Copy Number Variation Data Research Articles

Development and Validation of a Prognostic Model based on 11 E3-related Genes for Colon Cancer Patients.

Colon cancer is a common tumor in the gastrointestinal tract with a poor prognosis. According to research reports, ubiquitin-dependent modification systems have been found to play a crucial role in the development and advancement of different types of malignant tumors, including colon cancer. However, further investigation is required to fully understand the mechanism of ubiquitination in colon cancer. We collected the RNA expression matrix of the E3 ubiquitin ligase-related genes (E3RGs) from the patients with colon adenocarcinoma (COAD) using The Cancer Genome Atlas program (TCGA). The "limma" package was used to obtain differentially expressed E3RGs between COAD and adjacent normal tissues. Then, univariate COX regression and least absolute shrinkage and selection operator (LASSO) analysis were performed to construct the prognostic signature and nomogram model. Afterward, we used the original copy number variation data of COAD to find potential somatic mutation and employed the "pRRophetic" package to investigate the disparity in the effectiveness of chemotherapy drugs between high and low-risk groups. The RT-qPCR was also implied to detect mRNA expression levels in tumor tissues. A total of 137 differentially expressed E3RG3 were screened and 11 genes (CORO2B, KCTD9, RNF32, BACH2, RBCK1, DPH7, WDR78, UCHL1, TRIM58, WDR72, and ZBTB18) were identified for the construction of prognostic signatures. The Kaplan-Meier curve showed a worse prognosis for patients with high risk both in the training and test cohorts (P = 1.037e-05, P = 5.704e-03), and the area under the curve (AUC) was 0.728 and 0.892 in the training and test cohorts, respectively. Based on the stratified analysis, this 11- E3RGs signature was a novel and attractive prognostic model independent of several clinicopathological parameters (age, sex, stage, TNM) in COAD. The DEGs were subjected to GO and KEGG analysis, which identified pathways associated with cancer progression. These pathways included the cAMP signaling pathway, calcium signaling pathway, Wnt signaling pathway, signaling pathways regulating stem cell pluripotency, and proteoglycans in cancer. Additionally, immune infiltration analysis revealed significant differences in the infiltration of macrophages M0, T cells follicular helper, and plasma cells between the two groups. We developed a novel independent risk model consisting of 11 E3RGs and verified the effectiveness of this model in test cohorts, providing important insights into survival prediction in COAD and several promising targets for COAD therapy.

Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival.

Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease's complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learning methods. Leveraging multi-omics data, this study seeks to enhance the accuracy of survival prediction by proposing new feature extraction techniques combined with unbiased feature selection. Two lung adenocarcinoma multi-omics datasets, originating from the TCGA and CPTAC-3 projects, were employed for this purpose, emphasizing gene expression, methylation, and mutations as the most relevant data sources that provide features for the survival prediction models. Additionally, gene set aggregation was shown to be the most effective feature extraction method for mutation and copy number variation data. Using the TCGA dataset, we identified 32 molecular features that allowed the construction of a 2-year survival prediction model with an AUC of 0.839. The selected features were additionally tested on an independent CPTAC-3 dataset, achieving an AUC of 0.815 in nested cross-validation, which confirmed the robustness of the identified features.

Significance of Aneuploidy in Predicting Prognosis and Treatment Response of Uveal Melanoma.

This study aimed to improve personalized treatment strategies and predict survival outcomes for patients with uveal melanoma (UM). Copy number aberrations (CNAs) have been considered as a main feature of metastatic UM. This study was designed to explore the feasibility of using copy number variation (CNV) in UM classification, prognosis stratification and treatment response. The CNV data in the TCGA-UVM cohort were used to classify the samples. The differentially expressed genes (DEGs) between subtypes were screened by the "Limma" package. The module and hub genes related to aneuploidy score were identified by performing weighted gene co-expression network analysis (WGCNA) on the DEGs. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to train the hub genes for developing a prognosis model for UM. Finally, the expression levels of the screened prognostic key genes were verified in UM cells, and the cell migration and invasion abilities were detected using real-time quantitative PCR (qRT-PCR) and transwell assay. The UM samples were divided into 3 CNV subtypes, which differed significantly in overall survival (OS) and disease-specific survival (DSS). C1 had the shortest OS and DSS and the highest level of immune infiltration. A total of 2036 DEGs were obtained from the three subtypes. Eighty hub genes with the closest correlation with aneuploidy scores were selected by WGCNA. Univariate Cox and LASSO regression-based analyses finally determined eight genes as the key prognostic genes, including HES6, RNASEH2C, NQO1, NUDT14, TTYH3, GJC1, FKBP10, and MRPL24. A prognostic model was developed using the eight genes, demonstrating a strong prediction power. Differences in the response to immunotherapy among patients in different risk groups were significant. We found that high-risk patients were more sensitive to two drugs (Palbociclib_ 1054 and Ribociclib_1632), while low-risk patients were more sensitive to AZD1208_1449, ERK_2440_1713, Mirin_1048, and Selumetinib_1736. UM in this study was divided into three CNV subtypes, and a model based on eight aneuploidy score-related genes was established to evaluate the prognosis and drug treatment efficacy of UM patients. The current results may have the potential to help the clinical decision-making process for UM management.

