Consensus Clustering Analysis Research Articles

ALYREF m5C RNA methylation reader predicts bladder cancer prognosis by regulating the tumor immune microenvironment.

5-Methylcytidine (m5C) methylation is a recently emerging epigenetic modification that is closely related to tumor proliferation, occurrence, and metastasis. This study aimed to investigate the clinicopathological characteristics and prognostic value of m5C regulators in bladder cancer (BLCA), and their correlation with the tumor immune microenvironment. Thirteen m5C RNA methylation regulators were analyzed using RNA-sequencing and corresponding clinical information obtained from the TCGA database. The Cluster Profiler package was used to analyze the gene ontology function of potential targets and enriched the Kyoto Encyclopedia of Genes and Genomes pathway. Kaplan-Meier survival analysis was used to compare survival differences using the log-rank test and univariate Cox proportional hazards regression. The correlation between signature prognostic m5C regulators and various immune cells was analyzed. Univariate and multivariate Cox regression analyses identified independence of the ALYREF gene signature. Nine out of the 13 m5C RNA methylation regulators were differentially expressed in BLCA and normal samples and were co-expressed. These 9 regulators were associated with clinicopathological tumor characteristics, particularly high or low tumor risk, pT or pTNM stage, and migration. Consensus clustering analysis divides the BLCA samples into 4 clusters. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment annotation and gene ontology function analysis identified 273 upregulated and 594 downregulated genes in BLCA. Notably, only ALYREF was significantly correlated with OS (P < .05). ALYREF exhibited significant infiltration levels in macrophage cells. Therefore, we constructed a nomogram for ALYREF as an independent prognostic factor. Additionally, we observed that both the mRNA and protein levels of ALYREF were upregulated, and immunofluorescence showed that ALYREF was mainly distributed in nuclear speckles. ALYREF overexpression was significantly associated with poor OS. Our findings demonstrated the potential of ALYREF to predict clinical prognostic risks in BLCA patients and regulate the tumor immune microenvironment. As such, ALYREF may serve as a novel prognostic indicator in BLCA patients.

Single cell RNA-sequencing reveals the cellular senescence of placental mesenchymal stem/stromal cell in preeclampsia

IntroductionPreeclampsia (PE) is a severe obstetric complication closely associated with placental dysfunction. Placental mesenchymal stem/stromal cells (PMSCs) modulate placental development while PE PMSCs are excessively senescent to disturb placental function. Nevertheless, the senescence mechanism of PE PMSCs remains unclear. MethodsPE-related single-cell RNA sequencing datasets (GSE173193), data of chip detection (GSE99007) and bulk transcriptome RNA sequencing datasets (GSE75010) were extracted from the GEO database. Firstly, the functional enrichment analyses of the differentially expressed genes (DEGs) in PMSCs were conducted. Then, the clusters of PE PMSCs were distinguished according to the expressions of senescence-related genes (SRGs) by consensus clustering analysis. Cell cycle analysis, senescence β-galactosidase, Transwell, and tube formation were conducted. Next, the expressions of the senescence-associated secretory phenotype (SASPs) were displayed. The characteristic genes of PE were screened by the least absolute shrinkage and selection operator analysis. CTSZ was suppressed in PMSCs and the cellular senescence levels were evaluated. ResultsIn this study, The DEGs in PMSCs were closely associated with cellular senescence. The score of SRGs was significantly higher and most of the SASPs were abnormally expressed in the senescent group. Seven characteristic genes of PE were identified, thereinto, CTSZ reduction may accelerate the senescence in PMSCs in vitro. DiscussionCombined with bioinformatic analysis and lab experiments, our study emphatically revealed the abnormal cellular senescence in PE PMSCs, in which CTSZ, one of the PE characteristic genes, regulated the cellular senescence levels in PMSCs. These findings might help to deepen the understanding of the senescence mechanism of PMSCs in PE.

Identification of PPAR-related differentially expressed genes liver hepatocellular carcinoma and construction of a prognostic model based on data analysis and molecular docking.

Liver hepatocellular carcinoma (LIHC) is a significant global health issue with limited treatment options. In this study, single-cell RNA sequencing (scRNA-seq) data were used to explore the molecular mechanisms of LIHC development and identify potential targets for therapy. The expression of peroxisome proliferator-activated receptors (PPAR)-related genes was analysed in LIHC samples, and primary cell populations, including natural killer cells, T cells, B cells, myeloid cells, endothelial cells, fibroblasts and hepatocytes, were identified. Analysis of the differentially expressed genes (DEGs) between normal and tumour tissues revealed significant changes in gene expression in various cell populations. PPAR activity was evaluated using the 'AUCell' R software, which indicated higher scores in the normal versus the malignant hepatocytes. Furthermore, the DEGs showed significant enrichment of pathways related to lipid and glucose metabolism, cell development, differentiation and inflammation. A prognostic model was then constructed using 8 PPARs-related genes, including FABP5, LPL, ACAA1, PPARD, FABP4, PLIN1, HMGCS2 and CYP7A1, identified using least absolute shrinkage and selection operator-Cox regression analysis, and validated in the TCGA-LIHC, ICGI-LIRI and GSE14520 datasets. Patients with low-risk scores had better prognosis in all cohorts. Based on the expression of the eight model genes, two clusters of patients were identified by ConsensusCluster analysis. We also predicted small-molecule drugs targeting the model genes, and identified perfluorohexanesulfonic acid, triflumizole and perfluorononanoic acid as potential candidates. Finally, wound healing assay confirmed that PPARD can promote the migration of liver cancer cells. Overall, our study offers novel perspectives on the molecular mechanisms of LIHC and potential areas for therapeutic intervention, which may facilitate the development of more effective treatment regimens.

