large B-cell lymphoma (DLBCL). MethodsThe DLBCL gene sequencing dataset, tumor mutation burden data, copy number variation data of Somatic cell mutation data in TCGA were downloaded for data training analysis, along with four DLBCL datasets in GEO for validation analysis. The known oxidative stress related genes (OSRGs) were collected from websites. The weighted gene co-expression network analysis (WGCNA) was conducted on the TCGA DLBCL dataset to obtain gene modules related to oxidative stress and intersected with the known OSRGs to obtain the hub genes, which were used to perform consensus clustering on the samples to obtain new phenotypes. Next, the prognosis related OSRGs were selected through regression analysis algorithms and key genes were identified. These genes were used to establish the prognostic risk model and predictive model, and to compare functional and pathway differences among different risk groups. ResultsThrough website search, we obtained 297 known OSRGs, and after intersecting with WGCNA results, we obtained 26 OSRGs. The TCGA-DLBC samples were clustered into 2 subtypes with these genes and there were significant differences in immune infiltration between subtypes. After regression analysis, we obtained a total of four key genes, BMI1, CDKN1A, NOX1, and SESN1. The risk prediction model established with these four genes as variables has accurate prognostic prediction ability. The key genes interact with 65 miRNAs, 57 TFs, 47 RBPs, and 62 drugs, respectively, and are closely related to immune infiltration of the disease. Among them, CDKN1A and SESN1 had the highest variability. ConclusionsThe key genes involved in oxidative stress could predict the prognosis of DLBCL and potentially become therapeutic targets.

StSNV: a comprehensive resource of SNVs in spatial transcriptome.

Single nucleotide variants (SNVs), as important componentsof genetic variation, affect gene expression, function and phenotype. Mining and summarizing the spatial distribution of SNVs in diseased and normal tissues for a better understanding of their characteristics and potential roles in cell-lineage determination, aging, or disease occurrence is significant. Herein, we have developed a comprehensive spatial mutation resource stSNV (http://bio-bigdata.hrbmu.edu.cn/stSNV/index.jsp), which provides an atlas of spatial SNVs in major diseased and normal tissues of human and mouse. stSNV documents 42202 spatial mutated genes involving 898908 SNVs called from 730067 spots within 450 slices from 19 diseased and 28 normal tissues. Importantly, potential characteristics of SNVs are explored and provided by analyzing the perturbation of the SNVs to gene expression, spatial communication,biological function, region-specific mutated genes, spatial mutant signatures, SNV-cell co-localizationand mutation core region. All these spatial mutation data and in-depth analyses have been integrated into a user-friendly interface, visualized through intuitive tables and various image formats. Flexible tools are developed to explore co-localization among clusters, genes, cell types and SNVs in the same slice. In summary, stSNV as a valuable resourcehelps to dissect intra-tissue genetic heterogeneity and lays the groundwork for understanding the SNVs' biological regulatory mechanisms.

Prognosis prediction of head and neck squamous cell carcinoma through the basement membrane-related lncRNA risk model.

Head and neck squamous cell carcinoma (HNSCC) ranks among the most widespread and significantly heterogeneous malignant tumors globally. Increasing evidence suggests that the basement membrane (BM) and associated long non-coding RNAs (lncRNA) are correlated with the onset of HNSCC and its prognosis. Our study aims to construct a basement membrane-associated lncRNAs (BMlncRNAs) marker to accurately predict the prognosis of HNSCC patients and find novel immunotherapy targets. The Cancer Genome Atlas (TCGA) database was accessed to acquire the transcriptome expression matrices, somatic mutation data, and clinical follow-up data of HNSCC patients. Utilizing co-expression analysis, the BMlncRNAs were identified and the differentially expressed lncRNAs (DEBMlncRNA) were then filtered, The filtering thresholds are FDR<0.05 and |log2FC|≥1. Furthermore, univariate analysis, least absolute shrinkage and selection operator (LASSO), and multivariable Cox regression were utilized to develop the risk model. The model then underwent thorough evaluation across diverse perspectives, encompassing tumor immune infiltration, tumor mutation burden (TMB), functional enrichment, and chemotherapy sensitivity. The risk assessment model consists of 14 BMlncRNA pairs. The acquired data is indicative of the reliability of the risk score in its capacity as a prognostic factor. Individuals at high risk exhibited a poorer prognosis, and a statistically significant variance was noted in TMB and tumor immune infiltration compared to the low-risk group. Additionally, heightened sensitivity to paclitaxel and docetaxel was evident in the patients at high risk. We have established a BMLncRNA-based prognostic model that can provide clinical guidance for future laboratory and clinical studies of HNSCC.