Targeted Sequencing of Human Satellite 2 Repeat Sequences in Plasma cfDNA Reveals Potential Breast Cancer Biomarkers.

Liquid biopsies are revolutionizing the detection and management of malignant diseases. While repetitive DNA sequences, such as LINE-1 and ALU are established in cell-free DNA (cfDNA) research, their clinical applications remain limited. In this study, we explore human satellite 2 (HSATII), a prevalent repeat DNA sequence in plasma that exhibits increased levels in cancer patients, thereby positioning it as a potential pan-cancer biomarker. We employed targeted sequencing and copy number variation (CNV) analysis using two primer pairs to assess the differential abundance of HSATII sequences in the plasma of breast cancer patients compared to healthy individuals. PCR amplicons of HSATII from 10 patients and 10 control subjects were sequenced, generating 151 bp paired-end reads. By constructing a pooled reference dataset, HSATII copy ratios were estimated in the patients. Our analysis revealed several significant CNVs in HSATII, with certain sequences displaying notable gains and losses across all breast cancer patients, suggesting their potential as biomarkers. However, we observed pronounced fragmentation of cfDNA in cancer, leading to the loss of longer PCR amplicons (>180 bp). While not all observed losses can be attributed to fragmentation artifacts, this phenomenon does introduce complexity in interpreting CNV data. Notably, this research marks the first instance of targeted HSATII sequencing in a liquid biopsy context. Our findings lay the groundwork for developing sequencing-based assays to detect differentially represented HSATII sequences, potentially advancing the field of minimally-invasive cancer screening.

Integrative analysis of cuproptosis-related lncRNAs: Unveiling prognostic significance, immune microenvironment, and copper-induced mechanisms in prostate cancer

BackgroundLong non-coding ribonucleic acids (lncRNAs) regulate messenger RNA (mRNA) expression and influence cancer development and progression. Cuproptosis, a newly discovered form of cell death, plays an important role in cancer. Nonetheless, additional research investigating the association between cuproptosis-related lncRNAs and prostate cancer (PCa) prognosis is required. MethodsSequencing data and copy number variant data were obtained from 492 patients with PCa from The Cancer Genome Atlas (TCGA) Program. Prognostic models of PCa based on cuproptosis-related lncRNAs were constructed using a multi-level attention graph neural network (MLA-GNN) deep learning algorithm. Immune escape scoring was performed using Tumor Immune Dysfunction and Exclusion. Cellular experiments were conducted to explore the correlation between key lncRNAs and cuproptosis. ResultsData from 492 patients with PCa were randomized into two groups at a 1:1 ratio. Prognostic modeling was successfully established using MLA-GNN. Survival analysis suggested that patients could be divided into high- and low-risk groups according to model scores and that there was a significant difference in disease-free survival (DFS) (P < 0.01). The area under the receiver operating characteristic (ROC) curve (AUC) indicated a strong predictive performance for the model, with AUCs of 0.913, 0.847, and 0.863 for the training group and 0.815, 0.907, and 0.866 for the test group at 12, 36, and 60 months, respectively. The immune escape score and immune microenvironment analysis suggested that the high-risk group corresponded to a stronger immune escape and a poorer immune microenvironment (P < 0.05). Cellular experiments revealed that the expression of all six key lncRNAs was upregulated in the presence of copper ion carriers (P < 0.05). ConclusionsThis study identified cuproptosis-related lncRNAs that were strongly associated with PCa prognosis. Key lncRNAs could affect copper metabolism and may serve as new therapeutic targets.

Prevalence of CYP2D6 structural variation in large retrospective study.

