The Cancer Genome Atlas Dataset Research Articles

Construction of a New Ferroptosis-related Prognosis Model for Survival Prediction in Colorectal Cancer.

This study was designed to develop a ferroptosis-related gene signature for guiding the prognostic prediction in colorectal cancer (CRC) and to explore the potential in the molecular functions of the gene signature. Ferroptosis is mainly characterized by lipid peroxide accumulation on the cell membranes in an iron-dependent manner, resulting in cellular oxidative stress, metabolic disorders, and, ultimately, cell death. This study aimed to develop a prognostic ferroptosis signature in CRC and explore its potential molecular function. The present work was designed to devise a ferroptosis signature for CRC prognosis and explore its potential molecular function. Single-cell RNA sequencing data GSE161277 and transcriptome sequencing data GSE17537 and TCGA-CRC from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases were downloaded, respectively. Quality control, dimension reduction, clustering, and clustering of single-cell RNA sequencing (scRNA- seq) data were performed using the Seurat package. A total of 259 ferroptosis-correlated genes from the FerrDb database were acquired. The single sample gene set enrichment analysis (ssGSEA) was performed to calculate the scores of genes related to ferroptosis. ESTIMATE was used to calculate immune infiltration. Independent prognostic factors were determined by performing Weighted Gene Co-Expression Network Analysis (WGCNA), univariate and Cox analyses, and Lasso analyses were used to search for independent prognostic factors. From the scRNA-seq (GSE161277) dataset, 22 cell clusters were initially identified, and according to immune cell markers, only 8 types of cells (Follicular B, central memory T cell, Epithelial, Natural killer T cell, Plasma B, M1 macrophage, Fibroblasts, and Mast cell) were finally determined to be related to CRC prognosis. The results of the scRNA-seq analysis showed that the score of ferroptosis-related genes was higher in tumour tissues and in 8 types of cells in tumour samples. In the TCGA dataset, CRC samples were divided into ferroptosis-related high scores, ferroptosis-related median scores, and ferroptosis-related low scores. Immune cell analysis revealed that ferroptosis- related high scores had the highest abundance of immune cells. An 11-gene signature was developed by WGCNA, univariate Cox, and Lasso Cox regression. The prediction ability of the signature was successfully validated in the GSE17537 dataset. A comprehensive nomogram combining the 11 signature genes and clinical parameters could effectively predict the overall survival of CRC patients. The present molecular signature established based on the 11 ferroptosis-related genes performed well in assessing CRC prognosis. The present discoveries could inspire further research on ferroptosis, providing a new direction for CRC management.

Clinicopathological characterisation of MTAP alterations in gastrointestinal cancers

BackgroundMethylthioadenosine phosphorylase (MTAP) is an essential metabolic enzyme in the purine and methionine salvage pathway. In cancer, MTAP gene copy number loss (MTAP loss) confers a selective dependency on the...

Identification and verification of a prognostic autophagy-related gene signature in hepatocellular carcinoma

This study aimed to investigate the potential of autophagy-related genes (ATGs) as a prognostic signature for HCC and explore their relationships with immune cells and immune checkpoint molecules. A total of 483 samples were collected from the GEO database (n = 115) and The Cancer Genome Atlas (TCGA) database (n = 368). The GEO dataset was used as the training set, while the TCGA dataset was used for validation. The list of ATGs was obtained from the human autophagy database (HADB). Using Cox regression and LASSO regression methods, a prognostic signature based on ATGs was established. The independent use of this prognostic signature was tested through subgroup analysis. Additionally, the predictive value of this signature for immune-related profiles was explored. Following selection through univariate Cox regression analysis and iterative LASSO Cox analysis, a total of 11 ATGs were used in the GEO dataset to establish a prognostic signature that stratified patients into high- and low-risk groups based on survival. The robustness of this prognostic signature was validated using an external dataset. This signature remained a prognostic factor even in subgroups with different clinical features. Analysis of immune profiles revealed that patients in the high-risk group exhibited immunosuppressive states characterized by lower immune scores and ESTIMATE scores, greater tumour purity, and increased expression of immune checkpoint molecules. Furthermore, this signature was found to be correlated with the infiltration of different immune cell subpopulations. The results suggest that the ATG-based signature can be utilized to evaluate the prognosis of HCC patients and predict the immune status within the tumour microenvironment (TME). However, it is important to note that this study represents a preliminary attempt to use ATGs as prognostic indicators for HCC, and further validation is necessary to determine the predictive power of this signature.

