TCGA Dataset Research Articles

Phosphorus Metabolism-Related Genes Serve as Novel Biomarkers for Predicting Prognosis in Bladder Cancer: A Bioinformatics Analysis.

Phosphorus metabolism might be associated with tumor initiation and progression. We aimed to screen out the phosphorus metabolism genes related to bladder cancer and construct a clinical prognosis model. The dataset used for the analysis was obtained from TCGA database. GO and KEGG enrichment analyses were subsequently applied to differentially expressed genes. Consensus clustering was utilized, and different clusters of the tumor immune microenvironment and other features were compared. The phosphorus metabolism-related genes involved in prognosis were screened out by univariate Cox regression, LASSO regression and multivariate Cox regression analysis, and a nomogram was constructed. The performance of the nomogram was validated using TCGA dataset and the GEO dataset, respectively. Overall, 405 phosphorus metabolism-related differentially expressed genes from TCGA database were identified, which were associated with phosphorylation, cell proliferation, leukocyte activation, and signaling pathways. Two clusters were obtained by consistent clustering. After tumor immune microenvironment analysis, significant differences in immune cell infiltration between cluster 1 and cluster 2 were found. Four phosphorus metabolism-related genes (LIME1, LRP8, SPDYA, and MST1R) were associated with the prognosis of bladder cancer (BLCA) patients. We built a prognostic model and visualized the model as a nomogram. Calibration curves demonstrated the performance of this nomogram, in agreement with that shown by the ROC curves. We successfully identified four phosphorus metabolism-related genes associated with prognosis, providing potential targets for biomarkers and therapeutics. A nomogram based on these genes was developed. Nevertheless, this study is based on bioinformatics, and experimental validation remains essential.

Abstract B070: TGFB2 mRNA levels prognostically interact with Interferon-alpha receptor activation of IRF9 and IFI27, and makers for tumor-associated macrophages impacting overall survival in PDAC

Abstract Background: The treatment of pancreatic ductal adenocarcinoma (PDAC) is an unmet challenge, with the median overall survival rate remaining less than a year, even with the use of FOLFIRINOX-based therapies. Methods: In this study, we analyzed archived macrophage- associated mRNA expression using datasets deposited in the UCSC Xena web platform to compare normal pancreatic tissue and PDAC tumor samples. We also utilized TCGA datasets deposited in cBioportal (https://www.cbioportal.org/study/summary?id=paad_tcga_pan_can_atlas) and KMplotter (http://kmplot.com/analysis/index.php?p=service&cancer=pancancer_rnaseq) to relate mRNA expression levels to overall survival outcomes. The prognostic impact of TGFB2 mRNA expression levels was determined by comparing patients expressing high versus low levels of TGFB2 (50th percentile cut-off) in low macrophage TME. Results: The TGFB2 gene exhibited low mRNA expression levels in normal tissue, with less than one TPM, while, in tumor tissue, TGFB2 expression levels exhibited a 7.9-fold increase in mRNA expression relative to normal tissue (P < 0.0001). Interferon Alpha Inducible Protein 27 (IFI27), the downstream product of Interferon regulatory factor 9 (IRF9) and Signal transducer and activator of transcription 1 (STAT1) transcriptional activation by Interferon alpha/beta receptor 1 (IFNAR1) exhibited a significant 66.3-fold increase in expression in tumor tissue compared to normal tissue (P < 0.0001). The statistical contrast between the expression of IRF9 mRNA also displayed a significant (P<0.0001) 4.2-fold increase in mRNA expression levels. These increased levels of mRNA expression were found to be prognostically significant, whereby patients with high expression levels of either TGFB2, IRF9, or IFI27 showed median OS times ranging from 16 to 20 months (P<0.01 compared to 72 months for patients with low levels of expression for both TGFB2 and either IRF9 or IFI27). In TME with low macrophage levels, patients with high levels of TGFB2 mRNA exhibited significantly shorter OS outcomes than patients with low TGFB2 mRNA levels (Median OS of 72.2 versus 15.3 months, P<0.0001). Furthermore, expression of TGFB2 mRNA and TAM markers exhibited TGFB2 prognostic impact independent of 17 TAM markers suggested by multivariate Cox regression models. Conclusions: The results of our study suggest that a combination of pharmacological tools can be used in treating PDAC patients, targeting both TGFB2 and the components of the type-I interferon signaling pathway to improve OS outcomes. TGFB2 expression impacts OS independent of TAM markers, but the expression of TAM markers impacts the prognostic effect of TGFB2 mRNA expression. Citation Format: Sanjive Qazi, Wen-Han Chang, Cynthia Lee, Vuong Trieu. TGFB2 mRNA levels prognostically interact with Interferon-alpha receptor activation of IRF9 and IFI27, and makers for tumor-associated macrophages impacting overall survival in PDAC [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B070.