CYP2D6 is a highly polymorphic gene with clinically important structural variations. Commonly, only exon 9 is assayed on clinical pharmacogenomics panels, as it allows for accurate functional characterization even in the presence of a CYP2D6::CYP2D7 conversion. However, this method does not capture CYP2D7::CYP2D6 (CYP2D6*13) conversions, possibly leading to inaccurate phenotype assignment. The study's purpose was to determine the frequency of structural variations in CYP2D6 utilizing multiple copy number variation (CNV) assay locations to quantify the potential impact on clinical phenotype classification. A retrospective analysis was conducted of de-identified pharmacogenomics data submitted through the Translational Software, Inc. platform. Samples with CYP2D6 CNV data for exon 9 and at least one additional CNV location (5'UTR, exon 1, intron 2, exon 5 or intron 6) were included. CYP2D7::CYP2D6 and CYP2D6::CYP2D7 conversions were classified according to PharmVar nomenclature. The CYP2D6 copies were capped at four total copies to account for assay limitations in detecting more than four copies. A total of 106,474 samples were included for analysis. CYP2D7::CYP2D6 conversions were present in approximately 2.44% of samples, and 5.84% of samples had CYP2D6::CYP2D7 conversions. Many samples did not have a CYP2D7 conversion detected (91.5%; 97,462/106,474). A full gene deletion was detected in 0.15%, and 5.98% had a duplication or multiplication present. This retrospective study underscores the importance of testing more than one CNV site for CYP2D6 . Over 2% of patients were found to have a CYP2D7::CYP2D6 conversion. This translates into potentially misclassified phenotype classification and incongruent clinical recommendations.

Control-FREEC viewer: a tool for the visualization and exploration of copy number variation data

BackgroundCopy number alterations (CNAs) are genetic changes commonly found in cancer that involve different regions of the genome and impact cancer progression by affecting gene expression and genomic stability. Computational techniques can analyze copy number data obtained from high-throughput sequencing platforms, and various tools visualize and analyze CNAs in cancer genomes, providing insights into genetic mechanisms driving cancer development and progression. However, tools for visualizing copy number data in cancer research have some limitations. In fact, they can be complex to use and require expertise in bioinformatics or computational biology. While copy number data analysis and visualization provide insights into cancer biology, interpreting results can be challenging, and there may be multiple explanations for observed patterns of copy number alterations.ResultsWe created Control-FREEC Viewer, a tool that facilitates effective visualization and exploration of copy number data. With Control-FREEC Viewer, experimental data can be easily loaded by the user. After choosing the reference genome, copy number data are displayed in whole genome or single chromosome view. Gain or loss on a specific gene can be found and visualized on each chromosome. Analysis parameters for subsequent sessions can be stored and images can be exported in raster and vector formats.ConclusionsControl-FREEC Viewer enables users to import and visualize data analyzed by the Control-FREEC tool, as well as by other tools sharing a similar tabular output, providing a comprehensive and intuitive graphical user interface for data visualization.

Aneuploid serves as a prognostic marker and favors immunosuppressive microenvironment in ovarian cancer

Ovarian cancer is the most lethal gynecologic neoplasm, and most patients experience recurrence and chemoresistance. Even the promising immunotherapy showed limited efficacy in ovarian cancer, probably due to the immunosuppressive microenvironment. However, the behind mechanisms of the immune exclusion or cold phenotype in ovarian cancer still remain to be explored. As a cancer dominated by copy number variations instead of mutations, ovarian cancer contains a high fraction of aneuploid, which might correlate with immune inhibition. Nevertheless, whether or how aneuploid affects ovarian cancer is still unclear. For exploring the role of aneuploid cancer cells and the potential ploidy-immune relationship, herein, the ploidy information was first comprehensively analyzed combining the karyotype data and copy number variation data obtained from Mitelman and cBioPortal databases, respectively. Ovarian cancer showed strong ploidy heterogeneity, with high fraction of aneuploid and recurrent arm-level and whole chromosome changes. Furthermore, clinical parameters were compared between the highly-aneuploid and the near-diploid ovarian cancers. Aneuploid indicated high grade, poor overall survival and poor disease-free survival in ovarian cancer. To understand the biofunction affected by aneuploid, the differentially expressed genes between the highly-aneuploid and the near-diploid groups were analyzed. Transcription data suggested that aneuploid cancer correlated with deregulated MHC expression, abnormal antigen presentation, and less infiltration of macrophages and activated T cells and higher level of T cell exclusion. Furthermore, the ploidy-MHC association was verified using the Human Protein Atlas database. All these data supported that aneuploid might be promising for cancer management and immune surveillance in ovarian cancer.