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.

Development of a novel six DNA damage response-related prognostic signature in osteosarcoma.

DNA damage response (DDR) plays a vital role in the development of cancer. Nevertheless, in osteosarcoma, the potential of DDR-related genes (DDRGs) remains unclear. Thus, the current research is intended to investigate the mechanisms of DDRGs in the development of osteosarcoma and to explore potential DDR-related biomarkers in forecasting the prognosis of osteosarcoma patients. The osteosarcoma genomic data from TCGA, GEO and cBioPortal databases were utilized for screening and identification of differentially expressed DDRGs (DEDDRGs). Consensus clustering analysis was performed to identify different subtypes of osteosarcoma based on the expressions of DDRGs. Key DEDRRGs were identified by overlapping DEDRRGs between different subtypes and DEDRRGs between tumor and control samples. Univariate, as well as LASSO regressions, were further applied to obtain robust prognostic signatures. GSVA and ssGSEA analysis were implemented to explore the underlying mechanisms of prognostic DDRG signature in regulating osteosarcoma. In addition, the drug sensitivity of patients in low- and high-risk groups was evaluated using pRRophetic algorithm. A total of 43 key DEDRRGs were identified. Followed by univariate Cox along with LASSO regression analyses, CDK6, CSF1R, EGFR, ERBB4, GATA3 and SOCS1 were identified as prognostic signatures in osteosarcoma. Cox regressions revealed that the risk score was an independent prognostic factor in osteosarcoma. DDR may affect osteosarcoma via regulating immune microenvironment along with influencing cell proliferation, migration, adhesion and apoptosis. The chemotherapeutic response between patients in low- and high-risk groups was much different. The role of DDRGs in osteosarcoma and identified six DDR-linked biomarkers for forecasting the prognosis of osteosarcoma patients. Our outcomes enhanced the understanding of DDR-related molecular mechanisms involved in osteosarcoma and provided potential therapeutic targets for osteosarcoma patients.

An androgen receptor-based signature to predict prognosis and identification of ORC1 as a therapeutical target for prostate adenocarcinoma.

Aberrant activation of androgen receptor (AR) signaling plays a crucial role in the progression of prostate adenocarcinoma (PRAD) and contributes significantly to the development of enzalutamide resistance. In this study, we aimed to identify a novel AR-driven signature that can predict prognosis and endows potentially reveal novel therapeutic targets for PRAD. The Seurat package was used to preprocess the single-cell RNA sequencing (scRNA-seq). Differentially expressed genes were visualized using limma and pheamap packages. LASSO and multi-variate Cox regression models were established using glmnet package. The package "Consensus Cluster Plus" was utilized to perform the consensus clustering analysis. The biological roles of origin recognition complex subunit 1 (ORC1) in PRAD were determined by gain- and loss-of-function studies in vitro and in vivo. We characterized the scRNA-seq data from GSE99795 and identified 10 AR-associated genes (ARGs). The ARGs model was trained and validated in internal and external cohorts. The ARGs were identified as an independent hazard factor in PRAD and correlated with clinical risk characteristics. In addition, the ARGs were found to be correlated with somatic tumor mutation burden (TMB) levels. Two groups that have distinct prognostic and molecular features were identified through consensus clustering analysis. ORC1 was identified as a critical target among these ARGs, and it ORC1 promoted proliferation and stem-like properties of PRAD cells. Chromatin immunoprecipitation (ChIP)-qPCR assay confirmed that AR could directly bind the promoter of ORC1. Activated AR/ORC1 axis contributed to enzalutamide resistance, and targeting ORC1 rendered PRAD cells more susceptible to enzalutamide. This study defines an AR-driven signature that AR activates ORC1 expressions to promote PRAD progression and enzalutamide resistance, which may provide novel targets for PRAD treatment.

A Multi-Gene Signature of Non-Muscle-Invasive Bladder Cancer Identifies Patients Who Respond to Immunotherapies Including Bacillus Calmette-Guérin and Immune Checkpoint Inhibitors.