METTL3-Mediated m6A Modification of FMRP Drives Hepatocellular Carcinoma Progression and Indicates Poor Prognosis.

Accumulating studies reveal that m6A RNA methylation plays a critical role in cancer pathogenesis and progression. METTL3 as a m6A methyltransferase acts as an oncogene in multiple malignancies including hepatocellular carcinoma (HCC). However, the role and underlying mechanism by which METTL3 contributes to HCC remain unclear. The association of METTL3 expression with clinicopathological characteristics and prognosis in patients with HCC was assessed by reverse transcription polymerase chain reaction, Western blot, and public TCGA dataset. MTT, colony formation, Transwell assays, and xenograft tumor models were executed to reveal the role of METTL3 in HCC. m6A dot blot, RNA immunoprecipitation (RIP), m6A methylated RIP, and Western blot assays were used to uncover the regulatory mechanism of METTL3 in HCC cells. We found that METTL3 was dramatically upregulated in HCC tissue samples and acted as an independent prognostic factor for poor survival and tumor recurrence in patients with HCC. Silencing of METTL3 repressed cell growth and invasion in vitro and invivo, but restored expression of METTL3 boosted these effects. Mechanistical investigations revealed that METTL3 could directly interact with FMRP and harbor a positive correlation with FMRP expression. Knockdown of METTL3 reduced FMRP m6A levels as well as its mRNA and protein expression. FMRP overexpression drove cell colony formation and cell invasion and abolished METTL3 knockdown-induced antitumor effects and AKT/mTORC1 signaling inactivation. Elevated expression of FMRP could act as an independent prognostic factor for poor survival and tumor recurrence in patients with HCC. Our findings demonstrate that METTL3-mediated m6A modification of FMRP promotes growth and invasion of HCC cells and may provide a promising therapeutic target for HCC.

Exploring extrahepatic metastasis of hepatocellular carcinoma based on methylation driver genes and establishing a prognostic model for hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC), theseventh most common cancer worldwide, is characterized by a high mortality rate, advanced diagnosis, and susceptibility to extrahepatic metastasis. Numerous studies have shown that DNA methylation is a crucial factor in epigenetic modifications and regulation of carcinogenesis. MethodsHCC patient data were sourced from the TCGA dataset as a training set, while GSE116174 was used as an external validation set for verification. Differential methylation and expression analyses were performed on HCC samples with and without extrahepatic metastasis. In the intersecting genes, the relationship between methylation and expression levels of the intersecting genes was analyzed. Genes with a correlation coefficient≥|0.30| and P<0.05 were identified as methylation driver genes. Cox regression analysis was conducted to identify genes associated with HCC prognosis and establish a risk score. Subsequently, a prognostic model was established and validated using Cox regression analysis incorporating the risk score and other clinical factors. Using immunohistochemistry to evaluate the expression of DHX58 and EIF5A2 in HCC tissues with and without extrahepatic metastasis. Immunoinfiltration analysis was performed on the HCC samples using CIBERSORT. ResultsOur research identified eight methylation driver genes for HCC extrahepatic metastasis, of which two genes (DHX58 and EIF5A2) were associated with HCC patient prognosis. And the study further constructed and validated the risk score and prognostic model. Immunoinfiltration analysis showed that M0 macrophage abundance was correlated with the prognosis of HCC patients. Immunohistochemistry revealed differences in DHX58 and EIF5A2 expression between HCC tissues with and without extrahepatic metastasis, consistent with our bioinformatics findings.

