Copy Number Variation Data Research Articles

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.

NRXN1 as a Prognostic Biomarker: Linking Copy Number Variation to EMT and Survival in Colon Cancer.

The role of biomarkers in cancer treatment varies significantly depending on the cancer stage. Thus, in clinical practice, tailoring biomarkers to meet the specific needs and challenges of each cancer stage can increase the precision of treatment. Because they reflect underlying genetic alterations that influence cancer progression, copy number variation (CNV) biomarkers can play crucial prognostic roles. In our previous study, we identified potential survival-related genes for colorectal cancer (CRC) by analyzing CNV and gene expression data using a machine-learning approach. To further investigate the biological function of NRXN1, we assessed the use of RNA sequencing, phosphokinase assays, real-time quantitative PCR, and Western blot analysis. We found that NRXN1 copy number deletion was significantly associated with poor overall survival (OS) and recurrence-free survival (RFS), even in patients who received adjuvant chemotherapy. Compared with its expression in normal tissues, NRXN1 expression was lower in tumors, suggesting its potential role as a tumor suppressor. NRXN1 knockdown enhanced CRC cell viability and invasion, and transcriptome analysis indicated that the increased invasion was caused by GSK3β-mediated epithelial-mesenchymal transition. These findings highlight NRXN1 copy number deletion as a novel biomarker for predicting recurrence and survival in patients with resected colon cancer.

Integrated analysis and experiments uncover the function of disulfidptosis in predicting immunotherapy effectiveness and delineating immune landscapes in uterine corpus endometrial carcinoma.

Recently, a novel type of metabolic-regulated cell demise titled disulfidptosis has been discovered. Studies have demonstrated its importance in immune responses against cancer and its impact on the proliferation of cancer cells. Nonetheless, the precise mechanism and roles of disulfidptosis are not fully understood, particularly regarding the prognosis for individuals with uterine corpus endometrial carcinoma (UCEC). In this research, a distinctive disulfidptosis pattern was developed in UCEC, and by utilizing Non-negative Matrix Factorization (NMF) on 23 disulfidptosis related genes within the TCGA database, 3 distinct subgroups were distinguished. To collect data, we acquired gene expression profiles, somatic mutation information, copy number variation data, and corresponding clinical data from the TCGA and GEO database, specifically from UCEC patients. Cell line experiments and immunohistochemical (IHC) staining were conducted to validate the role of the LRPPRC in proliferation, migration and invasion. The genetic features and immune microenvironment of these subgroups were examined. It is worth mentioning that these subgroups offer important insights into comprehending the tumor microenvironment (TME) and the response of patients to immunotherapy and chemotherapy. Moreover, a disulfidptosis model was developed and validated, demonstrating a high level of accuracy in predicting the prognosis and outcomes of immunotherapy in UCEC patients. Additionally, a novel biomarker, LRPPRC, was identified, which can server as a promising predictor for forecasting prognosis in UCEC patients, with validation through tissue microarray staining and cell line experiments. This study has designed a classification system and a disulfidptosis model for UCEC, in addition to identifying a new biomarker, LRPPRC, for UCEC. These advancements serve as reliable and positive indicators for predicting outcomes and the efficacy of immunotherapy for each UCEC patient.

Odontogenic Myxomas Harbor Recurrent Copy Number Alterations and a Distinct Methylation Signature.

Odontogenic myxoma is a rare, benign, and locally aggressive tumor that develops in the tooth-bearing areas of the jaw. The molecular mechanisms underlying odontogenic myxomas are unknown and no diagnostic markers are available to date. The aim of this study was to analyze DNA methylation and copy number variations in odontogenic myxomas to identify new molecular signatures for diagnostic decision-making. We collected a cohort of 16 odontogenic myxomas from 2006 to 2021 located in the mandible (n = 10) and maxilla (n = 6) with available formalin-fixed paraffin-embedded or fresh frozen tumor tissue from a biopsy or resection material. Genome-wide DNA methylation and copy number variation data were generated from 12 odontogenic myxomas using the Illumina Infinium Methylation EPIC array, interrogating >850,000 CpG sites. Unsupervised clustering and dimensionality reduction (Uniform Manifold Approximation and Projection) revealed that odontogenic myxomas formed a distinct DNA methylation class. Copy number profiling showed recurrent whole-chromosome gains (trisomies) of chromosomes 5, 8, and 20 in all cases, and of chromosomes 10, 12, and 17 in all except one case. In conclusion, odontogenic myxomas harbor recurrent copy number patterns and a distinct DNA methylation profile, which can be used as an additional diagnostic tool in the appropriate clinical and radiologic context. Further research is needed to explain the genetic mechanisms caused by these alterations that drive these locally aggressive neoplasms.