Approximately 75% of bladder cancer cases originate as non-muscle-invasive bladder cancer (NMIBC). Despite initial diagnosis, NMIBC commonly recurs, with up to 45% advancing to muscle-invasive bladder cancer (MIBC) and metastatic disease. Treatment for high-risk NMIBC typically includes procedures like transurethral resection and, depending on recurrence risk, intravesical chemotherapy or immunotherapy such as Bacillus Calmette-Guérin (BCG). However, persistent shortages of BCG necessitate alternative first-line treatments. We aim to use a multi-gene signature in high-risk NMIBC patients to determine whether patients may benefit from immune checkpoint inhibitors (ICIs) as an alternative to BCG and to evaluate their clinical utility. The multi-gene signature obtained from the three independent NMIBC cohorts was applied to stratify the UROMOL2016 cohort (n = 476) using consensus clustering. Each subtype was distinguished by biological pathway analysis. Validation analysis using a machine learning algorithm was performed in six independent cohorts including the BRS (n = 283) cohort treated with BCG and the IMvigor210 (n = 298) clinical trials treated with PD-L1 inhibitors. Based on consensus cluster analysis, NMIBC patients in the UROMOL2016 cohort were classified into three classes exhibiting distinguished characteristics, including DNA damage repair (DDR). Survival analysis showed that the NMIBC-DDR class had the highest rates of disease progression (progression-free survival, p = 0.002 by log-rank test) in the UROMOL cohort and benefited from BCG and ICIs (respectively, p = 0.02 and p = 0.03 by log-rank test). This study suggests that the multi-gene signature may have a role in identifying high-risk NMIBC patients and improving the responsiveness of ICIs. Additionally, we propose immunotherapy as a new first-line treatment for patients with high-risk NMIBC because of the shortage of BCG supply. It is important to help more patients prioritize cancer immunotherapy.

Abstract 6245: Missed opportunity at the subcellular level: Enhancing the utility of cellular imaging modality in spatial transcriptomic profiling of tumor tissues

Abstract Mini-bulk or single-spot spatial transcriptomic technologies, such as GeoMX Digital Spatial Profiler (DSP), have revolutionized our ability to probe spatial heterogeneity and examine transcripts at a subcellular level. In cancer research, this level of granularity is crucial for uncovering the distinct gene expression signatures within specific compartments or structures of interest such as tumor, immune, stroma, and tertiary lymphoid structures. However, in most GeoMX DSP studies, the spatial information obtained from multiplex immunofluorescence imaging has been primarily used for identifying the regions of interest (ROI), rather than as an integral part of the downstream transcriptomic data interpretation. To fix this missed opportunity, we developed a machine learning based analytical framework to fully leverage the rich spatial context provided by the in situ imaging modality. The framework has two main functionalities. (1) ROI cropping and cell segmentation: our framework enables the automated cropping of ROI images, followed by a cell segmentation process utilizing a multi-color-optimized deep learning model pre-trained based on the TissueNet. (2) Cell typing and integrative spatial analysis: the pipeline extracts cell-level morphological features (e.g. cell size and circularity), and AI-driven characteristics. These features are subsequently employed for cell typing and the quantification of cell mixture. The framework is equipped to perform a series of advanced spatial analyses, such as spatial metrics calculation and consensus clustering analysis with the matched gene expression data. To validate the framework, we conducted GeoMX DSP analyses on samples obtained from bladder cancer and upper tract urothelial carcinoma, encompassing 56 ROIs annotated by pathologists. Our results demonstrated high accuracy, with the calculated tumor-immune proportions aligning precisely with the original annotations. We further compared the cell deconvolution results of our framework with those obtained using SpatialDecon. Consistency was noted across both methods, with a high correlation reaching 0.90. However, we observed that SpatialDecon tends to underestimate tumor purity in tumor core regions, with discrepancies as large as 0.30, and overestimating tumor purity in stromal regions. Additionally, analysis of ROIs in tumor margins revealed that SpatialDecon consistently overestimates the overall proportions of immune cells. In conclusion, our results underscore the importance of integrating in situ imaging with subcellular-level spatial transcriptomics for a more accurate and reliable analysis of tumor tissues. Our approach provides critical insights into the tumor microenvironment and cellular interactions, with significant implications for both research and clinical applications in oncology. Citation Format: Xiaofei Song, Xiaoqing Yu, Carlos M. Moran-Segura, G Daniel Grass, Roger Li, Xuefeng Wang. Missed opportunity at the subcellular level: Enhancing the utility of cellular imaging modality in spatial transcriptomic profiling of tumor tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6245.