Development of a prognostic model for NSCLC based on differential genes in tumour stem cells

Non-small cell lung cancer (NSCLC) constitutes a significant portion of lung cancers and cytotoxic drugs (e.g. cisplatin) are currently the first-line treatment. However, NSCLC has developed resistance to this drug, which limits the therapeutic effect and thus affects prognosis. NSCLC sc-RNA-seq data were downloaded from the GEO database and Ku Leuven Laboratory for Functional Epigenetics, and bulk RNA-seq data were obtained from the TCGA database. The “Seurat” package was employed for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were applied for downscaling and cluster identification. Use the FindAllMarkers function to find differential genes (DEGs) for tumor stem cells. Then, we performed univariate regression analyses on the DEGs to identify potential prognostic genes. We created a machine learning framework based on potential prognostic genes, which combines 10 machine learning methods and their 101 combinations to get the optimal prognostic risk model. The model was evaluated in the training set and validation set. A nomogram was developed to provide physicians with a quantitative tool for prognosis prediction. Finally, we evaluated the expression and functionality of SLC2A1. We discovered 22 cell clusters containing 218379 cells by examining single-cell RNA sequencing datasets (GSE148071, KU_lom, GSE131907, GSE136246, GSE127465). Tumour cells were isolated for subpopulation analysis and 162 differential genes from SOX2_cancer were obtained. After univariate Cox analysis, we found 23 genes with prognostic potential prognostic value and utilized them to develop 101‑combination machine learning computational framework. We eventually picked the best performing ‘StepCox[both] + RSF’, which includes 8 genes. The model has a relatively high prediction accuracy in both TCGA and GEO datasets. In in vitro investigations, targeted suppression of the SLC2A1 gene resulted in significant reductions in proliferation, invasion and migration in A549 cells. In addition, a significant reduction in cisplatin resistance was seen in A549/DDP cells. The outcomes demonstrated the precision and credibility of the prognostic model for NSCLC, highlighting its potential significance in the treatment and prognosis of individuals affected by this disease. SLC2A1 may become a promising prognostic marker and a potential therapeutic target, offering valuable insights to inform clinical treatment decisions.

High levels of tumor cell-intrinsic STING signaling are associated with increased infiltration of CD8+ T cells in dMMR/MSI-H gastric cancer

Mismatch repair deficient (dMMR)/microsatellite instability-high (MSI-H) gastric cancer (GC) exhibits an immune-active tumor microenvironment (TME) compared to MMR proficient (pMMR)/microsatellite stable/Epstein-Barr virus-negative [EBV (−)] GC. The tumor cell-intrinsic cyclic GMP–AMP synthase (cGAS)–stimulator of interferon genes (STING) pathway has been considered a key regulator of immune cell activation in the TME. However, its significance in regulating the immune-active TME in dMMR/MSI-H GC remains unclear. Here, we demonstrated that tumor cell-intrinsic cGAS–STING was highly expressed in dMMR GC compared to pMMR/EBV (−) GC. The expression of tumor cell-intrinsic STING was significantly and positively associated with the number of CD8+ tumor-infiltrating lymphocytes in GC. Analysis of TCGA datasets revealed that the expression of interferon-stimulated genes and STING downstream T-cell attracting chemokines was significantly higher in MSI-H GC compared to other subtypes of GC with EBV (−). These results suggest that tumor cell-intrinsic STING signaling plays a key role in activating immune cells in the dMMR/MSI-H GC TME and might serve as a novel biomarker predicting the efficacy of immunotherapy for GC treatment.

Consensus molecular subtyping of metastatic colorectal cancer expands biomarker-directed therapeutic benefit for patients with CMS1 and CMS2 tumors.