Identification of multiomics map and key biomarkers in uveal melanoma with chromosome 3 loss

Purpose: Chromosome 3 loss is an independent risk factor for uveal melanoma (UM), but its exact molecular mechanisms remain unclear. This study was designed to investigate the relationship between chromosome 3 loss and molecular alterations at multiple levels to construct a prognostic model. Methods: Forty-four UM cases with chromosome 3 loss (chr3 del group) and 36 UM cases without copy number variation on chromosome 3 (chr3 wt group) were collected from the Cancer Genome Atlas (TCGA). The TCGA dataset was subjected to a univariate Cox regression analysis to identify different expressed genes, and a subsequent random forest algorithm analysis revealed significant changes in different expressed genes, which were used to develop key biomarkers for UM. Following that, the immune cell infiltration analysis and drug sensitivity analyses were carried out. The UM cell line was then utilized to investigate the potential functions of the key biomarker via cell apoptosis, proliferation, cycle assays, WB, and RT-qPCR. Results: By analyzing the 80 cases data in TCGA, the authors unveiled molecular changes relevant to loss of chromosome 3 in UM as well as their poor survival. In addition, machine learning analysis identified three hub genes (GRIN2A, ACAN, and MMP9) as potential therapeutic targets. The differentially enriched pathways between the two groups were mainly about immune-system activity, and hub genes expression was also highly correlated with immune infiltration levels. Conclusion: Chromosome 3 loss has considerable clinical significance for UM, and GRIN2A may be useful in diagnosing, treating, and prognosticating the condition.

Application of artificial intelligence (AI) features of nuclear morphology from BLASST-1 (Bladder Cancer Signal Seeking Trial) of nivolumab, gemcitabine, and cisplatin in patients with MIBC undergoing cystectomy.

674 Background: BLASST-1 is a multi-center phase II trial evaluating neoadjuvant nivolumab (N) with gemcitabine-cisplatin (GC) for patients (pts) with MIBC undergoing radical cystectomy (RC) (NCT03294304). 41 pts with MIBC (cT2-T4a, N≤1, M0) were enrolled between Feb 2018 and June 2019; (cT2N0 90%, cT3N0 7%, cT4N1 3%). Pts received C (70mg/m2) IV on D1, G (1000mg/m2) on D1, D8, and N (360 mg) IV on D8 every 21 days for 4 cycles followed by RC within 8 wks. The primary endpoint was pathologic downstaging (PaR; ≤pT1N0). Safety, Relapse-free survival (RFS), Progression-free survival (PFS) and biomarker analyses were secondary endpoints. PaR rate was 65.8%, the pCR (≤pT is N0) rate was 49% and there were no safety concerns or delays to RC. Morphometric characteristics of the cell nucleus can be used to assess bladder cancer grading and gain insights into cellular functionalities. In this study, we sought to evaluate the ability of the AI model to identify non-responders to neoadjuvant chemo-immunotherapy. in the BLASST-1 cohort based on computerized image features of nuclear morphology and architecture on pre-treatment transurethral resection of bladder tumor (TURBT) tissues. Methods: Of the 41 pts, we had H&E images available for 34 pts, of which 23 had PaR and 11 did not have PaR and these were classified as responder (R) and non-responder (NR) groups. A machine learning model (U-net) was developed and invoked for tumor segmentation on the H&E images from the BLASST-1 cohort. A second machine learning model (HoVer-Net) was employed to segment and classify individual nuclei. A total of 408 features relating to the textural and spatial arrangement of individual cancer nuclei were extracted. The 17 most significant features, identified through the least absolute shrinkage and selection operator, were used to train a Cox regression model to predict the risk of death using 361 MIBC pts from the Cancer Genome Atlas (TCGA). This Cox model was then applied to assign a risk score to pts in BLASST-1, using a threshold learned from TCGA pts, the individual pts in BLASST-1 were assigned as either low-risk or high-risk. Results: The top identified prognostic features described the textural appearance of individual nuclei with more texture. This model accurately predicted PaR in BLASST-1, with an area under a receiver operating characteristic curve of 0.83. Overall, the pts with the same nuclear angle direction within the tumor tissue had a high chance of response to the neoadjuvant chemo-IO combination in the BLASST-1 trial. Conclusions: A computerized AI model relying on nuclear morphologic and architecture features demonstrated prognostic capability in MIBC within the TCGA dataset and predictive capability for the PaR in the BLASST-1 trial. These findings support further validation studies.

Risk model based on genes regulating the response of tumor cells to T-cell-mediated killing in esophageal squamous cell carcinoma.