Genome-wide association study of copy number variations in Parkinson's disease.

Our study investigates the impact of copy number variations (CNVs) on Parkinson's disease (PD) pathogenesis using genome-wide data, aiming to uncover novel genetic mechanisms and improve the understanding of the role of CNVs in sporadic PD. We applied a sliding window approach to perform CNV-GWAS and conducted genome-wide burden analyses on CNV data from 11,035 PD patients (including 2,731 early-onset PD (EOPD)) and 8,901 controls from the COURAGE-PD consortium. We identified 14 genome-wide significant CNV loci associated with PD, including one deletion and 13 duplications. Among these, duplications in 7q22.1, 11q12.3 and 7q33 displayed the highest effect. Two significant duplications overlapped with PD-related genes SNCA and VPS13C, but none overlapped with recent significant SNP-based GWAS findings. Five duplications included genes associated with neurological disease, and four overlapping genes were dosage-sensitive and intolerant to loss-of-function variants. Enriched pathways included neurodegeneration, steroid hormone biosynthesis, and lipid metabolism. In early-onset cases, four loci were significantly associated with EOPD, including three known duplications and one novel deletion in PRKN. CNV burden analysis showed a higher prevalence of CNVs in PD-related genes in patients compared to controls (OR=1.56 [1.18-2.09], p=0.0013), with PRKN showing the highest burden (OR=1.47 [1.10-1.98], p=0.026). Patients with CNVs in PRKN had an earlier disease onset. Burden analysis with controls and EOPD patients showed similar results. This is the largest CNV-based GWAS in PD identifying novel CNV regions and confirming the significant CNV burden in EOPD, primarily driven by the PRKN gene, warranting further investigation.

Comprehensive analysis of flavohemoprotein copy number variation in Giardia intestinalis: exploring links to metronidazole resistance

BackgroundGiardiasis, caused by the protozoan parasite Giardia intestinalis, often presents a treatment challenge, particularly in terms of resistance to metronidazole. Despite extensive research, markers for metronidazole resistance have not yet been identified.MethodsThis study analysed 28 clinical samples of G. intestinalis from sub-assemblage AII, characterised by varying responses to metronidazole treatment. We focussed on copy number variation (CNV) of the multi-copy flavohemoprotein gene, analysed using digital polymerase chain reaction (dPCR) and next generation sequencing (NGS). Additionally, chromosomal ploidy was tested in 18 of these samples. Flavohemoprotein CNV was also assessed in 17 samples from other sub-assemblages.ResultsAnalyses revealed variable CNVs of the flavohemoprotein gene among the isolates, with no correlation to clinical metronidazole resistance. Discrepancies in CNVs detected from NGS data were attributed to biases linked to the whole genome amplification. However, dPCR helped to clarify these discrepancies by providing more consistent CNV data. Significant differences in flavohemoprotein CNVs were observed across different G. intestinalis sub-assemblages. Notably, Giardia exhibits a propensity for aneuploidy, contributing to genomic variability within and between sub-assemblages.ConclusionsThe complexity of the clinical metronidazole resistance in Giardia is influenced by multiple genetic factors, including CNVs and aneuploidy. No significant differences in the CNV of the flavohemoprotein gene between isolates from metronidazole-resistant and metronidazole-sensitive cases of giardiasis were found, underscoring the need for further research to identify reliable genetic markers for resistance. We demonstrate that dPCR and NGS are robust methods for analysing CNVs and provide cross-validating results, highlighting their utility in the genetic analyses of this parasite.Graphical

Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in Precision.

The accurate identification of the primary tumor origin in metastatic cancer cases is crucial for guiding treatment decisions and improving patient outcomes. Copy number alterations (CNAs) and copy number variation (CNV) have emerged as valuable genomic markers for predicting the origin of metastases. However, current models that predict cancer type based on CNV or CNA suffer from low AUC values. To address this challenge, we employed a cutting-edge neural network approach utilizing a dataset comprising CNA profiles from twenty different cancer types. We developed two workflows: the first evaluated the performance of two deep neural networks-one ReLU-based and the other a 2D convolutional network. In the second workflow, we stratified cancer types based on anatomical and physiological classifications, constructing shallow neural networks to differentiate between cancer types within the same cluster. Both approaches demonstrated high AUC values, with deep neural networks achieving a precision of 60%, suggesting a mathematical relationship between CNV type, location, and cancer type. Our findings highlight the potential of using CNA/CNV to aid pathologists in accurately identifying cancer origins with accessible clinical tests.