Abstract 1915: A scalable pancancer antigen target discovery platform for precision oncology

Abstract With the emergence of precision oncology as a new paradigm in cancer care, there is an urgent need to develop tools capable of mining the massive amounts of rich omic data generated every year. To support target discovery programs at scale, we developed a pan-cancer bioinformatics platform combining patient data with extensive biological and pharmaceutical knowledge for the identification and prioritization of novel antigen targets. Our pipeline was first validated with the discovery of new antigen targets amenable to CAR-T therapy for relapsed/refractory multiple myeloma. Here, we are exploring AML, a type of leukemia with several unmet needs. First, we identified and integrated 36 relevant microarray datasets from the GEO database using a proprietary data identification and integration pipeline. The clinical data was curated using our proprietary oncology ontology, machine learning models, and domain expert quality control processes. The molecular data was normalized and the datasets aggregated into a virtual patient cohort of unprecedented size and quality, comprising 2,995 AML patients and 220 healthy controls. To both find a clinically relevant patient sub-population and reduce the cohort heterogeneity, AML patients were stratified based on their transcriptomic profile using a consensus clustering analysis, which we interpreted thanks to the curated clinical data. Among multiple others, we identified a highly stable cluster enriched in AML-M3 patients, a very distinct and aggressive subtype of AML caused by a t(15;17) translocation. A differential gene expression analysis was performed comparing this cluster with the control group and 574 genes were found to be overexpressed. We then applied proteomic filters to exclusively focus on cell surface-bound protein targets demonstrating an acceptable level of anticipated cytotoxicity. Finally, the 23 short-listed antigen targets were prioritized with additional multi-omic patient and cell line data to optimize their safety and efficacy profiles as well as expression robustness. Interestingly, well-known targets, such as CD96 and ABCC1, were found in the top targets. Developing scalable pipelines will be instrumental in the advent of precision oncology. Combining unbiased data-driven tools with cancer biology-driven approaches, our state-of-the-art pipeline can be used for any cancer type and antigen-targeting modality, including CAR-T and antibody-based therapies. The present study illustrates the potential of our platform when applied to AML, one of the most heterogeneous groups of neoplastic disorders. Leveraging our large and unique AML patient cohort, we were not only able to detect a relevant subgroup of patients, but also identified novel antigen target candidates for this specific population, which were then prioritized based on domain expertise. A broader study addressing other cancer indications, including solid tumors, is underway. Citation Format: Eleonore Fox, Guillaume Appe, Abdelkader Behdenna, Lea Meunier, Akpeli Nordor, Solene Weill, Camille Marijon. A scalable pancancer antigen target discovery platform for precision oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1915.

Abstract 4357: Examining the role of end-stage kidney as a potential intermediate stage in the development of renal cell carcinomas; understanding the pathogenesis allowing for early detection

Abstract Introduction: Clear cell renal cell carcinoma (CCRCC) and papillary renal cell carcinoma (PRCC) are both thought to arise from the renal proximal tubules. End stage renal disease (ESRD) patients have an increased incidence of developing PRCC and CCRCC. Some studies showed a progenitor cell population that is inherently present in renal tubules, and is significantly increased after renal injury. The studies have also illustrated similarities between these progenitor cells and PRCC &amp; CCRCC; hypothesizing that they are the cell of origin of these tumors. This trend was not observed in other renal cell carcinomas such as chromophobe renal cell carcinoma (chRCC). This study aims to further examine the possible role of ESRD in the development of PRCC and CCRCC to understand this initial step of renal cell carcinoma pathogenesis. Methods: A cohort of n = 39 PRCC, 25 CCRCC, 63 normal, 10 end-stage and 18 chRCC cases were selected. RNA sequencing was performed and differential-gene-expression analysis was conducted between normal, ESRD and tumor using DESeq2 software. Pathway analysis was performed using GSEA. Genes from oncogenic pathways that have been studied to be involved in renal cancer development were selected to perform consensus clustering analysis on Genepattern. Results: Oncogenic and developmental pathways enriched in ESRD are consistently overlapping with that of CCRCC and PRCC tumors. 50% of end-stage developmental pathways are found in both CCRCC and PRCC but only 4.5% in chRCC. 62.4% and 46.4% of end-stage oncogenic pathways are found in CCRCC and PRCC respectively but only 16% in chRCC. Clustering analysis showed ESRD cases clustered together with CCRCC and PRCC groups and not with chRCC. Overall, the ESRD cases exhibited a gene expression profile more closely resembling that of CCRCC and PRCC than chRCC. Conclusion: Our findings support the hypothesis that ESRD provides a favorable environment for tumor growth promoting pathogenesis of both PRCC and CCRCC. The enrichment of the same developmental pathways in both ESRD and the tumors suggests that the progenitor cell population in the renal tubules, mentioned above, could be the origin of both PRCC and CCRCC lesions. Further analysis would allow the uncovering of the pathways that lead to tumor progression from these cells of origin, and can be used to determine biomarkers for early detection of CCRCC and PRCC. This is of particular benefit for those with increased risk as the ESRD patients. Citation Format: Mingyuan Wan, Vincent Castillo, Rola Saleeb. Examining the role of end-stage kidney as a potential intermediate stage in the development of renal cell carcinomas; understanding the pathogenesis allowing for early detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4357.