Immune checkpoint inhibitors (ICIs) represent a promising therapeutic approach for esophageal squamous cell carcinoma (ESCC). However, the subpopulations of ESCC patients expected to benefit from ICIs have not been clearly defined. The anti-tumor cytotoxic activity of T cells is an important pharmacological mechanism of ICIs. In this study, the prognostic value of the genes regulating tumor cells to T cell-mediated killing (referred to as GRTTKs) in ESCC was explored by using a comprehensive bioinformatics approach. Training and validation datasets were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. A prognostic risk scoring model was developed by integrating prognostic GRTTKs from TCGA and GEO datasets using a ridge regression algorithm. Patients with ESCC were divided into high- and low-risk groups based on eight GRTTKs (EIF4H, CDK2, TCEA1, SPTLC2, TMEM209, RGP1, EIF3D, and CAPZA3) to predict overall survival in the TCGA cohort. Using Kaplan-Meier curves, receiver operating characteristic curves, and C-index analysis, the high reliability of the prognostic risk-scoring model was certified. The model scores served as independent prognostic factors, and combining clinical staging with risk scoring improved the predictive value. Patients in the high-risk group exhibited abundant immune cell infiltration, including immune checkpoint expression, antigen presentation capability, immune cycle gene expression, and high tumor inflammation signature scores. The high-risk group exhibited a greater response to immunotherapy and neoadjuvant chemotherapy than the low-risk group. Drug sensitivity analysis demonstrated lower IC50 for AZD6244 and PD.0332991 in high-risk groups and lower IC50 for cisplatin, ATRA, QS11, and vinorelbine in the low-risk group. Furthermore, the differential expression of GRTTK-related signatures including CDK2, TCEA1, and TMEM209 were verified in ESCC tissues and paracancerous tissues. Overall, the novel GRTTK-based prognostic model can serve as indicators to predict the survival status and immunotherapy response of patients with ESCC, thereby providing guidance for the development of personalized treatment strategies.

Construction of a 12-Gene Prognostic Risk Model and Tumor Immune Microenvironment Analysis Based on the Clear Cell Renal Cell Carcinoma Model.

Accurate survival predictions and early interventional therapy are crucial for people with clear cell renal cell carcinoma (ccRCC). In this retrospective study, we identified differentially expressed immune-related (DE-IRGs) and oncogenic (DE-OGs) genes from The Cancer Genome Atlas (TCGA) dataset to construct a prognostic risk model using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. We compared the immunogenomic characterization between the high- and low-risk patients in the TCGA and the PUCH cohort, including the immune cell infiltration level, immune score, immune checkpoint, and T-effector cell- and interferon (IFN)-γ-related gene expression. A prognostic risk model was constructed based on 9 DE-IRGs and 3 DE-OGs and validated in the training and testing TCGA datasets. The high-risk group exhibited significantly poor overall survival compared with the low-risk group in the training (P < 0.0001), testing (P = 0.016), and total (P < 0.0001) datasets. The prognostic risk model provided accurate predictive value for ccRCC prognosis in all datasets. Decision curve analysis revealed that the nomogram showed the best net benefit for the 1-, 3-, and 5-year risk predictions. Immunogenomic analyses of the TCGA and PUCH cohorts showed higher immune cell infiltration levels, immune scores, immune checkpoint, and T-effector cell- and IFN-γ-related cytotoxic gene expression in the high-risk group than in the low-risk group. The 12-gene prognostic risk model can reliably predict overall survival outcomes and is strongly associated with the tumor immune microenvironment of ccRCC.

Integrating bioinformatics and experimental validation to unveil disulfidptosis-related lncRNAs as prognostic biomarker and therapeutic target in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) stands as a prevalent malignancy globally, characterized by significant morbidity and mortality. Despite continuous advancements in the treatment of HCC, the prognosis of patients with this cancer remains unsatisfactory. This study aims at constructing a disulfidoptosis‑related long noncoding RNA (lncRNA) signature to probe the prognosis and personalized treatment of patients with HCC.MethodsThe data of patients with HCC were extracted from The Cancer Genome Atlas (TCGA) databases. Univariate, multivariate, and least absolute selection operator Cox regression analyses were performed to build a disulfidptosis-related lncRNAs (DRLs) signature. Kaplan–Meier plots were used to evaluate the prognosis of the patients with HCC. Functional enrichment analysis was used to identify key DRLs-associated signaling pathways. Spearman’s rank correlation was used to elucidate the association between the DRLs signature and immune microenvironment. The function of TMCC1-AS1 in HCC was validated in two HCC cell lines (HEP3B and HEPG2).ResultsWe identified 11 prognostic DRLs from the TCGA dataset, three of which were selected to construct the prognostic signature of DRLs. We found that the survival time of low-risk patients was considerably longer than that of high-risk patients. We further observed that the composition and the function of immune cell subpopulations were significantly different between high- and low-risk groups. Additionally, we identified that sorafenib, 5-Fluorouracil, and doxorubicin displayed better responses in the low-score group than those in the high-score group, based on IC50 values. Finally, we confirmed that inhibition of TMCC1-AS1 impeded the proliferation, migration, and invasion of hepatocellular carcinoma cells.ConclusionsThe DRL signatures have been shown to be a reliable prognostic and treatment response indicator in HCC patients. TMCC1-AS1 showed potential as a novel prognostic biomarker and therapeutic target for HCC.