Integrative analysis of genomic and epigenomic regulation reveals miRNA mediated tumor heterogeneity and immune evasion in lower grade glioma

The expression dysregulation of microRNAs (miRNA) has been widely reported during cancer development, however, the underling mechanism remains largely unanswered. In the present work, we performed a systematic integrative study for genome-wide DNA methylation, copy number variation and miRNA expression data to identify mechanisms underlying miRNA dysregulation in lower grade glioma. We identify 719 miRNAs whose expression was associated with alterations of copy number variation or promoter methylation. Integrative multi-omics analysis revealed four subtypes with differing prognoses. These glioma subtypes exhibited distinct immune-related characteristics as well as clinical and genetic features. By construction of a miRNA regulatory network, we identified candidate miRNAs associated with immune evasion and response to immunotherapy. Finally, eight prognosis related miRNAs were validated to promote cell migration, invasion and proliferation through in vitro experiments. Our study reveals the crosstalk among DNA methylation, copy number variation and miRNA expression for immune regulation in glioma, and could have important implications for patient stratification and development of biomarkers for immunotherapy approaches.

Multi-omics analysis of the prognostic and biological role of cuproptosis-related gene in gastric cancer.

A considerable number of gastric cancer (GC) patients cannot receive benefits from current treatments. We aimed to identify possible biomarkers of cuproptosis-related genes (CRGs) in GC patients, which may help guide precision medicine-based decision-making. RNA sequencing data, copy number variations (CNVs) data, and single nucleotide variant (SNV) data were obtained from The Cancer Genome Atlas (TCGA) database and Gene Set Cancer Analysis (GSCA) database. Chi-squared test was adopted to screen differentially expressed CRGs (DE-CRGs) between samples from 14 kinds of carcinoma and adjacent tissue samples. Then, GC samples were divided into high- and low-expressed groups based on DE-CRGs for further survival analyses and the selection of biomarkers. Methylation sites related with biomarkers were acquired. The correlation between immune cells and biomarkers was verified. Finally, miRNA-mRNA, TFs-mRNA, and co-expression networks were established to detect factors with regulating effects on biomarkers. Three CRGs including LIAS, GLS, and CDKN2A were identified as biomarkers in GC patients. Three methylation sites with a significant survival effect including cg13601799, 07562918, and 07253264 were acquired. Then, we found that B cells native was significantly correlated with CDKN2A, four immune cells such as T cells regulatory are significantly correlated with GLS, and two immune cells such as T cells CD4 memory activated were significantly correlated with LIAS. Moreover, 10 miRNAs in the miRNA-mRNA network and three transcription factors (TFs) in the TFs-mRNA network had a significant correlation with overall survival (OS). Finally, 20 enrichment functions were obtained on the basis of the co-expression network. Three biomarkers with a prognosis prediction value of GC were found, and multi-factor regulatory networks were constructed to screen out 13 factors with regulating influences of biomarkers.

An Innovative Multi-Omics Model Integrating Latent Alignment and Attention Mechanism for Drug Response Prediction.

By using omics, we can now examine all components of biological systems simultaneously. Deep learning-based drug prediction methods have shown promise by integrating cancer-related multi-omics data. However, the complex interaction between genes poses challenges in accurately projecting multi-omics data. In this research, we present a predictive model for drug response that incorporates diverse types of omics data, comprising genetic mutation, copy number variation, methylation, and gene expression data. This study proposes latent alignment for information mismatch in integration, which is achieved through an attention module capturing interactions among diverse types of omics data. The latent alignment and attention modules significantly improve predictions, outperforming the baseline model, with MSE = 1.1333, F1-score = 0.5342, and AUROC = 0.5776. High accuracy was achieved in predicting drug responses for piplartine and tenovin-6, while the accuracy was comparatively lower for mitomycin-C and obatoclax. The latent alignment module exclusively outperforms the baseline model, enhancing the MSE by 0.2375, the F1-score by 4.84%, and the AUROC by 6.1%. Similarly, the attention module only improves these metrics by 0.1899, 2.88%, and 2.84%, respectively. In the interpretability case study, panobinostat exhibited the most effective predicted response, with a value of -4.895. We provide reliable insights for drug selection in personalized medicine by identifying crucial genetic factors influencing drug response.