Revealing SARS-CoV-2 Mpro mutation cold and hot spots: Dynamic residue network analysis meets machine learning

Deciphering the effect of evolutionary mutations of viruses and predicting future mutations is crucial for designing long-lasting and effective drugs. While understanding the impact of current mutations on protein drug targets is feasible, predicting future mutations due to natural evolution of viruses and environmental pressures remains challenging. Here, we leveraged existing mutation data during the evolution of the SARS-CoV-2 protein drug target main protease (Mpro) to test the predictive power of dynamic residue network (DRN) analysis in identifying mutation cold and hot spots. We conducted molecular dynamics simulations on the Mpro of SARS-CoV-2 (Wuhan strain) and calculated eight DRN metrics (averaged BC, CC, DC, EC, ECC, KC, L, PR), each of which identifies a unique network feature within the protein. The sets of residues with the highest and lowest values for each metric, comprising potential cold and hot spots, were compared to published biochemical analyses and per residue mutation frequencies observed across five SARS-CoV-2 lineages, encompassing a total of 191,878 sequences. Individual DRN metrics displayed only modest power to predict the mutation frequency of individual residues. However, integrating the eight DRN metrics with additional structural and sequence-derived metrics allowed us to develop machine learning models which significantly improved the prediction of residue mutation frequency. While further refinements should enhance accuracy, we demonstrated a robust method to understand pathogen evolution. This approach can also guide the development of long-lasting drugs by targeting functional residues located in and near active site, and allosteric sites, that are less prone to mutations.

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.

Integrative analysis of single-cell and bulk multi-omics data to reveal subtype-specific characteristics and therapeutic strategies in clear cell renal cell carcinoma patients.

Background: Kidney renal clear cell carcinoma (KIRC) is the most prevalent subtype of malignant renal cell carcinoma and is well known as a common genitourinary cancer. Stratifying tumors based on heterogeneity is essential for better treatment options. Methods: In this study, consensus clusters were constructed based on gene expression, DNA methylation, and gene mutation data, which were combined with multiple clustering algorithms. After identifying two heterogeneous subtypes, we analyzed the molecular characteristics, immunotherapy response, and drug sensitivity differences of each subtype. And we further integrated bulk data and single-cell RNA sequencing (scRNA-Seq) data to infer the immune cell composition and malignant tumor cell proportion of subtype-related cell subpopulations. Results: Among the two identified consensus subtypes (CS1 and CS2), CS1 was enriched in more inflammation-related and oncogenic pathways than CS2. Simultaneously, CS1 showed a worse prognosis and we found more copy number variations and BAP1 mutations in CS1. Although CS1 had a high immune infiltration score, it exhibited high expression of suppressive immune features. Based on the prediction of immunotherapy and drug sensitivity, we inferred that CS1 may respond poorly to immunotherapy and be less sensitive to targeted drugs. The analysis of bulk data integrated with single-cell data further reflected the high expression of inhibitory immune features in CS1 and the high proportion of malignant tumor cells. And CS2 contained a large number of plasmacytoid B cells, presenting an activated immune microenvironment. Finally, the robustness of our subtypes was successfully validated in four external datasets. Conclusion: In summary, we conducted a comprehensive analysis of multi-omics data with 10 clustering algorithms to reveal the molecular characteristics of KIRC patients and validated the relevant conclusions by single-cell analysis and external data. Our findings discovered new KIRC subtypes and may further guide personalized and precision treatments.

Treatment Strategies' Impact on Progression-Free Survival According to RMST Function in Metastatic Colorectal Cancer Patients: A Retrospective Study from Romania.