MiRNA levels are associated with body mass index in endometrial cancer and may have implications for therapy.

Endometrial cancer (EC) is the most prevalent gynecological cancer in high-income countries. Its incidence is skyrocketing due to the increase in risk factors such as obesity, which represents a true pandemic. This study aimed to evaluate microRNA (miRNA) expression in obesity-related EC to identify potential associations between this specific cancer type and obesity. miRNA levels were analyzed in 84 EC patients stratified based on body mass index (BMI; ≥30 or <30) and nine noncancer women with obesity. The data were further tested in The Cancer Genome Atlas (TCGA) cohort, including 384 EC patients, 235 with BMI ≥30 and 149 with BMI <30. Prediction of miRNA targets and analysis of their expression were also performed to identify the potential epigenetic networks involved in obesity modulation. In the EC cohort, BMI ≥30 was significantly associated with 11 deregulated miRNAs. The topmost deregulated miRNAs were first analyzed in 84 EC samples by single miRNA assay and then tested in the TCGA dataset. This independent validation provided further confirmation about the significant difference of three miRNAs (miR-199a-5p, miR-449a, miR-449b-5p) in normal-weight EC patients versus EC patients with obesity, resulting significantly higher expressed in the latter. Moreover, the three miRNAs were significantly correlated with grade, histological type, and overall survival. Analysis of their target genes revealed that these miRNAs may regulate obesity-related pathways. In conclusion, we identified specific miRNAs associated with BMI that are potentially involved in modulating obesity-related pathways and that may provide novel implications for the clinical management of obese EC patients.

Construction of a 12-gene prognostic model for colorectal cancer based on heat shock protein-related genes

Some heat shock proteins (HSPs) have been shown to influence tumor prognosis, but their prognostic significance in colorectal cancer (CRC) remains unclear. This study explored the prognostic significance of HSP-related genes in CRC. Transcriptional data and clinical information of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database, and a literature search was conducted to identify HSP-related genes. Using Least Absolute Selection and Shrinkage Operator (LASSO) regression and univariate/multivariate Cox regression analyses, 12 HSP-related genes demonstrating significant associations with CRC survival were successfully identified and employed to formulate a predictive risk score model. The efficacy and precision of this model were validated utilizing TCGA and Gene Expression Omnibus (GEO) datasets, demonstrating its reliability in CRC prognosis prediction. gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed significant disparities between high- and low-risk groups in chromatin remodeling biological functions and neutrophil extracellular trap formation pathways. Single sample gene set enrichment analysis (ssGSEA) further revealed differences in immune cell types and immune functional status between the two risk groups. Differential analysis showed higher expression of immune checkpoints within the low-risk group, while the high-risk group exhibited notably higher Tumor Immune Dysfunction and Exclusion (TIDE) scores. Additionally, we predicted the sensitivity of different prognosis risk patients to various drugs, providing potential drug choices for tailored treatment. Combined, our study successfully crafted a novel CRC prognostic model that can effectively predict patient survival, immune landscape, and treatment response, providing important support and guidance for CRC patient prognosis.

Population-based high-dimensional analyses identify multiple intrinsic characters for cancer vaccines against lung squamous cell carcinoma.