Identification of novel driver risk genes in CNV loci associated with neurodevelopmental disorders

Copy number variants (CNVs) are genome-wide structural variations involving the duplication or deletion of large nucleotide sequences. While these types of variations can be commonly found in humans, large and rare CNVs are known to contribute to the development of various neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD). Nevertheless, given that these NDD-risk CNVs cover broad regions of the genome, it is particularly challenging to pinpoint the critical gene(s) responsible for the manifestation of the phenotype. In this study, we performed a meta-analysis of CNV data from 11,614 patients with NDDs and 4,031 controls from SFARI database to identify 41 NDD-risk CNV loci, including 24 novel regions. We also found evidence for dosage-sensitive genes within these regions being significantly enriched for known NDD-risk genes and pathways. In addition, a significant proportion of these genes was found to i) converge in protein-protein interaction networks; ii) be among most expressed genes in the brain across all developmental stages; and iii) be hit by deletions that are significantly over-transmitted to individuals with ASD within multiplex ASD families from the iHART cohort. Finally, we conducted a burden analysis using 4,281 NDD cases from Decipher and iHART cohorts, and 2,504 neurotypical controls from 1,000 Genomes and iHART, that resulted in the validation of the association of 162 dosage sensitive genes driving risk for NDDs, including 22 novel NDD-risk genes. Importantly, most NDD-risk CNV loci entail multiple NDD-risk genes in agreement with a polygenic model associated with the majority of NDD cases.

Relationship between dynamic changes in circulating tumor fraction and real-world imaging with real-world survival in patients with solid tumors treated with immunotherapy.

3046 Background: There is an unmet need for a sensitive biomarker that can determine which patients will confer long term benefit from immune checkpoint inhibitors (ICI). While radiologic imaging is the current standard of care for assessing ICI response, imaging can be difficult to interpret and is typically only assessed at 3 month intervals. Dynamic changes in circulating tumor fraction (TF) is a potential biomarker for monitoring ICI response. Here we evaluate the clinical benefit of a molecular biomarker, xM for treatment response monitoring (TRM), in addition to imaging, in a real-world (rw), pan-cancer cohort treated with ICIs. Methods: Using the Tempus liquid biopsy assay, ctDNA TF was computed by applying an ensemble algorithm incorporating copy number variant data and pathogenic variant allele frequencies. Deidentified patient records from the Tempus multimodal database were analyzed if patients had a baseline xF test ≤ 40 days prior to the start of ICI (alone or in combination with chemotherapy [CT], median = 13 days) and an on-treatment xF test 15-180 days post-ICI (median=93 days) and clinical response evaluated via imaging 15-126 days post ICI (median=77 days). Molecular responders (MR) were defined as patients with a ≥ 50% decrease in TF between tests. Rw-imaging, as documented by a provider in their clinical notes, was categorized as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD), Rw overall survival (rwOS) was defined as time from the imaging date closest to on-treatment xF test to death. A likelihood ratio test at a two-sided 5% significance level was performed to assess if a Cox proportional hazards model incorporating both xM Monitor and rw-imaging as covariates (full model) had predictive power over a reduced model with only rw-imaging as a covariate. Results: The evaluable cohort (N=51) – 53% female, median age 64 yrs – had >10 advanced solid tumors (NSCLC 29% and breast cancer 18%). The majority of patients (69%) received ICI + CT, while 31% received ICI alone. 24 patients achieved MR (79% on ICI monotherapy, 21% on ICI + CT). Concordance of response markers is shown in the table. In the full model, xM was a significant predictor of rwOS (HR=0.33 [0.13, 0.84], p=0.02). The added information provided from xM was significant and superior compared to the reduced model of only rw-imaging (p=0.02). For patients with nMR and CR/PR/SD predicted median survival was 10 months whereas for patients with MR and CR/PR/SD predicted median survival was 16 months. Conclusions: MRs achieved significantly longer rwOS than nMRs and provides significant power in predicting rwOS beyond rw-imaging response. xM used for TRM can help identify nMRS who can discontinue therapy and be spared ICI toxicity. [Table: see text]