We developed a whole transcriptome sequencing (WTS)-based Consensus Molecular Subtypes (CMS) classifier using FFPE tissue and investigated its prognostic and predictive utility in a large clinico-genomic database of CRC patients (n = 24,939). The classifier was trained against the original CMS datasets using an SVM model and validated in an independent blinded TCGA dataset (88.0% accuracy). Kaplan-Meier estimates of overall survival (OS) and time-on-treatment (TOT) were calculated for each CMS (p < 0.05 considered significant). CMS2 tumors were enriched on left-side of colon and conferred the longest median OS. In RAS-wildtype mCRC, left-sided tumors and CMS2 classification were associated with longer TOT with anti-EGFR antibodies (cetuximab and panitumumab). When restricting to only CMS2, there was no significant difference in TOT between right- versus left-sided tumors. CMS1 tumors were associated with a longer median TOT with pembrolizumab relative to other CMS groups, even when analyzing only microsatellite stable (MSS) tumors. A WTS-based CMS classifier allowed investigation of a large multi-institutional clinico-genomic mCRC cohort, suggesting anti-EGFR therapy benefit for right-sided RAS-WT CMS2 tumors and immune checkpoint inhibitor benefit for MSS CMS1. Routine CMS classification of CRC provides important treatment associations that should be further investigated.

Exploring the mechanism of genistein in treating hepatocellular carcinoma through network pharmacology and molecular docking

Abstract Objectives Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, with treatment options limited and outcomes often poor, especially in advanced stages. This study explores the therapeutic potential of genistein, a soybean-derived isoflavone, on HCC using network pharmacology to uncover its multi-targeted anti-cancer mechanisms. Methods Potential targets of genistein were predicted using databases such as Super-PRED, PharmMapper, and SwissTargetPrediction. Abnormally expressed genes in HCC tissues were analyzed from TCGA and GEO datasets, with genes linked to the prognosis of HCC patients selected as potential therapeutic targets. GO and KEGG pathway enrichment analyses were conducted for both genistein’s targets and the HCC-related gene set. Key targets were identified through network analysis using Cytoscape software. Molecular docking was performed with Autodock to assess the binding affinity between genistein and these key targets. The therapeutic effects of genistein on HCC were validated through animal experiments and cell line studies. Results This study identified 343 potential targets for genistein in treating hepatocellular carcinoma (HCC). Analyses revealed enrichment in cell cycle regulation pathways through GO and KEGG assessments. Transcriptomic data from HCC datasets unveiled 184 potential therapeutic targets, emphasizing cell cycle regulation. Notably, 12 proteins were identified as targets of both genistein and HCC treatment. Molecular docking studies demonstrated genistein’s strong binding affinity with CDC25C and MELK. In vitro and in vivo validations affirmed genistein’s role in inhibiting HCC proliferation by inducing G2/M phase arrest. This study elucidates genistein’s multi-target mechanism in suppressing HCC cell proliferation, supporting its potential clinical application in HCC treatment. Conclusions This study demonstrates that genistein effectively suppresses the malignant growth of HCC by interfering with the transition from the G2 to M phase, revealing a multifaceted mechanism of action.

Plasma Exosome miR-203a-3p is a Potential Liquid Biopsy Marker for Assessing Tumor Progression in Breast Cancer Patients.

Timely detection of tumor progression in breast cancer (BC) patients is critical for therapeutic management and prognosis. Plasma exosomal miRNAs are potential liquid biopsy markers for monitoring tumor progression, but their roles in BC remain unclear. In the TCGA database, we first screened for miRNAs significantly associated with BC progression by comparing miRNA expression in para-carcinoma tissues, stage I BC tissues, and stage II-III BC tissues (n = 1026). Cox regression analyses and survival analyses were performed on candidate miRNAs to explore their prognostic value (n = 848). KEGG, GO, and PPI analyses were used to identify enriched pathways associated with cancer. Finally, the potential of candidate miRNAs as liquid biopsy markers was evaluated by sequencing and analyzing plasma exosomal miRNAs from our collection of 45 BC patients (14 in stage I, 31 in stage II-III) and 5 healthy controls, combined with qRT-PCR analysis to assess the correlation of candidate gene expression in plasma exosomes and BC tissues. We found that only miR-203a-3p was progressively elevated with BC progression and was associated with poor prognosis in the TCGA dataset. Its potential target genes were enriched in pathways related to tumor progression, and the downregulation of 48 of these genes was associated with poor prognosis. More importantly, plasma exosomal miR-203a-3p was also found to gradually increase with BC progression, and its expression was positively correlated with miR-203a-3p in BC tissues. This result suggests that plasma exosomal miR-203a-3p may reflect the expression of miR-203a-3p in tumor tissues and serve as a potential liquid biopsy marker for monitoring BC progressions. We found for the first time that elevated miR-203a-3p was associated with BC progression and poor prognosis. Our findings suggested that plasma exosomal miR-203a-3p could hold potential as a liquid biopsy marker for evaluating BC progression in patients.