Background: This retrospective study investigates the impact of various treatment strategies on progression-free survival (PFS) in patients with metastatic colorectal cancer (mCRC), a significant global health issue. Methods: We employed the restricted mean survival time (RMST) to evaluate how different treatments affect PFS over a defined period. The study included 225 patients with mCRC who were treated between 2015 and 2023 at the Oncology Department of Colțea Clinical Hospital in Bucharest. To assign KRAS status, mutation data from exons 2, 3, and 4 of the KRAS gene were required. Eligibility criteria included a confirmed histopathological diagnosis of colorectal adenocarcinoma, a valid RAS mutation test from a solid biopsy, radiological confirmation of stage IV disease by computed tomography, and at least one line of systemic treatment in the metastatic setting. Results: Our analysis revealed a small difference in PFS based on KRAS status, but this difference was not statistically significant. Neither sex nor the urban versus rural environment impacted PFS; however, the data indicated that educational level affected survival outcomes. Conclusions: Consistent with existing literature, our findings showed no survival benefit from locoregional treatments such as surgery of the primary tumor or curative radiotherapy at diagnosis. In contrast, resection of hepatic metastases was associated with improved survival outcomes.

A maternal germline mutator phenotype in a family affected by heritable colorectal cancer.

Variation in DNA repair genes can increase cancer risk by elevating the rate of oncogenic mutation. Defects in one such gene, MUTYH, are known to elevate the incidence of colorectal cancer in a recessive Mendelian manner. Recent evidence has also linked MUTYH to a mutator phenotype affecting normal somatic cells as well as the female germline. Here, we use whole genome sequencing to measure germline de novo mutation rates in a large extended family containing both mothers and fathers who are affected by pathogenic MUTYH variation. By developing novel methodology that uses siblings as "surrogate parents" to identify de novo mutations, we were able to include mutation data from several children whose parents were unavailable for sequencing. In the children of mothers affected by the pathogenic MUTYH genotype p.Y179C/V234M, we identify an elevation of the C>A mutation rate that is weaker than mutator effects previously reported to be caused by other pathogenic MUTYH genotypes, suggesting that mutation rates in normal tissues may be useful for classifying cancer-associated variation along a continuum of severity. Surprisingly, we detect no significant elevation of the C>A mutation rate in children born to a father with the same MUTYH genotype, and we similarly find that the mutator effect of the mouse homolog Mutyh appears to be localized to embryonic development, not the spermatocytes. Our results suggest that maternal MUTYH variants can cause germline mutations by attenuating the repair of oxidative DNA damage in the early embryo.

Exploring the role of ADAMTSL2 across multiple cancer types: A pan-cancer analysis and validated in colorectal cancer

BackgroundRecent studies have established a correlation between ADAMTSL2 (ADAMTS-like 2) and the development of various cancers. This study aims to conduct a comprehensive pan-cancer analysis in 37 cancer types and investigate its potential role in colon and rectal adenocarcinoma (COADREAD).MethodPan-cancer and mutation data were sourced from The Cancer Genome Atlas (TCGA) database and analyzed using Sangerbox analysis platform. We explored the expression patterns and prognostic implications of ADAMTSL2, and investigated its relationships with tumor heterogeneity, stemness, immune checkpoint genes, immune cell infiltration, RNA modifications, and mutational profiles across different cancers. Additionally, with Ethics Committee approval, we conducted immunohistochemical (IHC) analysis on 120 COADEAD samples to evaluate ADAMTSL2 expression and its association with clinicopathological parameters.ResultsADAMTSL2 expression was positively correlated with the hazard ratio of OS, DSS, DFI and PFI for ESCA and COADREAD. A negative correlation was observed between ADAMTSL2 expression and NEO levels in COAD. Gene alterations in ADAMTSL2 were observed, with a mutation frequency of 5.0% in COAD. There is a significant correlation between ADAMTSL2 expression and immune cell infiltration in a variety of cancers. The expression level of ADAMTSL2 protein was associated with T stage, N stage, M stage (p < 0.05). Kaplan‒Meier survival curves demonstrated that the high ADAMTSL2 group had a shorter OS time (p = 0.047) and progression free survival time (p = 0.026) than the low ADAMTSL2 group.ConclusionIn summary, we conducted a comprehensive pan-cancer analysis of ADAMTSL2 and we demonstrated that ADAMTSL2 may serve as a novel prognostic biomarker and immunotherapy target in COADREAD.