Mutational landscape of HSP family on human breast cancer

Breast cancer (BRCA) is a prevalent malignancy with the highest incidence among females. BRCA can be categorized into five intrinsic molecular subtypes (LumA, LumB, HER2, Basal, and Normal), each characterized by varying molecular and clinical features determined by the expression of intrinsic genes (PAM50). The Heat Shock Protein (HSP) family is composed of 95 genes evolutionary conservated, they have critical roles in proteostasis in both normal and cancerous processes. Many studies have linked HSP to the development and spread of cancer. They modulate the activity of multiple proteins expressed by oncogenes and anti-oncogenes through a range of interactions. In this study, we evaluate the mutational changes that HSP undergoes in BRCA mainly from the TCGA database. We observe that Copy Number Variations (CNV) are the more frequent events analyzed surpassing the occurrence of point mutations, indels, and translation start site mutations. The Basal subtype showcased the highest count of amplified CNV, including subtype-specific changes, whereas the Luminals tumors accumulated the greatest number of deletion CNV. Meanwhile, the HER2 subtype exhibited a comparatively lower frequency of CNV alterations when compared to the other subtypes. This study integrates CNV and expression data, finding associations between these two variables and the influence of CNV on the deregulation of HSP expression. To enhance the role of HSP as a risk predictor in BRCA, we succeeded in identifying CNV profiles as a prognostic marker. We included Artificial Intelligence to improve the clustering of patients, and we achieved a molecular CNV signature as a significant risk factor independent of known classic markers, including molecular subtypes PAM50. This research enhances the comprehension of HSP DNA alterations in BRCA and its relation with predicting the risk of affected individuals providing insights to develop guide personalized treatment strategies.

Targeting PRKDC activates the efficacy of antitumor immunity while sensitizing to chemotherapy and targeted therapy in liver hepatocellular carcinoma.

Liver hepatocellular carcinoma (LIHC) ranks among the malignancies with the highest mortality rates, primarily due to chemoresistance culminating in treatment failure. Despite its impact, predictive models addressing disease progression, tumor microenvironment, and drug sensitivity remain elusive for LIHC patients. Recognizing the significant influence of various programmed cell death (PCD) modes on tumor evolution, this study investigates PCD genes to elucidate their implications on the prognosis and immune landscape of LIHC. To develop the classification and model, we employed a total of 17 genes associated with PCD patterns. To collect data, we acquired gene expression profiles, somatic mutation information, copy number variation data, and corresponding clinical data from the TCGA database, specifically from LIHC patients. Moreover, we obtained spatial transcriptome data and additional bulk datasets from the Gene Expression Omnibus (GEO) database to conduct further analysis. Various experiments were conducted to validate the role of the PCD gene PRKDC in proliferation, migration, invasion, EMT, cell cycle, therapeutic sensitivity, and antitumor immunity. A novel LIHC classification based on these genes divided patients into two clusters, C1 and C2. The C2 cluster exhibited characteristics indicative of poor prognosis and an immune-activated microenvironment. This group showed greater response potential to immune checkpoint inhibitors, displaying higher levels of certain immune signatures and receptors. A programmed cell death index (PCDI) was constructed using 17 selected PCD genes. This index could effectively predict patient prognosis, with higher PCDI indicating poorer survival rates and a more pro-tumor microenvironment. Immune landscapes revealed varying interactions with PCDI, suggesting therapeutic targets and insights into treatment resistance. Moreover, experiments results suggested that PRKDC can augment the invasive nature and growth of malignant cells and it can serve as a potential target for therapy, offering hope for ameliorating the prognosis of LIHC patients. The study uncovers the insights of programmed cell death in the prognosis and potential therapeutic interventions. And we found that PRKDC can serve as a target for enhancing the efficacy of antitumor immunity while sensitizing chemotherapy and targeted therapy in liver hepatocellular carcinoma.