In lung squamous cell carcinoma (LUSC), current cancer vaccines show promising effects, despite a lack of benefit for a large number of patients. We first identified the tumor antigens into shared and private antigens, and determined the population by clustering analysis in public datasets. For vaccine development, The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were collected. WGCNA method was furthermore applied to construct a consensus gene co-expression network based on TCGA and CPTAC datasets. The main analyses in bulk sequencing included survival, clinical features, tumor microenvironment (TME), and pathways enrichment. In addition, single-cell RNA (scRNA) analysis of cancer epithelium dissected consensus subtype. We identified the ideal population for cancer vaccines, and candidate neoantigens including AOC1, COL5A2, LGI2, and POSTN. According to subtype analysis, Lung squamous 1 (LSQ1) type exhibited a higher tumor mutational load (TMB) and copy number but no immune infiltration, whereas lung squamous 2 (LSQ2) tumors had a higher global methylation level and more fibroblasts but had less stemness. Meanwhile, trajectory analysis further revealed that the evolution of TME influenced prognosis. We emphasized specific pathways or targets with the potential for combination immunotherapy by consensus network and single-cell RNA analyses. Anti-androgen therapy has been validated in vitro experiments of LUSC as proof of concept. In conclusion,LSQ1 was linked to immune exclusion and might be utilized for vaccination, while LSQ2 was linked to immune dysfunction and could be used for programmed cell death protein 1 (PD1) blocking therapy.

Exploring Unique Extracellular Vesicles Associated Signatures: Prognostic Insights, Immune Microenvironment Dynamics, and Therapeutic Responses in Pancreatic Adenocarcinoma.

Extracellular vesicles play an important role in the progression of pancreatic adenocarcinoma (PAAD) through the transfer of proteins, mRNAs, and long noncoding RNAs (lncRNAs). However, the intricate interplay between extracellular vesicles-related lncRNAs and the tumor microenvironment (TME) remains poorly elucidated. Consequently, our investigation aimed to delineate the association between extracellular vesicles-related lncRNAs and the PAAD microenvironment. Initially, we identified differentially expressed lncRNAs (DELs) from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) project datasets. Subsequently, we validated the expression of these DELs within extracellular vesicles and assessed their prognostic implications in PAAD using the GSE133684 and TCGA datasets. Multiomics data were analyzed comprehensively, including genomic landscape, functional annotation, immune profiles, and therapeutic responses. Differential expression of selected lncRNAs in both cellular and exosomal fractions of PAAD was further confirmed through quantitative polymerase chain reaction (qPCR). Eight DELs were identified from TCGA and GTEx datasets, and two exosomal lncRNAs exhibited a significant correlation with overall survival, warranting further investigation. Specifically, elevated expression of LINC00996 correlated positively with immune infiltration and enhanced response to immunotherapy. Conversely, heightened expression of TRHED-AS1 was associated with compromised immune cell infiltration and diminished responsiveness to immunotherapy. Our study establishes a compelling link between two extracellular vesicles-related gene signatures, prognosis, and immune infiltration in PAAD. Notably, these signatures serve as robust prognostic indicators for PAAD patients, offering valuable insights for the strategic selection of immunotherapeutic interventions.

Exploration of the prognostic effect of costimulatory genes in bladder cancer.

A prognostic model of bladder cancer was constructed based on costimulatory molecules, and its stability and accuracy were verified in different datasets. The expression profile of bladder cancer RNA and the corresponding clinical data in The Cancer Genome Atlas (TCGA) database were analyzed employing computational biology, and a prognostic model was constructed for costimulating molecule-related genes. The model was applied in GSE160693, GSE176307, Xiangya_Cohort, GSE13507, GSE19423, GSE31684, GSE32894, GSE48075, GSE69795 and GSE70691 in TCGA dataset and Gene Expression Omnibus database. The role of costimulating molecules in bladder cancer tumor subtypes was also explored. By consistent cluster analysis, bladder cancer in the TCGA dataset was categorized into two subtypes: C1 and C2. The C1 subtype exhibited a poor prognosis, high levels of immune cell infiltration and significant enrichment of natural killer cells, T cells and dendritic cells in the C1 subtype. In addition, the ImmuneScore calculated by the ESTIMATE algorithm differed greatly between the two subtypes, and the ImmuneScore of the C1 subtype was greater than the C2 subtype in a significant manner. This study also assessed the relationship between costimulating molecules and immunotherapy response. The high-risk group responded poorly to immunotherapy, with significant differences in the amount of most immune cells between the two groups. Further, three indices of the ESTIMATE algorithm and 22 immune cells of the CIBERSORT algorithm were significantly correlated with risk values. These findings suggest the potential value of costimulating molecules in predicting immunotherapy response. A costimulatory molecule-based prognostic model for bladder cancer was established and validated across multiple datasets. This model introduces a novel mode for tailoring treatments to each individual with bladder cancer, and offers valuable insights for informed clinical choices. Simultaneously, this research also delved into the significance of costimulating molecules within distinct bladder cancer subtypes, shedding novel insights into improving immunotherapy strategies for the treatment of bladder cancer.