Machine learning framework develops neutrophil extracellular traps model for clinical outcome and immunotherapy response in lung adenocarcinoma.

Neutrophil extracellular traps (NETs) are novel inflammatory cell death in neutrophils. Emerging studies demonstrated NETs contributed to cancer progression and metastases in multiple ways. This study intends to provide a prognostic NETs signature and therapeutic target for lung adenocarcinoma (LUAD) patients. Consensus cluster analysis performed by 38 reported NET-related genes in TCGA-LUAD cohorts. Then, WGCNA network was conducted to investigate characteristics genes in clusters. Seven machine learning algorithms were assessed for training of the model, the optimal model was picked by C-index and 1-, 3-, 5-year ROC value. Then, we constructed a NETs signature to predict the overall survival of LUAD patients. Moreover, multi-omics validation was performed based on NETs signature. Finally, we constructed stable knockdown critical gene LUAD cell lines to verify biological functions of Phospholipid Scramblase 1 (PLSCR1) in vitro and in vivo. Two NETs-related clusters were identified in LUAD patients. Among them, C2 cluster was provided as "hot" tumor phenotype and exhibited a better prognosis. Then, WGCNA network identified 643 characteristic genes in C2 cluster. Then, Coxboost algorithm proved its optimal performance and provided a prognostic NETs signature. Multi-omics revealed that NETs signature was involved in an immunosuppressive microenvironment and predicted immunotherapy efficacy. In vitro and in vivo experiments demonstrated that knockdown of PLSCR1 inhibited tumor growth and EMT ability. Besides, cocultural assay indicated that the knockdown of PLSCR1 impaired the ability of neutrophils to generate NETs. Finally, tissue microarray (TMA) for LUAD patients verified the prognostic value of PLSCR1 expression. In this study, we focus on emerging hot topic NETs in LUAD. We provide a prognostic NETs signature and identify PLSCR1 with multiple roles in LUAD. This work can contribute to risk stratification and screen novel therapeutic targets for LUAD patients.

An Integrated Machine Learning Framework Identifies Prognostic Gene Pair Biomarkers Associated with Programmed Cell Death Modalities in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a common and lethal urological malignancy for which there are no effective personalized therapeutic strategies. Programmed cell death (PCD) patterns have emerged as critical determinants of clinical prognosis and immunotherapy responses. However, the actual clinical relevance of PCD processes in ccRCC is still poorly understood. We screened for PCD-related gene pairs through single-sample gene set enrichment analysis (ssGSEA), consensus cluster analysis, and univariate Cox regression analysis. A novel machine learning framework incorporating 12 algorithms and 113 unique combinations were used to develop the cell death-related gene pair score (CDRGPS). Additionally, a radiomic score (Rad_Score) derived from computed tomography (CT) image features was used to classify the CDRGPS status as high or low. Finally, we conclusively verified the function of PRSS23 in ccRCC. The CDRGPS was developed through an integrated machine learning approach that leveraged 113 algorithm combinations. CDRGPS represents an independent prognostic biomarker for overall survival and demonstrated consistent performance between training and external validation cohorts. Moreover, CDRGPS showed better prognostic accuracy compared to seven previously published cell death-related signatures. In addition, patients classified as high-risk by CDRGPS exhibited increased responsiveness to tyrosine kinase inhibitors (TKIs), mammalian Target of Rapamycin (mTOR) inhibitors, and immunotherapy. The Rad_Score demonstrated excellent discrimination for predicting high versus low CDRGPS status, with an area under the curve (AUC) value of 0.813 in the Cancer Imaging Archive (TCIA) database. PRSS23 was identified as a significant factor in the metastasis and immune response of ccRCC, thereby validating experimental in vitro results. CDRGPS is a robust and non-invasive tool that has the potential to improve clinical outcomes and enable personalized medicine in ccRCC patients.