Implications of GCLC in prognosis and immunity of lung adenocarcinoma and multi-omics regulation mechanisms

BackgroundFerroptosis is an iron-dependent type of regulated cell death, and has been implicated in lung adenocarcinoma (LUAD). Evidence has proved the key role of glutamate-cysteine ligase catalytic subunit (GCLC) in ferroptosis, but its role in LUAD remains unclear. Herein, we explored the implications of GCLC and relevant genes in LUAD prognosis and immunity as well as underlying molecular mechanisms.MethodsThis work gathered mRNA, miRNA, DNA methylation, somatic mutation and copy-number variation data from TCGA-LUAD. WGCNA was utilized for selecting GCLC-relevant genes, and a GCLC-relevant prognostic signature was built by uni- and multivariate-cox regression analyses. Immune compositions were estimated via CIBERSORT, and two immunotherapy cohorts of solid tumors were analyzed. Multi-omics regulatory mechanisms were finally assessed.ResultsOur results showed that GCLC was overexpressed in LUAD, and potentially resulted in undesirable survival. A prognostic model was generated, which owned accurate and independent performance in prognostication. GCLC, and relevant genes were notably connected with immune compositions and immune checkpoints. High GCLC expression was linked with better responses to anti-PD-L1 and anti-CTLA-4 treatment. Their possible DNA methylation sites were inferred, e.g., hypomethylation in cg19740353 might contribute to GCLC up-regulation. Frequent genetic mutations also affected their expression. Upstream transcription factors (E2F1/3/4, etc.), post-transcriptional regulation of miRNAs (hsa-mir-30c-1, etc.), lncRNAs (C8orf34-AS1, etc.), and IGF2BP1-mediated m6A modification were identified. It was also found NOP58-mediated SUMOylation post-translational modification.ConclusionsTogether, we show that GCLC and relevant genes exert crucial roles in LUAD prognosis and immunity, and their expression can be controlled by complex multi-omics mechanisms.

Identification of necroptosis-related gene signatures for predicting the prognosis of ovarian cancer

Ovarian cancer (OC) is one of the most prevalent and fatal malignant tumors of the female reproductive system. Our research aimed to develop a prognostic model to assist inclinical treatment decision-making.Utilizing data from The Cancer Genome Atlas (TCGA) and copy number variation (CNV) data from the University of California Santa Cruz (UCSC) database, we conducted analyses of differentially expressed genes (DEGs), gene function, and tumor microenvironment (TME) scores in various clusters of OC samples.Next, we classified participants into low-risk and high-risk groups based on the median risk score, thereby dividing both the training group and the entire group accordingly. Overall survival (OS) was significantly reduced in the high-risk group, and two independent prognostic factors were identified: age and risk score. Additionally, three genes—C-X-C Motif Chemokine Ligand 10 (CXCL10), RELB, and Caspase-3 (CASP3)—emerged as potential candidates for an independent prognostic signature with acceptable prognostic value. In Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, pathways related to immune responses and inflammatory cell chemotaxis were identified. Cellular experiments further validated the reliability and precision of our findings. In conclusion, necroptosis-related genes play critical roles in tumor immunity, and our model introduces a novel strategy for predicting the prognosis of OC patients.

F2RL3 Regulates Epithelial-Mesenchymal Transition and Angiogenesis in Gastric Cancer through the Rap1/MAPK Signaling Pathway.

Gastric cancer (GC) is frequently diagnosed at advanced stages, when cancer cells have already metastasized. Therefore, patients with GC have a low survival rate and poor prognosis even after treatment. We downloaded GC-related RNA sequencing (RNA-Seq) data, copy number variation (CNV) data, and clinical data for bioinformatics analysis to screen prognostic genes of GC. Single-sample gene set enrichment analysis and survival analyses were performed on the RNA-Seq data, and differential and correlation analyses were conducted on the CNV data to obtain CNV-driven differentially expressed genes (DEGs). Prognostic genes were identified through univariate Cox analyses of the CNV-driven DEGs, combined with the clinical data. F2R like thrombin or trypsin receptor 3 (F2RL3) was finally selected for verification after functional and survival analyses of the prognostic genes. F2RL3 expression was lower in paracancer tissue than in GC tissue, and lower in GES-1 gastric epithelial cells than in GC cells. The cell culture supernatants from F2RL3-knockdown GC cells were collected and used to culture human umbilical vein endothelial cells (HUVECs). It was observed that F2RL3 enhanced the activity, metastasis, invasion, and angiogenesis of GC cells; promoted the epithelial-mesenchymal transition (EMT) of GC cells; and impacted the Ras-associated protein 1 (Rap1)/mitogen-activated protein kinase (MAPK) pathway. To further explore the involvement of the Rap1/MAPK pathway in GC development, a pathway activator was added to GC cells with knockdown of F2RL3 expression. This pathway activator not only enhanced the activity, invasion, and migration of GC cells but also promoted the EMT and blood vessel formation. F2RL3 regulates the angiogenesis and EMT of GC cells through the Rap1/MAPK pathway, thus influencing the onset and progression of GC.