Liquid-liquid phase separation-related signature predicts prognosis and therapeutic response in esophageal adenocarcinoma

BackgroundEsophageal adenocarcinoma is a leading cause of mortality worldwide. New evidence indicates that liquid-liquid phase separation is related to malignancies. The current study aims at exploring the functions of liquid-liquid phase separation within esophageal adenocarcinoma. Patients within the TCGA dataset were classified using liquid–liquid phase separation-related genes. Significantly differentially expressed genes and prognostic factors for overall survival have been screened by Cox regression. Based on the liquid-liquid phase separation score, the construction of a prognostic model and liquid-liquid phase separation signature was constructed. Tumor mutation burden and drug sensitivity were analyzed in two groups: high liquid-liquid phase separation scores, and low liquid-liquid phase separation scores. According to liquid-liquid phase separation, some small-molecule compounds targeting esophageal adenocarcinoma were screened. The results were verified in vitro with an external cohort. Results87 samples are involved, and 61 liquid-liquid phase separation-related genes may influence esophageal adenocarcinoma by changing DNA conformation and metabolism. Meanwhile, based on a high liquid-liquid phase separation score and low score group including 43 patients, it is found that the result significantly lowered the 5-year overall survival to 32.6 %, compared to 64.8 % in the low-score group of 44 patients with p < 0.001. The high score group had an average TIDE score of 0.27 versus 0.14 in the low-score group, with p = 0.003. The median tumor mutation burden was 9.1 mutations/Mb in the high-score group versus 6.4 mutations/Mb in the low-score group, with p = 0.011. The predictive model worked very well, with area under the curve values of 0.82, 0.79, and 0.76 for 1-, 3-, and 5-year survival, respectively. Liquid-liquid phase separation has been validated as an effective prognostic biomarker and drug sensitivity predictor. SignificanceLiquid-liquid phase separation is potentially implicated in esophageal adenocarcinoma and works as a prognostic biomarker assessment of vulnerability to LLPS, which could help develop individualized therapies by showing how one is situated about various medications where responses vary across the body.

Clinical sequencing reveals diagnostic, therapeutic, and prognostic biomarkers for adult-type diffuse gliomas

Diffuse gliomas in adults are highly infiltrative and largely incurable. Whole exome sequencing (WES) has been demonstrated very useful in genetic analysis. Here WES was performed to characterize genomic landscape of adult-type diffuse gliomas to discover the diagnostic, therapeutic and prognostic biomarkers. Somatic and germline variants of 66 patients with adult-type diffuse gliomas were detected by WES based on the next-generation sequencing. TCGA and CGGA datasets were included to analyze the integrated diagnosis and prognosis. Among 66 patients, the diagnosis of 9 cases was changed, in which 8 cases of astrocytoma were corrected into IDH-wildtype glioblastoma (GBM), and 1 oligodendroglioma without 1p/19q co-deletion into astrocytoma. The distribution of mutations including ATRX/TP53 differed in three cohorts. The genetic mutations in GBM mainly concentrated on the cell cycle, PI3K and RTK pathways. The mutational landscape of astrocytoma was more similar to that of GBM, with the highest frequency in germline variants. Patients with IDH-mutant astrocytoma harboring SNVs of PIK3CA and PIK3R1 showed a significantly worse overall survival (OS) than wild-type patients. AEBP1 amplification was associated with shorter OS in GBM. Our study suggests that clinical sequencing can recapitulate previous findings, which may provide a powerful approach to discover diagnostic, therapeutic and prognostic markers for precision medicine in adult-type diffuse gliomas.

Multicohort study testing the generalisability of the SASKit-ML stroke and PDAC prognostic model pipeline to other chronic diseases

ObjectivesTo validate and test the generalisability of the SASKit-ML pipeline, a prepublished feature selection and machine learning pipeline for the prediction of health deterioration after a stroke or pancreatic adenocarcinoma...