The cell death-related genes machine learning model for precise therapy and clinical drug selection in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the prevailing subtype of hepatocellular malignancy. While previous investigations have evidenced a robust link with programmed cell death (PCD) and tumorigenesis, a comprehensive inquiry targeting the relationship between multiple PCDs and HCC remains scant. Our aim was to develop a predictive model for different PCD patterns in order to investigate their impact on survival rates, prognosis and drug response rates in HCC patients. We performed functional annotation and pathway analysis on identified PCD-related genes (PCDRGs) using multiple bioinformatics tools. The prognostic value of these PCDRGs was verified through a dataset obtained from GEO. Consensus clustering analysis was utilized to elucidate the correlation between diverse PCD clusters and pertinent clinical characteristics. To comprehensively uncover the distinct PCD regulatory patterns, our analysis integrated gene expression profiling, immune cell infiltration and enrichment analysis. To predict survival differences in HCC patients, we established a PCD model. To enhance the clinical applicability for the model, we developed a highly accurate nomogram. To address the treatment of HCC, we identified several promising chemotherapeutic agents and novel targeted drugs. These drugs may be effective in treating HCC and could improve patient outcomes. To develop a cell death feature for HCC patients, we conducted an analysis of 12 different PCD mechanisms using eligible data obtained from public databases. Through this analysis, we were able to identify 1254 PCDRGs likely to contribute to cell death on HCC. Further analysis of 1254 PCDRGs identified 37 genes with prognostic value in HCC patients. These genes were then categorized into two PCD clusters A and B. The categorization was based on the expression patterns of the genes in the different clusters. Patients in PCD cluster B had better survival probabilities. This suggests that PCD mechanisms, as represented by the genes in cluster B, may have a protective effect against HCC progression. Furthermore, the expression of PCDRGs was significantly higher in PCD cluster A, indicating that this cluster may be more closely associated with PCD mechanisms. Furthermore, our observations indicate that patients exhibiting elevated tumour mutation burden (TMB) are at an augmented risk of mortality, in comparison to those displaying low TMB and low-risk statuses, who are more likely to experience prolonged survival. In addition, we have investigated the potential distinctions in the susceptibility of diverse risk cohorts towards emerging targeted therapies, designed for the treatment of HCC. Moreover, our investigation has shown that AZD2014, SB505124, LJI308 and OSI-207 show a greater efficacy in patients in the low-risk category. Conversely, for the high-risk group patients, PD173074, ZM447439 and CZC24832 exhibit a stronger response. Our findings suggest that the identification of risk groups and personalized treatment selection could lead to better clinical outcomes for patients with HCC. Furthermore, significant heterogeneity in clinical response to ICI therapy was observed among HCC patients with varying PCD expression patterns. This novel discovery underscores the prospective usefulness of these expression patterns as prognostic indicators for HCC patients and may aid in tailoring targeted treatment for those of distinct risk strata. Our investigation introduces a novel prognostic model for HCC that integrates diverse PCD expression patterns. This innovative model provides a novel approach for forecasting prognosis and assessing drug sensitivity in HCC patients, driving a more personalized and efficacious treatment paradigm, elevating clinical outcomes. Nonetheless, additional research endeavours are required to confirm the model's precision and assess its potential to inform clinical decision-making for HCC patients.

Tumor-infiltrating immune cells and survival in head and neck squamous cell carcinoma: a retrospective computational study

The immune infiltration profiles of the tumor microenvironment have effects on the prognosis of head and neck squamous cell carcinoma (HNSCC). Whereas, HNSCC is a heterogeneous group of tumors, but past work has not taken this into consideration. Herein, we investigate the associations between survival and the function of immune cells in different tumorigenic sites of HNSCC. 1149 samples of HNSCC were collected from publicly accessible databases. Based on gene expression data, CIBERSORTx was applied to determine the proportion of 22 immune cell subpopulations. In the Cox regression model, the associations between overall survival, disease-free survival, and immune cells were examined, modeling gene expression and immune cell proportion as quartiles. Consensus cluster analysis was utilized to uncover immune infiltration profiles. Regardless of tumor sites, CD8+ T cells and activated CD4 memory T cells were associated with favorable survival, while eosinophils were the opposite. The survival of the hypopharynx, oral cavity, and larynx subsites was somewhat affected by immune cells, while the survival of the oropharynx subsite potentially was the most impacted. High expression of TIGIT, CIITA, and CXCR6 was linked to better survival, mainly in the oropharynx subsite. Immune cell clusters with four distinct survival profiles were discovered, of which the cluster with a high CD8+ T cell content had a better prognosis. The immune-infiltration pattern is related to the survival of HNSCC to varying degrees depending on the tumor sites; forthcoming studies into immune-mediated infiltration profiles will lay the groundwork for treating HNSCC with precision therapy.

Development and validation of a novel biomarker panel for Crohn's disease and rheumatoid arthritis diagnosis and treatment.

Crohn's disease (CD) and rheumatoid arthritis (RA) are immune-mediated inflammatory diseases. However, the molecular mechanisms linking these two diseases remain unclear. To identify shared core genes between CD and RA, we employed differential gene analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. Functional annotation of these core biomarkers was performed using consensus clustering and gene set enrichment analysis. We also constructed a protein-protein network and a miRNA-mRNA network using multiple databases, and potential therapeutic agents targeting the core biomarkers were predicted. Finally, we confirmed the expression of the genes in the biomarker panel in both CD and RA using quantitative PCR. A total of five shared core genes, namely C-X-C motif chemokine ligand 10 (CXCL10), C-X-C motif chemokine ligand 9 (CXCL9), aquaporin 9 (AQP9), secreted phosphoprotein 1 (SPP1), and metallothionein 1M (MT1M), were identified as core biomarkers. These biomarkers activate classical pro-inflammatory and immune signaling pathways, influencing immune cell aggregation. Additionally, testosterone was identified as a potential therapeutic agent targeting the biomarkers identified in this study. The expression of genes in the biomarker panel in CD and RA was confirmed through quantitative PCR. Our study revealed some core genes shared between CD and RA and established a novel biomarker panel with potential implications for the diagnosis and treatment of these diseases.