An original aneuploidy-related gene model for predicting lung adenocarcinoma survival and guiding therapy.

Aneuploidy is a hallmark of cancers, but the role of aneuploidy-related genes in lung adenocarcinoma (LUAD) and their prognostic value remain elusive. Gene expression and copy number variation (CNV) data were enrolled from TCGA and GEO database. Consistency clustering analysis was performed for molecular cluster. Tumor microenvironment was assessed by the xCell and ESTIMATE algorithm. Limma package was used for selecting differentially expressed genes (DEGs). LASSO and stepwise multivariate Cox regression analysis were used to establish an aneuploidy-related riskscore (ARS) signature. GDSC database was conducted to predict drug sensitivity. A nomogram was designed by rms R package. TCGA-LUAD patients were stratified into 3 clusters based on CNV data. The C1 cluster displayed the optimal survival advantage and highest inflammatory infiltration. Based on integrated intersecting DEGs, we constructed a 6-gene ARS model, which showed effective prediction for patient's survival. Drug sensitivity test predicted possible sensitive drugs in two risk groups. Additionally, the nomogram exhibited great predictive clinical treatment benefits. We established a 6-gene aneuploidy-related signature that could effectively predict the survival and therapy for LUAD patients. Additionally, the ARS model and nomogram could offer guidance for the preoperative estimation and postoperative therapy of LUAD.

Abstract LB246: Deep learning framework for patient recurrence and genetic marker prediction in diffuse glioma

Abstract Investigation of important molecular features of glioma patients may reveal driving mechanisms of patient recurrence and treatment failures in glioma. Applying various deep learning techniques enables the integration and systematic comparison of multi-omics features, allowing robust marker prediction and patient stratification. We extracted mutation and copy number variation data from the Glioma Longitudinal Analysis (GLASS) Consortium, consisting of 628 primary or recurrent samples. To achieve the patient recurrence classification task, we designed a transformer-based model that learns the genomic profiles and predicts patient recurrence status. Our model outperforms traditional machine learning models, including support vector machine (SVM), logistic regression, and decision trees, for up to 34% and 40% improvement of AUROC and AUPRC, respectively. Including mutational features alone can achieve the best prediction performance, while copy number variations are less informative in predicting patient recurrence status. Our approach is an early attempt that employs deep learning for dissecting the contribution of diverse genomic profiles on glioma recurrence and might provide insights into developing novel therapies for treating glioma. Citation Format: Ko-Hong Lin, Kai Zhang, Dung-Fang Lee, Xiaoqian Jiang. Deep learning framework for patient recurrence and genetic marker prediction in diffuse glioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB246.

Identification and validation of a novel anoikis-related prognostic model for prostate cancer.

Anoikis resistance is a hallmark characteristic of oncogenic transformation, which is crucial for tumor progression and metastasis. The aim of this study was to identify and validate a novel anoikis-related prognostic model for prostate cancer (PCa). We collected a gene expression profile, single nucleotide polymorphism mutation and copy number variation (CNV) data of 495 PCa patients from the TCGA database and 140 PCa samples from the MSKCC dataset. We extracted 434 anoikis-related genes and unsupervised consensus cluster analysis was used to identify molecular subtypes. The immune infiltration, molecular function, and genome alteration of subtypes were evaluated. A risk signature was developed using Cox regression analysis and validated with the MSKCC dataset. We also identify potential drugs for high-risk group patients. Two subtypes were identified. C1 exhibited a higher level of CNV amplification, immune score, stromal score, aneuploidy score, homologous recombination deficiency, intratumor heterogeneity, single-nucleotide variant neoantigens, and tumor mutational burden compared to C2. C2 showed a better survival outcome and had a high level of gamma delta T cell and activated B cell infiltration. The risk signature consisting of four genes (HELLS, ZWINT, ABCC5, and TPSB2) was developed (area under the curve = 0.780) and was found to be an independent prognostic factor for overall survival in PCa patients. Four CTRP-derived and four PRISM-derived compounds were identified for high-risk patients. The anoikis-related prognostic model developed in this study could be a useful tool for clinical decision-making. This study may provide a new perspective for the treatment of anoikis-related PCa.