Identification of a circadian-based prognostic signature predicting cancer-associated fibroblasts infiltration and immunotherapy response in bladder cancer.

Circadian rhythm disruption impacts the efficiency of both chemotherapy and immunotherapy, yet identifying the key factors involved remains challenging. Circadian rhythm disruption can trigger aberrant fibroblasts activation, suggesting potential roles of cancer-associated fibroblasts (CAFs) in addressing this issue. In this paper, TCGA-BLCA patients were classified into two subgroups based on the expression of core circadian rhythm genes (CCRGs). The CCRG-based subgroups showed distinct fibroblast-related signals, from which a risk model composed of five fibroblast-related genes was finally established with excellent survival prognostic value in both TCGA and GEO datasets. The risk model was positively associated with the infiltration of CAFs and can efficiently predict the immunotherapy response in BLCA. Besides, high-risk score was associated with reduced sensitivity to a majority of traditional chemotherapeutic drugs such as oxaliplatin and gemcitabine. Further, the correlation between CCRGs and the risk genes was analyzed. Among the five risk genes, FAM20C displayed the most extensive correlation with the CCRGs and exhibited the strongest connection with CAFs infiltration. Moreover, FAM20C independently served as a predictor for the response to immunotherapy in BLCA. In conclusion, this study has identified a circadian-based signature for evaluating CAFs infiltration and predicting the efficacy of chemotherapy and immunotherapy. The central gene FAM20C has emerged as a promising candidate which merits further investigations.

Medullary thyroid cancer: single-cell transcriptome and tumor evolution

BackgroundMedullary thyroid cancer (MTC) is a rare neuroendocrine tumor that originates from the parafollicular C cells of thyroid gland. Understanding the fundamental pathophysiology of MTC is essential for clinical management. Single-cell RNA sequencing (scRNA-seq) technology is a powerful tool for identifying distinct cell types, offering a new biological foundation for comprehending the MTC ecosystem and developing precise treatment.MethodsFormalin fixed and paraffin-embedded (FFPE) samples of primary and adjacent non-cancerous tissues of three MTC cases were collected, and single-cell transcriptome data of MTC were obtained by using scRNA-seq technology. Annotated cell subpopulations were categorized and functionally enriched by principal component analysis, differential gene expression, and cell clustering analysis, to explore the biological process of tumor evolution that may be involved in each cell subpopulation. The copy number variation (CNV) profile was used to distinguish the malignancy of parafollicular thyroid cells, and the evolutionary trajectories of normal cells and tumor cells were revealed by the proposed time series analysis. The highly expressed genes in each cell subpopulation were analyzed by the FindAllMarker function of Seurat software, and verified by immunohistochemistry and fluorescence in situ hybridization. The prognostic value of specific cell subtypes was validated using large-scale public datasets.ResultsA total of 32,544 cells were obtained from the MTC tissue samples and 11,751 cells from the adjacent non-cancerous samples, which were classified into 7 heterogenous subpopulations by using R package of Seurat module. Copy number variations (CNVs) were significantly higher in tumor tissues than in adjacent non-tumor samples, predominantly enriched in subtypes C2 and C4. In addition, the pseudo-time for trajectory analysis suggested that the evolution of MTC tumor cells might begin with the C2 subtype, then transition to the early cancer subgroup C3, and further differentiate into four major malignant cell subpopulations C0, C1, C5 and C6. Survival analysis of a thyroid cancer cohort using the TCGA dataset revealed that high expression of genes linked to the C0 subcluster was correlated with poorer overall survival compared to low expression. Immunohistochemical staining showed that MAP3K4 was highly expressed in MTC tissues compared to adjacent non-cancerous tissues. Fluorescence in situ hybridization also confirmed the amplification of these two genes in MTC samples.ConclusionsBy conducting scRNA-seq on FFPE samples, we mapped the single-cell transcriptome of MTC, uncovering the tumor heterogeneity and unique biological features of each cellular subpopulation. The biological roles of identified tumor cell subpopulations such as C0 and C3 subtypes of parafollicular cells suggested the potential to discover new therapeutic targets and biomarkers for MTC, providing valuable insights for future translational and clinical research.

Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study

IntroductionMolecular profiling of lung cancer is essential to identify genetic alterations that predict response to targeted therapy. While deep learning shows promise for predicting oncogenic mutations from whole tissue images, existing studies often face challenges such as limited sample sizes, a focus on earlier stage patients, and insufficient analysis of robustness and generalizability. MethodsThis retrospective study evaluates factors influencing mutation prediction accuracy using the large Heidelberg Lung Adenocarcinoma Cohort (HLCC), a cohort of 2356 late-stage FFPE samples. Validation is performed in the publicly available TCGA-LUAD cohort. ResultsModels trained on the larger HLCC cohort generalized well to the TCGA dataset for mutations in EGFR (AUC 0.76), STK11 (AUC 0.71) and TP53 (AUC 0.75), in line with the hypothesis that larger cohort sizes improve model robustness. Variation in performance due to pre-processing and modeling choices, such as mutation variant calling, affected EGFR prediction accuracy by up to 7 %. DiscussionModel explanations suggest that acinar and papillary growth patterns are critical for the detection of EGFR mutations, whereas solid growth patterns and large nuclei are indicative of TP53 mutations. These findings highlight the importance of specific morphological features in mutation detection and the potential of deep learning models to improve mutation prediction accuracy. ConclusionAlthough deep learning models trained on larger cohorts show improved robustness and generalizability in predicting oncogenic mutations, they cannot replace comprehensive molecular profiling. However, they may support patient pre-selection for clinical trials and deepen the insight in genotype-phenotype relationships.

Integration of Bioinformatics and Machine Learning to Identify CD8+ T Cell-Related Prognostic Signature to Predict Clinical Outcomes and Treatment Response in Breast Cancer Patients.

We assessed the abundance of CD8+ T cells in the TCGA and METABRIC datasets and obtained CTRGs through WGCNA. Subsequently, a prognostic signature (CTR score) was constructed from CTRGs screened by seven machine learning algorithms, and the relationship between the CTR score and TME, immunotherapy, and drug sensitivity was analyzed. Additionally, CTRGs' expression in different cells within TME was identified through single-cell analysis and spatial transcriptomics. Finally, the expression of CTRGs in clinical tissues was verified via RT-PCR. The CD8+ T cell-related prognostic signature consists of two CTRGs. In the TCGA and METABRIC datasets, the CTR score appeared to be negatively linked to the abundance of CD8+ T cells, and BC patients with higher risk score show a worse prognosis. The low CTR score group exhibits higher immune infiltration levels, closely associated with inhibiting the tumor microenvironment. Compared with the high CTR score group, the low CTR score group shows better responses to chemotherapy and immune checkpoint therapy. Single-cell analysis and spatial transcriptomics reveal the heterogeneity of two CTRGs in different cells. Compared with the adjacent tissues, CD163L1 and KLRB1 mRNA are downregulated in tumor tissues. This study establishes a robust CD8+ T cell-related prognostic signature, providing new insights for predicting the clinical outcomes and treatment responses of breast cancer patients.

Exploring the prognostic value of BRMS1 + microglia based on single-cell anoikis regulator patterns in the immunologic microenvironment of GBM