Occurrence of NECTIN4 amplification in solid tumors and enfortumab vedotin response in metastatic urothelial cancer.

673 Background: The anti-NECTIN4 antibody-drug conjugate (ADC) enfortumab vedotin (EV) is approved for patients with metastatic urothelial cancer (mUC). However, durable benefit is only achieved in a small, yet uncharacterized patient subset. NECTIN4 is located on chromosome 1q23.3, and 1q23.3 gains are frequent genomic events in mUC leading to NECTIN4 amplifications. Here, we aimed to evaluate the potential of NECTIN4 amplification as a genomic biomarker to predict EV response in patients with mUC. Methods: We established a NECTIN4-specific fluorescence in-situ hybridization (FISH) assay to assess NECTIN4 copy number variations (CNVs) in a multicenter EV-treated mUC patient cohort (UC-EV, N=77), and CNV data were correlated with membranous NECTIN4 protein expression (H-score) assessed via immunohistochemistry, EV treatment responses and outcomes. Next, we conducted a pan-cancer analysis of the The Cancer Genome Atlas (TCGA) datasets, comprising 10,712 patients across 32 cancer types, to investigate the relationship between NECTIN4 CNV, mRNA expression (RNAseq) and protein levels (RPPA) across entities. Results: In TCGA cohorts, NECTIN4 amplification occurs frequently across different solid cancer types, especially in 15-20% of bladder cancers (17% in TCGA-BLCA), as well as 5-10% in breast cancer (TCGA-BRCA) and lung adenocarcinoma (TCGA-LUAD). We confirmed the amplification frequency in our UC-EV cohort (18%). NECTIN4 amplification is significantly associated with both increased NECTIN4 mRNA expression (e.g., TCGA-BLCA, BRCA, LUAD) and membranous NECTIN4 protein expression (UC-EV), and represents a stable genomic alteration during metastatic progression. In the UC-EV cohort, all patients with NECTIN4 amplification (N=14) responded to EV. In multivariable Cox adjusted for age and sex, NECTIN4 amplifications led to a 93% risk reduction for death compared to a NECTIN4non-amplified status (HR=0.07, 95%-CI 0.01–0.53; P&lt;0.001). Conclusions: Our study highlights the value of NECTIN4 amplifications to predict EV responses in patients with mUC. NECTIN4 amplifications occur frequently in different cancer types and therefore have the potential to be a novel tumor-agnostic genomic biomarker that enables tailored NECTIN4-targeted therapies in various entities.

Developing a prognosis and chemotherapy evaluating model for colon adenocarcinoma based on mitotic catastrophe-related genes

Mitotic catastrophe (MC) is a novel form of cell death that plays an important role in the treatment and drug resistance of colon adenocarcinoma (COAD). However, MC related genes in COAD treatment and prognosis evaluation are rarely studied. In this study, the transcriptome data, somatic mutation and copy number variation data were obtained from The Cancer Genome Atlas (TCGA) database. The mitotic catastrophe related genes (MCRGs) were obtained from GENCARDS website. Differential gene analysis was conducted with LIMMA package. Univariate Cox regression analysis was used to identify prognostic related genes. Mutation analysis was performed and displayed by maftools package. RCircos package was used for localizing the position of genes on chromosomes. “Glmnet” R package was applied for constructing a risk model via the LASSO regression method. Consensus clustering analyses was implemented for clustering different subtypes. Functional enrichment analysis through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods, immune infiltration analysis via single sample gene set enrichment analysis (ssGSEA), tumor mutation burden and drug sensitivity analysis by pRRophetic R package were also carried out for risk model or molecular subtype’s assessment. Additionally, the connections between the expression of hub genes and overall survival (OS) were obtained from online Human Protein Atlas (HPA) website. Real-Time Quantitative Polymerase Chain Reaction (RT‑qPCR) further validated the expression of hub genes. A total of 207 differentially expressed MCRGs were selected in the TCGA cohort, 23 of which were significantly associated with OS in COAD patients. Subsequently, we constructed risk score prognostic models with 5 hub MCRGs, including SYCE2, SERPINE1, TRIP6, LIMK1, and EEPD1. The high-risk patients suffered from poorer prognosis. Furthermore, we developed a nomogram that gathered age, sex, staging, and risk score to accurately forecast the clinical survival outcomes in 1, 3, and 5 years. The results of functional enrichment suggested a significant correlation between MCRGs characteristics and cancer progression, with important implications for the immune microenvironment. Moreover, patients who displayed high TMB and high risk score showed worse prognosis, and risk characteristics were associated with different chemotherapeutic agents. Finally, RT‑qPCR verified the increased expression of the five MCRGs in clinical samples. The five MCRGs in the prognostic signature were associated with prognosis, and could be treated as reliable prognostic biomarkers and therapeutic targets for COAD patients with distinct clinicopathological characteristics, thereby providing a foundation for the precise application of pertinent drugs in COAD patients.