MOCS, a novel classifier system integrated multimoics analysis refining molecular subtypes and prognosis for skin melanoma

Purpose: The present investigation focuses on Skin Cutaneous Melanoma (SKCM), a melanocytic carcinoma characterized by marked aggression, significant heterogeneity, and a complex etiological background, factors which collectively contribute to the challenge in prognostic determinations. We defined a novel classifier system specifically tailored for SKCM based on multiomics. Methods: We collected 423 SKCM samples with multi omics datasets to perform a consensus cluster analysis using 10 machine learning algorithms and verified in 2 independent cohorts. Clinical features, biological characteristics, immune infiltration pattern, therapeutic response and mutation landscape were compared between subtypes. Results: Based on consensus clustering algorithms, we identified two Multi-Omics-Based-Cancer-Subtypes (MOCS) in SKCM in TCGA project and validated in GSE19234 and GSE65904 cohorts. MOCS2 emerged as a subtype with poor prognosis, characterized by a complex immune microenvironment, dysfunctional anti-tumor immune state, high cancer stemness index, and genomic instability. MOCS2 exhibited resistance to chemotherapy agents like erlotinib and sunitinib while sensitive to rapamycin, NSC87877, MG132, and FH355. Additionally, ELSPBP1 was identified as the target involving in glycolysis and M2 macrophage infiltration in SKCM. Conclusions: MOCS classification could stably predict prognosis of SKCM; patients with a high cancer stemness index combined with genomic instability may be predisposed to an immune exhaustion state. Communicated by Ramaswamy H. Sarma

Molecular characterization of Golgi apparatus-related genes indicates prognosis and immune infiltration in osteosarcoma.

The Golgi apparatus (GA) is crucial for protein synthesis and modification, and regulates various cellular processes. Dysregulation of GA can lead to pathological conditions like neoplastic growth. GA-related genes (GARGs) mutations are commonly found in cancer, contributing to tumor metastasis. However, the expression and prognostic significance of GARGs in osteosarcoma are yet to be understood. Gene expression and clinical data of osteosarcoma patients were obtained from the TARGET and GEO databases. A consensus clustering analysis identified distinct molecular subtypes based on GARGs. Discrepancies in biological processes and immunological features among the subtypes were explored using GSVA, ssGSEA, and Metascape analysis. A GARGs signature was constructed using Cox regression. The prognostic value of the GARGs signature in osteosarcoma was evaluated using Kaplan-Meier curves and a nomogram. Two GARG subtypes were identified, with Cluster A showing better prognosis, immunogenicity, and immune cell infiltration than Cluster B. A novel risk model of 3 GARGs was established using the TARGET dataset and validated with independent datasets. High-risk patients had poorer overall survival, and the GARGs signature independently predicted osteosarcoma prognosis. Combining risk scores and clinical characteristics in a nomogram improved prediction performance. Additionally, we discovered Stanniocalcin-2 (STC2) as a significant prognostic gene highly expressed in osteosarcoma and potential disease biomarker. Our study revealed that patients with osteosarcoma can be divided into two GARGs subgroups. Furthermore, we have developed a GARGs prognostic signature that can accurately forecast the prognosis of osteosarcoma patients.

Multi-omics analysis elucidates the relationship between intratumor microbiome and host immune heterogeneity in breast cancer.

Research has indicated that intratumor microbiomes affect the occurrence, progression, and therapeutic response in many cancer types by influencing the immune system. We aim to evaluate the characteristics of immune-related intratumor microbiomes (IRIMs) in breast cancer (BC) and search for potential prognosis prediction factors and treatment targets. The clinical information, microbiome data, transcriptomics data of The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) patients were obtained from Kraken-TCGA-Raw-Data and TCGA portal. The core tumor-infiltrating immune cell was identified using univariate Cox regression analysis. Based on consensus clustering analysis, BC patients were categorized into two immune subtypes, referred to as immune-enriched and immune-deficient subtypes. The immune-enriched subtype, characterized by higher levels of immune infiltration of CD8+ T and macrophage M1 cells, demonstrated a more favorable prognosis. Furthermore, significant differences in alpha-diversity and beta-diversity were observed between the two immune subtypes, and the least discriminant analysis effect size method identified 33 types of IRIMs. An intratumor microbiome-based prognostic signature consisting of four prognostic IRIMs (Acidibacillus, Succinimonas, Lachnoclostridium, and Pseudogulbenkiania) was constructed using the Cox proportional-hazard model, and it had great prognostic value. The prognostic IRIMs were correlated with immune gene expression and the sensitivity of chemotherapy drugs, specifically tamoxifen and docetaxel. In conclusion, our research has successfully identified two distinct immune subtypes in BC, which exhibit contrasting prognoses and possess unique epigenetic and intratumor microbiomes. The critical IRIMs were correlated with prognosis, tumor-infiltrating immune cell abundance, and immunotherapeutic efficacy in BC. Consequently, this study has identified potential IRIMs as biomarkers, providing a novel therapeutic approach for treating BC.IMPORTANCERecent research has substantiated the presence of the intratumor microbiome in tumor immune microenvironment, which could influence tumor occurrence and progression, as well as provide new opportunities for cancer diagnosis and treatment. This study identified the critical immune-related intratumor microbiome (Acidibacillus, Succinimonas, Lachnoclostridium, and Pseudogulbenkiania), which were correlated with prognosis, tumor-infiltrating immune cell abundance, and immunotherapeutic efficacy in breast cancer and might be the novel target to regulate immunotherapy in BC.