Prognostic Significance of Iron Metabolism and Immune-Related Genes as Risk Markers in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a highly lethal liver cancer with significant heterogeneity, which poses challenges in predicting prognosis and treatment outcomes. The impact of iron metabolism and immune-related genes (IMRGs) on HCC patient prognoses remains elusive. We utilized The Cancer Genome Atlas (TCGA) dataset to obtain mRNA expression data and clinical information from HCC patients. Through the application of LASSO regression and univariate/multivariate Cox regression analyses, we identified five IMRGs significantly associated with survival of HCC patients. We constructed a prognostic model comprising these five genes. The model demonstrated excellent predictive performance, not only within TCGA dataset but also when validated using the Gene Expression Omnibus (GEO) dataset. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses presented significant variations in functional categories, such as apical plasma membrane and collagen-containing extracellular matrix. Several pathways, including the PI3K-AKT signaling pathway and the calcium signaling pathway, exhibited significant variations among HCC patients with varying prognoses (P < 0.05). Immune infiltration analysis indicated significantly lower levels of various immune cells, immune functions, and immune checkpoints, such as B cells, CD8+ T cells, and TILs, in the high-risk group (P < 0.05). Immunophenoscore results suggested that the low-risk group may exhibit a more favorable response to immune therapy. Furthermore, the CellMiner database predicted anti-tumor drugs significantly associated with prognostic genes (P < 0.001). In conclusion, our findings highlight the predictive role of IMRGs in prognosis and immune treatment of HCC, indicating that ADAMTS13, CRHBP, VIPR1, FCN3, and CLEC1B may serve as potential prognostic biomarkers for HCC.

The Inflammatory Characteristics of Symptomatic Glioma Associated With Poor Prognosis and Chemoresistance via Tumor Necrosis Factor Signaling Pathway.

Among gliomas, the most common primary malignant brain tumor, incidental gliomas account for 2.5%-5% of cases. The controversy over whether to pursue immediate treatment or adopt a wait-and-see approach remains, and more molecular and immunological evidence is needed for definitive treatment decisions. Total RNA sequencing (RNA-seq) data and single cell RNA sequencing (scRNA-seq) data were retrospectively analyzed to compare the molecular and immunological tumor microenvironment differences between incidental glioma and symptomatic glioma samples. These were classified using symptom data from The Cancer Genome Atlas (TCGA) and public dataset. RNA-seq analysis of the GBMLGG dataset identified 343 genes upregulated in symptomatic glioma and 118 in incidental glioma, with 104 common genes upregulated in symptomatic glioma across both the TCGA and Chinese Glioma Genome Atlas (CGGA) datasets. Enrichment analysis revealed that these 104 genes in symptomatic glioma were significantly associated with immunological pathways. scRNA-seq analysis of glioma revealed 11 cell types, including T cells, myeloid cells, and oligodendrocytes, with the tumor necrosis factor (TNF) signaling pathway strongly influencing other cell types, particularly myeloid cells. Enrichment and survival analyses showed that TNF signaling is associated with temozolomide resistance and poorer prognosis in glioma patients. The findings suggest that symptomatic glioma enhances inflammatory responses linked to poor prognosis and chemoresistance. This supports the hypothesis that immediate treatment of incidental glioma may improve patient outcomes over a wait-and-see approach.

A bioinformatic analysis found low expression and clinical significance of ATF4 in breast cancer