TCGAnalyzeR: An Online Pan-Cancer Tool for Integrative Visualization of Molecular and Clinical Data of Cancer Patients for Cohort and Associated Gene Discovery.

For humans, the parallel processing capability of visual recognition allows for faster comprehension of complex scenes and patterns. This is essential, especially for clinicians interpreting big data for whom the visualization tools play an even more vital role in transforming raw big data into clinical decision making by managing the inherent complexity and monitoring patterns interactively in real time. The Cancer Genome Atlas (TCGA) database's size and data variety challenge the effective utilization of this valuable resource by clinicians and biologists. We re-analyzed the five molecular data types, i.e., mutation, transcriptome profile, copy number variation, miRNA, and methylation data, of ~11,000 cancer patients with all 33 cancer types and integrated the existing TCGA patient cohorts from the literature into a free and efficient web application: TCGAnalyzeR. TCGAnalyzeR provides an integrative visualization of pre-analyzed TCGA data with several novel modules: (i) simple nucleotide variations with driver prediction; (ii) recurrent copy number alterations; (iii) differential expression in tumor versus normal, with pathway and the survival analysis; (iv) TCGA clinical data including metastasis and survival analysis; (v) external subcohorts from the literature, curatedTCGAData, and BiocOncoTK R packages; (vi) internal patient clusters determined using an iClusterPlus R package or signature-based expression analysis of five molecular data types. TCGAnalyzeR integrated the multi-omics, pan-cancer TCGA with ~120 subcohorts from the literature along with clipboard panels, thus allowing users to create their own subcohorts, compare against existing external subcohorts (MSI, Immune, PAM50, Triple Negative, IDH1, miRNA, metastasis, etc.) along with our internal patient clusters, and visualize cohort-centric or gene-centric results interactively using TCGAnalyzeR.

GLIMMERS: glioma molecular markers exploration using long-read sequencing.

The revised WHO guidelines for classifying and grading brain tumors include several copy number variation (CNV) markers. The turnaround time for detecting CNVs and alterations throughout the entire genome is drastically reduced with the customized read incremental approach on the nanopore platform. However, this approach is challenging for non-bioinformaticians due to the need to use multiple software tools, extract CNV markers and interpret results, which creates barriers due to the time and specialized resources that are necessary. To address this problem and help clinicians classify and grade brain tumors, we developed GLIMMERS: glioma molecular markers exploration using long-read sequencing, an open-access tool that automatically analyzes nanopore-based CNV data and generates simplified reports. GLIMMERS is available at https://gitlab.com/silol_public/glimmers under the terms of the MIT license.

Retracted: Identifying a 6-Gene Prognostic Signature for Lung Adenocarcinoma Based on Copy Number Variation and Gene Expression Data.

[This retracts the article DOI: 10.1155/2022/6962163.].

Identification of a ceRNA Network Driven by Copy Number Variations in Esophageal Cancer.

Copy number variation (CNV) is associated with progression of esophageal cancer (EC), a common gastrointestinal neoplasm. Using sequencing data, CNV data, and clinical data of EC transcriptome samples obtained from public databases, we performed differential expression analysis on sequencing data. Differentially expressed CNV-driven lncRNAs were screened using the chi-square test, and CNV-driven lncRNA-associated miRNAs and mRNAs were predicted. Cytoscape software was then used to construct ceRNA networks. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to investigate biological functions of mRNAs in the ceRNA network. Survival curves were plotted to explore correlations between lncRNAs in the ceRNA network and overall survival of CNV patients. Multiple databases were used to predict lncRNAs-related drugs. A dysregulated lncRNA-associated ceRNA network driven by CNV in EC, including 11 lncRNAs, 11 miRNAs and 159 mRNAs, was constructed. Downstream enrichment of mRNAs was related to biological processes such as extracellular matrix organization, indicating that these mRNAs mainly participate in intercellular exchange between tumor cells. Additionally, expression of all lncRNAs in the ceRNA network, except LINC00950, LINC01270 and MIR181A1HG, was correlated with patients' CNV. In addition, none of the 11 lncRNAs was significantly correlated with overall survival of CNV patients. CH5424802 and PD-033299CNV mainly affected the RTK signaling pathway and the cell cycle of tumor cells via RP11-180N14.1 and RP11-273 G15.2 in the ceRNA network. This study identified 11 CNV-driven lncRNAs that might affect EC development, 2 of which have promising effects if applied to drug treatment. These findings might assist in identifying novel treatments for EC.