Identification of circadian rhythm-related gene classification patterns and immune infiltration analysis in heart failure based on machine learning

BackgroundCircadian rhythms play a key role in the failing heart, but the exact molecular mechanisms linking changes in the expression of circadian rhythm-related genes to heart failure (HF) remain unclear. MethodsBy intersecting differentially expressed genes (DEGs) between normal and HF samples in the Gene Expression Omnibus (GEO) database with circadian rhythm-related genes (CRGs), differentially expressed circadian rhythm-related genes (DE-CRGs) were obtained. Machine learning algorithms were used to screen for feature genes, and diagnostic models were constructed based on these feature genes. Subsequently, consensus clustering algorithms and non-negative matrix factorization (NMF) algorithms were used for clustering analysis of HF samples. On this basis, immune infiltration analysis was used to score the immune infiltration status between HF and normal samples as well as among different subclusters. Gene Set Variation Analysis (GSVA) evaluated the biological functional differences among subclusters. Results13 CRGs showed differential expression between HF patients and normal samples. Nine feature genes were obtained through cross-referencing results from four distinct machine learning algorithms. Multivariate LASSO regression and external dataset validation were performed to select five key genes with diagnostic value, including NAMPT, SERPINA3, MAPK10, NPPA, and SLC2A1. Moreover, consensus clustering analysis could divide HF patients into two distinct clusters, which exhibited different biological functions and immune characteristics. Additionally, two subgroups were distinguished using the NMF algorithm based on circadian rhythm associated differentially expressed genes. Studies on immune infiltration showed marked variances in levels of immune infiltration between these subgroups. Subgroup A had higher immune scores and more widespread immune infiltration. Finally, the Weighted Gene Co-expression Network Analysis (WGCNA) method was utilized to discern the modules that had the closest association with the two observed subgroups, and hub genes were pinpointed via protein-protein interaction (PPI) networks. GRIN2A, DLG1, ERBB4, LRRC7, and NRG1 were circadian rhythm-related hub genes closely associated with HF. ConclusionThis study provides valuable references for further elucidating the pathogenesis of HF and offers beneficial insights for targeting circadian rhythm mechanisms to regulate immune responses and energy metabolism in HF treatment. Five genes identified by us as diagnostic features could be potential targets for therapy for HF.

Creation of signatures and identification of molecular subtypes of glioblastoma based on disulfidptosis-related genes for predicting patient prognosis and immunological activity

BackgroundIn recent times, disulfidptosis, an intricate form of cellular demise, has garnered attention due to its impact on prognosis, tumor progression and treatment response. Nevertheless, the exact significance of disulfidptosis-related genes (DisRGs) in glioblastoma (GBM) remains enigmatic. MethodsThe GEO and TCGA databases provided transcriptional and clinically relevant data on tumor samples, while the GTEx database provided data on healthy tissues. Disulfidptosis-related genes (DisRGs) were procured from previous scholarly investigations. The expression profile of DisRGs was initially scrutinized among patients diagnosed with GBM, subsequent to which their prognostic value was explored. Through consensus clustering, we constructed DisRGs-related clusters and gene subtypes. Our results established that the DisRG-related clusters had differentially expressed genes, resulting in a DisulfidptosisScore model, which had a positive prognostic value. ResultsThe differential expression profile of 24 DisRGs between GBM samples and healthy samples was acquired. Through consensus cluster analysis, two distinct disulfidptosis subtypes, namely DisRGcluster A and DisRGcluster B, were identified. Then, the DisulfidptosisScore model including 4 characteristic genes was constructed.Notably, patients with GBM assigned with lower score demonstrated a considerably longer overall survival (OS) compared to those with higher score. ConclusionWe have effectively devised a prognostic model associated with disulfidptosis, presenting autonomous prognostic predictions for patients with GBM. These findings serve as a valuable addition to the current comprehension of disulfidptosis and offer fresh theoretical substantiation for the development of enhanced treatment strategies.