BackgroundActivating Transcription Factor 4 (ATF4) expression exhibits differential patterns across different types of tumors. Besides, the pathogenesis of breast cancer is complex, and the exact relationship between ATF4 and ATF4 remains uncertain. MethodsThe analysis of ATF4 expression was conducted by utilizing The Cancer Genome Atlas (TCGA) pan-cancer data, while the gene expression profile of breast cancer was checked by the comprehensive database-Gene Expression Omnibus database. In order to gain a more comprehensive understanding of the specific cell types that exhibit ATF4 expression within the microenvironment of breast cancer, we conducted a single-cell analysis of ATF4 using two distinct datasets of human breast cancer (GSE114717 and GSE11088, respectively). The spatial distribution of ATF4 within a tissue was demonstrated based on datasets obtained from the Human Protein Atlas (HPA) and SpatialDB. The clinical prognostic significance of ATF4 was assessed by analyzing clinical survival data obtained from TCGA, GSE4830, and GSE25055 datasets. We used the R package clusterProfiler to carry out an enrichment analysis of ATF4. We assessed how ATF4 impacts the growth and movement of breast cancer cell lines. We manipulated ATF4 levels using plasmid transfection techniques. ResultsThe expression of ATF4 was found to be suboptimal and demonstrated a significant correlation with enhanced disease-specific survival (p = 0.012) and overall survival (p = 0.032) in breast cancer as well as other malignancies. We conducted an analysis to investigate the interaction between the infiltration level of immune cells and the expression of ATF4, using samples obtained from TCGA with known immune cell infiltration scores. Furthermore, a notable positive correlation exists between the elevated expression of ATF4 and immune-related genomes, specifically those associated with chemokine as well as immunity. Subsequent examination revealed a notable augmentation in the cytodifferentiation of T cells into regulatory T (Treg) cells within tissues exhibiting elevated levels of ATF4 expression. ATF4 exhibits notable upregulation in the MDA-MB-231 cell, thereby exerting a substantial impact on cell proliferation and migration upon its knockdown. Conversely, the overexpression of ATF4 in the MCF7 Luminal A breast cancer cell line can also modulate cellular function. ConclusionsOur study suggests that ATF4 helps T cells differentiate into Treg cells in breast cancer. ATF4 can represent a clinically useful biomarker to predict the overall survival rate, especially in patients with different subtypes of breast cancer. Provide certain guidance value for the development of targeted drugs or inhibitors targeting ATF4.

Overexpression of SLC35F2 is a potential prognostic biomarker for lung adenocarcinoma

ObjectiveTo explore the potential clinical and prognostic significance of Homo sapiens solute carrier family 35 member F2 (SLC35F2) in the context of lung adenocarcinoma (LUAD). MethodsThe expression pattern of SLC35F2 in LUAD tissues and normal tissues was analyzed in The Cancer Genome Atlas (TCGA) datasets and validated in 12 pairs of fresh clinical LUAD tissues and their corresponding adjacent normal tissues using quantitative real-time PCR (qRT-PCR) and western blotting. Immunohistochemistry (IHC) was used to assess the protein expression of SLC35F2 in 60 paraffin-embedded LUAD tissues, and its associations with clinicopathological parameters were further examined. The prognostic significance of SLC35F2 mRNA expression was also evaluated using the Kaplan-Meier method, and Cox regression models in LUAD patients from the TCGA database. The potential utility of SLC35F2 as an indicator of recurrence or metastasis was explored through the follow-up of selected clinical LUAD cases. Lastly, gene set enrichment analysis (GSEA) was conducted to investigate the underlying biological mechanisms and signaling pathways. ResultsBioinformatics analysis utilizing the TCGA database indicated that SLC35F2 mRNA exhibited heightened expression in LUAD tissues when compared to normal tissues. These findings were further substantiated through the examination of 12 pairs of clinical LUAD tissues and their corresponding adjacent normal tissues, employing qRT-PCR and western blotting techniques. IHC results from a cohort of 60 LUAD patients demonstrated an up-regulation of SLC35F2 in 38 out of 60 individuals (63.3 %), which exhibited a significant correlation with tumor size, lymph node metastasis, and clinical stage (all P < 0.05). Both the Kaplan-Meier curve and the Cox proportional hazard analyses indicated a strong association between the up-regulation of SLC35F2 mRNA expression and unfavorable overall survival (OS) in patients with LUAD, as observed in the TCGA datasets (P < 0.05). The follow-up findings from select clinical LUAD cases provided evidence that the expression of SLC35F2 could serve as a dependable biomarker for monitoring the recurrence or metastasis. Additionally, the GSEA highlighted the enrichment of apoptosis, adhesion, small cell lung cancer (SCLC), and p53 signaling pathways in the subgroup of LUAD patients with elevated SLC35F2 expression. ConclusionSLC35F2 exhibited an up-regulated in both mRNA and protein expression, rendering it a valuable independent prognostic indicator for patients diagnosed with LUAD.

Revealing chronic disease progression patterns using Gaussian process for stage inference.