Genome-scale copy number variant analysis in schizophrenia patients and controls from South India.

Copy number variants (CNVs) are among the main genetic factors identified in schizophrenia (SZ) through genome-scale studies conducted mostly in Caucasian populations. However, to date, there have been no genome-scale CNV reports on patients from India. To address this shortcoming, we generated, for the first time, genome-scale CNV data for 168 SZ patients and 168 controls from South India. In total, 63 different CNVs were identified in 56 patients and 46 controls with a significantly higher proportion of medium-sized deletions (100 kb-1 Mb) after multiple testing (FDR = 2.7E-4) in patients. Of these, 13 CNVs were previously reported; however, when searched against GWAS, transcriptome, exome, and DNA methylation studies, another 17 CNVs with candidate genes were identified. Of the total 30 CNVs, 28 were present in 38 patients and 12 in 27 controls, indicating a significantly higher representation in the former (p = 1.87E-5). Only 4q35.1-q35.2 duplications were significant (p = 0.020) and observed in 11 controls and 2 patients. Among the others that are not significant, a few examples of patient-specific and previously reported CNVs include deletions of 11q14.1 (DLG2), 22q11.21, and 14q21.1 (LRFN5). 16p13.3 deletion (RBFOX1), 3p14.2 duplication (CADPS), and 7p11.2 duplication (CCT6A) were some of the novel CNVs containing candidate genes. However, these observations need to be replicated in a larger sample size. In conclusion, this report constitutes an important foundation for future CNV studies in a relatively unexplored population. In addition, the data indicate that there are advantages in using an integrated approach for better identification of candidate CNVs for SZ and other mental health disorders.

Identification of three lncRNA-related prognostic signatures in gastric cancer by integrated multi-omics analysis.

Aims: The systematic identification of molecular features correlated with the clinical status of gastric cancer (GC) inpatients is significant, although such investigation remains insufficient. Methods: GC subtyping based on RNA sequencing, copy number variation and DNA methylation data were derived from The Cancer Genome Atlasprogram. Prognostics lncRNA biomarkers for GC were identified by univariate Cox, LASSO and SVM-RFE analysis. Results: Three molecular subtypes with significant survival discrepancies, and their specific DEmRNAs and DElncRNAs were identified. Three reliable prognostic-associated lncRNA, including LINC00670, LINC00452 and LINC00160, were selected for GC. Conclusion: Our findings expanded the understanding on the regulatory network of lncRNAs in GC, providing potential targets for prognosis and treatment of GC patients.

Prognostic Value and the Immune Microenvironment-associated Role of SMAD4 in Pancreatic Adenocarcinoma.

SMAD4 is a well-known cancer suppressor gene that regulates cell proliferation, differentiation, autoimmunity, pluripotency, and immune responses in pancreatic adenocarcinoma (PAAD). We herein investigated a novel involvement of SMAD4 within the PAAD microenvironment. Transcriptome data, derived from The Cancer Genome Atlas and Genotype-Tissue Expression Using TIMER 2.0, CIBERSORT, and ImmuCellAI, were used to identify the immune cell infiltration pattern of PAAD. We then knocked-down SMAD4 in the PANC-1 cell line and acquired RNA-seq data through the Illumina microarray technology. Kyoto Encyclopedia of Genes and Genomes was utilized along with Gene Ontology enrichment analyses, and protein-protein interaction network analysis to screen for genes that were differentially expressed. We constructed a miRNA-mRNA regulatory network and analyzed SMAD4 copy number variation (CNV) data. In PAAD, decreased levels of SMAD4 expression were found to be connected to an unfavorable prognosis. There was a significantly higher infiltration level of DC cells, CD8+ T cells, TgD, Tc, and Tex cells and a lower level of B cells and Th2 in the SMAD4-high group. The expression of SMAD4 and immune cell infiltration including CD8+ T cells, myeloid dendritic cells, neutrophils, and macrophages are significantly positively correlated. DEGs were found enriched in the hypoxia response pathway. The six hypoxia-related genes exhibited a significant correlation with immune cell infiltration and survival rates. SMAD4 CNV levels were associated with MSI, stemness, infiltration of immune cells, and survival rates. A statistically significant correlation was found between SMAD4 expression and immune cell infiltration. SMAD4 could mediate hypoxia response in pancreatic cancer. The CNV levels of SMAD4 were associated with prognosis. SMAD4 has potential as a prognostic biomarker and provides a new orientation for the immunotherapy of PAAD.

Comparison of World Health Organization and International Consensus Classification Guidelines for Myeloid Neoplasms Harboring TP53-Mutations Using an Independent International Cohort

BACKGROUND: Recently proposed World Health Organization (WHO, 5 th edition) and International Consensus Classification (ICC) recognized myeloid neoplasms (MN) harboring TP53-mutated ( TP53mut) as a separate entity. However, there are critical differences between the two classifications regarding allelic-status, variance allele frequency (VAF) cut-off and blast categories, that may lead to under- or overestimation of the risk. METHODS: We analyzed MN harboring TP53mut (VAF ≥2%, n=536) managed at Mayo Clinic (USA) and South Australia Health Network (Australia). ICC (Arber et al, Blood 2022) and WHO (Bluebook, Beta v2) definitions were used to stratify MN harboring TP53mut. RESULTS: Of 536 cases, 379 (71%) had myelodysplastic syndromes (MDS) (&amp;lt;20% blasts) and 157 (29%) had acute myeloid leukemia (AML). Using WHO, 160 (42%) of 379 were classified as MDS-bi TP53 as they harbored 2 TP53mut (n=115; 30%) or 1 TP53mut plus confirmed loss of heterozygosity (LOH) or copy neutral LOH (cnLOH) of TP53 locus (n=45; 12%). Sixty-two (16%) TP53mut MDS with VAF &amp;gt;49% were classified as presumptive MDS-bi TP53. Of the 157 cases with 1 TP53mut VAF ≤49%, 17 confirmed to have an intact wild-type TP53 copy and were classified as monoallelic. While 140 (89%) cases did not have copy number variation (CNV) analysis. We next evaluated the number of TP53mut cases classified differently by WHO and ICC. As WHO does not recognize TP53mut AML as a separate entity, 157 (29%) TP53mut AML were not classifiable. 46% (174/379) of the TP53mut MDS including 48 (30%), 12 (19%) and 79 (65%) of bi TP53, presumptive bi TP53 and monoallelic TP53mut MDS according to WHO were classified differently by ICC ( Figure 1A). Discordance between the two classifications were predominantly driven by: (i) The difference in VAF cut-off. Sixty patients (11%) with TP53mut VAF &amp;lt;10% were excluded from the ICC classification of TP53mut MN as it mandates TP53mut VAF cut-off ≥10%. 11 (18%) and 41 (68%) of these had bi TP53, monoallelic TP53mut MDS by WHO criteria while 8 (13%) had AML. Importantly, within the VAF &amp;lt;10% cohort, median overall survival (OS) of 2 TP53mut or 1 TP53mut with 17p13.1 (hereafter 17p) deletion or complex karyotype (CK, n=30) was significantly poor compared to 1 TP53mut VAF &amp;lt;10% (11.6 vs 32.4 months; P =0.002, Figure 1B), suggesting that ICC may underestimate the poor prognosis of multi-hit or multi-hit equivalent TP53mut MN with VAF &amp;lt;10%. (ii) The difference in bone marrow blast cut-off. The underlying assumption by WHO is that prognosis of bi TP53mut MDS is poor irrespective of blast percentage category (0-9% vs 10-19%). In contrast, ICC considers that TP53mut MDS/AML (blasts 10-19%) is associated with poor prognosis irrespective of TP53 allelic status. Median OS of TP53mut MDS with 10-19% blasts was significantly poor compared to blast 0-9% (6.4 vs 11.6 months, P &amp;lt;0.0001). Second, median OS of bi TP53mut was significantly poor than monoallelic TP53mut in MDS- blast 0-9% (10.1 vs 15.9 months; P =0.0001) but not in blasts 10-19% (6.8 vs 6.2 months; P =0.19). Hence, exclusion of monoallelic TP53mut MDS with blast 10-19% as TP53mut MN from the WHO classification may underestimate the poor prognosis. (iii) The difference in the definition of biallelic and multi-hit TP53 loss. 35 cases with 1 TP53mut VAF ≤49%, had 17p deletion but did not have CNV analysis to confirm the loss of TP53 locus and could not be classified further using WHO. ICC classified 18 (50%) of these cases as multi-hit. Median OS of 1 TP53mut VAF ≤49%, and 17p deletion on karyotype was significantly poor than 1 TP53mut (VAF ≤49%without 17p deletion on karyotype (6.4 vs 14.6 months, P =0.001). (iv) The difference in the definition of multi-hit equivalent. 32% (33/104) WHO defined monoallelic cases without available CNV data, were classified as multi-hit-equivalent by ICC as they harbored CK. However, WHO does not consider CK as presumptive evidence of bi TP53. Median OS of 1 TP53mut VAF ≤49% with CK was significantly poor compared to 1 TP53mut without CK (8.2 vs 26.9 months, P =0.0008). CONCLUSION: Our study provides evidence that poor prognosis of ‘multi-hit’ TP53 with VAF &amp;lt;10% is underestimated by ICC. On the other hand, poor prognosis of monoallelic TP53 MDS with 10-19% blasts as well as 1 TP53mut VAF ≤49% in the presence of CK is underestimated by WHO. These data suggest that both classifications can be further refined.