The early stages of chronic disease typically progress slowly, so symptoms are usually only noticed until the disease is advanced. Slow progression and heterogeneous manifestations make it challenging to model the transition from normal to disease status. As patient conditions are only observed at discrete timestamps with varying intervals, an incomplete understanding of disease progression and heterogeneity affects clinical practice and drug development. We developed the Gaussian Process for Stage Inference (GPSI) approach to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. We tested the ability of the GPSI to reliably stratify synthetic and real-world data for osteoarthritis (OA) in the Osteoarthritis Initiative (OAI), bipolar disorder (BP) in the Adolescent Brain Cognitive Development Study (ABCD), and hepatocellular carcinoma (HCC) in the UTHealth and The Cancer Genome Atlas (TCGA). First, GPSI identified two subgroups of OA based on image features, where these subgroups corresponded to different genotypes, indicating the bone-remodeling and overweight-related pathways. Second, GPSI differentiated BP into two distinct developmental patterns and defined the contribution of specific brain region atrophy from early to advanced disease stages, demonstrating the ability of the GPSI to identify diagnostic subgroups. Third, HCC progression patterns were well reproduced in the two independent UTHealth and TCGA datasets. Our study demonstrated that an unsupervised approach can disentangle temporal and phenotypic heterogeneity and identify population subgroups with common patterns of disease progression. Based on the differences in these features across stages, physicians can better tailor treatment plans and medications to individual patients.

Construction and validation of a risk model based on the key SNARE proteins to predict the prognosis and immune microenvironment of gliomas.

Synaptic transmission between neurons and glioma cells can promote glioma progression. The soluble N-ethylmaleimide-sensitive fusion factor attachment protein receptors (SNARE) play a key role in synaptic functions. We aimed to construct and validate a novel model based on the SNARE proteins to predict the prognosis and immune microenvironment of glioma. Differential expression analysis and COX regression analysis were used to identify key SRGs in glioma datasets, and we constructed a prognostic risk model based on the key SRGs. The prognostic value and predictive performance of the model were assessed in The Cancer Genome Atlas (TCGA) and Chinese glioma Genome Atlas (CGGA) datasets. Functional enrichment analysis and immune-related evaluation were employed to reveal the association of risk scores with tumor progression and microenvironment. A prognostic nomogram containing the risk score was established and assessed by calibration curves and time-dependent receiver operating characteristic curves. We verified the changes of the key SRGs in glioma specimens and cells by real-time quantitative PCR and Western blot analyses. Vesicle-associated membrane protein 2 (VAMP2) and vesicle-associated membrane protein 5 (VAMP5) were identified as two SRGs affecting the prognoses of glioma patients. High-risk patients characterized by higher VAMP5 and lower VAMP2 expression had a worse prognosis. Higher risk scores were associated with older age, higher tumor grades, IDH wild-type, and 1p19q non-codeletion. The SRGs risk model showed an excellent predictive performance in predicting the prognosis in TCGA and CGGA datasets. Differentially expressed genes between low- and high-risk groups were mainly enriched in the pathways related to immune infiltration, tumor metastasis, and neuronal activity. Immune score, stromal score, estimate score, tumor mutational burden, and expression of checkpoint genes were positively correlated with risk scores. The nomogram containing the risk score showed good performance in predicting the prognosis of glioma. Low VAMP2 and high VAMP5 were found in different grades of glioma specimens and cell lines. We constructed and validated a novel risk model based on the expression of VAMP2 and VAMP5 by bioinformatics analysis and experimental confirmation. This model might be helpful for clinically predicting the prognosis and response to immunotherapy of glioma patients.

Expression of pyroptosis-associated genes and construction of prognostic model for thyroid cancer.

Thyroid cancer (THCA) is a common endocrine malignancy. This study aimed to explore the expression of pyroptosis-related genes in THCA and establish a prognosis prediction model. Differentially expressed pyroptosis-related genes (DEPRGs) were identified in The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB). Subsequently, these genes were subjected to Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A least absolute shrinkage and selection operator (LASSO) regression model was employed to establish a DEPRG signature, and its reliability was validated through survival analysis and receiver operating characteristic (ROC) curves. Patients in TCGA-THCA cohort were stratified into two risk groups. The biological functions of the genes between the two risk groups were assessed through gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Finally, expression of DEPRGs was validated using datasets from the Gene Expression Omnibus (GEO) and the Human Protein Atlas (HPA) databases. Six DEPRGs were identified in TCGA dataset. Through LASSO Cox regression analysis, we determined IL6, TP63, NOD1, and BAX to be significant. Kaplan-Meier survival analysis demonstrated that patients with THCA expressing high levels of NOD1 and classified as low-risk individuals exhibited prolonged survival. The multifactorial ROC curves yielded area under the curve (AUC) scores exceeding 0.7 for risk score, age, and T-stage, affirming their significance as independent prognostic factors as determined by multivariate analysis. Additionally, we observed elevated expression levels of BAX and NOD1 in THCA using data derived from the HPA database and the GEO dataset. We established a novel DEPRG signature for predicting the prognosis of THCA, potentially offering a promising therapeutic marker for THCA treatment.