XL1R-Net: Explainable AI-driven improved [formula omitted]-regularized deep neural architecture for NSCLC biomarker identification

Background and Objective:Non-small cell lung cancer (NSCLC) exhibits intrinsic molecular heterogeneity, primarily driven by the mutation of specific biomarkers. Identification of these biomarkers would assist not only in distinguishing NSCLC into its major subtypes — Adenocarcinoma and Squamous Cell Carcinoma, but also in developing targeted therapy. Medical practitioners use one or more types of omic data to identify these biomarkers, copy number variation (CNV) being one such type. CNV provides a measure of genomic instability, which is considered a hallmark of carcinoma. However, the CNV data has not received much attention for biomarker identification. This paper aims to identify biomarkers for NSCLC using CNV data. Methods:An eXplainable AI (XAI)-driven L1-regularized deep learning architecture, XL1R-Net, is proposed that introduces a novel modification of the standard L1-regularized gradient descent algorithm to arrive at an improved deep neural classifier for NSCLC subtyping. Further, XAI-based feature identification has been used to leverage the trained classifier to uncover a set of twenty NCSLC-relevant biomarkers. Results:The identified biomarkers are evaluated based on their classification performance and clinical relevance. Using Multilayer Perceptron (MLP)-based model, a classification accuracy of 84.95% using 10-fold cross-validation is achieved. Moreover, the statistical significance test on the classification performance also revealed the superiority of the MLP model over the competitive machine learning models. Further, the publicly available Drug–Gene Interaction Database reveals twelve of the identified biomarkers as potentially druggable. The K–M Plotter tool was used to verify eighteen of the identified biomarkers with a high probability of predicting NSCLC patients’ likelihood of survival. While nine of the identified biomarkers confirm the recent literature, five find mention in the OncoKB Gene List. Conclusion:A set of seven novel biomarkers that have not been reported in the literature could be investigated for their potential contribution towards NSCLC therapy. Given NSCLC’s genetic diversity, using only one omics data type may not adequately capture the tumor’s complexity. Multiomics data and its integration with other sources will be examined in the future to better understand NSCLC heterogeneity.

MethylClass: an R package to construct DNA methylation-based classification models.

DNA methylation profiling is a useful tool to increase the accuracy of a cancer diagnosis. However, a comprehensive R package specially for it is lacking. Hence, we developed the R package methylClass for methylation-based classification. Within it, we provide the eSVM (ensemble-based support vector machine) model to achieve much higher accuracy in methylation data classification than the popular random forest model and overcome the time-consuming problem of the traditional SVM. In addition, some novel feature selection methods are included in the package to improve the classification. Furthermore, because methylation data can be converted to other omics, such as copy number variation data, we also provide functions for multi-omics studies. The testing of this package on four datasets shows the accurate performance of our package, especially eSVM, which can be used in both methylation and multi-omics models and outperforms other methods in both cases. methylClass is available at: https://github.com/yuabrahamliu/methylClass.