BackgroundAnoikis is a specialized form of programmed cell death induced by the loss of cell adhesion to the extracellular matrix (ECM). Acquisition of anoikis resistance is a significant marker for cancer cell invasion, metastasis, therapy resistance, and recurrence. Although current research has identified multiple factors that regulate anoikis resistance, the pathological mechanisms of anoikis-mediated tumor microenvironment (TME) in glioblastoma (GBM) remain largely unexplored.MethodsUtilizing single-cell RNA sequencing (scRNA-seq) data and employing non-negative matrix factorization (NMF), we identified and characterized TME cell clusters with distinct anoikis-associated gene signatures. Prognostic and therapeutic response analyses were conducted using TCGA and CGGA datasets to assess the clinical significance of different TME cell clusters. The spatial relationship between BRMS1 + microglia and tumor cells was inferred from spatial transcriptome RNA sequencing (stRNA-seq) data. To simulate the tumor immune microenvironment, co-culture experiments were performed with microglia (HMC3) and GBM cells (U118/U251), and microglia were transfected with a BRMS1 overexpression lentivirus. Western blot or ELISA were used to detect BRMS1, M2 macrophage-specific markers, PI3K/AKT signaling proteins, and apoptosis-related proteins. The proliferation and apoptosis capabilities of tumor cells were evaluated using CCK-8, colony formation, and apoptosis assays, while the invasive and migratory abilities of tumor cells were assessed using Transwell assays.ResultsNMF-based analysis successfully identified CD8 + T cell and microglia cell clusters with distinct gene signature characteristics. Trajectory analysis, cell communication, and gene regulatory network analyses collectively indicated that anoikis-mediated TME cell clusters can influence tumor cell development through various mechanisms. Notably, BRMS1 + AP-Mic exhibited an M2 macrophage phenotype and had significant cell communication with malignant cells. Moreover, high expression of BRMS1 + AP-Mic in TCGA and CGGA datasets was associated with poorer survival outcomes, indicating its detrimental impact on immunotherapy. Upregulation of BRMS1 in microglia may lead to M2 macrophage polarization, activate the PI3K/AKT signaling pathway through SPP1/CD44-mediated cell interactions, inhibit tumor cell apoptosis, and promote tumor proliferation and invasion.ConclusionThis pioneering study used NMF-based analysis to reveal the important predictive value of anoikis-regulated TME in GBM for prognosis and immunotherapeutic response. BRMS1 + microglial cells provide a new perspective for a deeper understanding of the immunosuppressive microenvironment of GBM and could serve as a potential therapeutic target in the future.

Targeted gene sequencing reveals disparate genomic mutations between young and older adults in renal cell carcinoma

BackgroundRenal cell carcinoma (RCC) is a type of cancer that can develop at any point in adulthood, spanning the range of age-related changes that occur in the body. However, the specific molecular mechanisms underlying the connections between age and genetic mutations in RCC have not been extensively investigated.MethodsClinical and genetic data from patients diagnosed with RCC were collected from two prominent medical centers in China as well as the TCGA dataset. The patients were categorized into two groups based on their prognosticated age: young adults (YAs) and older adults (OAs). Univariate and multivariate analysis were employed to evaluate the relationships between age and genetic mutations. Furthermore, a mediation analysis was conducted to assess the association between age and overall survival, with genetic disparities serving as a mediator.ResultsOur analysis revealed significant differences in clinical presentation between YAs and OAs with RCC, including histopathological types, histopathological tumor stage, and sarcomatoid differentiation. YAs were found to have lower mutation burden and significantly mutated genes (SMGs) of RCC. However, we did not observe any significant differences between the two groups in terms of 10 canonical oncogenic signaling pathways-related genes mutation, telomerase-related genes (TRGs) mutation, copy number changes, and genetic mutations associated with clinically actionable targeted drugs. Importantly, we demonstrate superior survival outcomes in YAs, and we confirmed the mediating effect of genetic disparities on these survival outcome differences between YAs and OAs.ConclusionOur findings reveal previously unrecognized associations between age and the molecular underpinnings of RCC. These associations may serve as valuable insights to guide precision diagnostics and treatments for RCC.

Predicting lymphoma prognosis using machine learning-based genes associated with lactylation

BackgroundLactylation, a newly discovered PTM involving lactic acid, is linked to solid tumor proliferation and metastasis. Lymphoma patients exhibit high lactic acid levels, yet lactylation's role in lymphoma is underexplored. This study aimed to identify lactylation-related genes in lymphoma using tumor databases and assess their predictive value in patient prognosis through cell experiments and clinical specimens. MethodsUsing TCGA and GEO datasets, we analyzed the expression levels of lactylation-related genes in diffuse large B-cell lymphoma patients. We also evaluated the prognostic significance of lactylation gene risk scores, exploring their impact on drug sensitivity and tumor immune function. Key lactylation-affecting genes were identified and functionally validated through cell experiments and mouse in vivo experiments. Additionally, the relationship between lactylation and lymphoma prognosis was examined in clinical specimens. ResultsWe identified 70 genes linked to diffuse large B-cell lymphoma prognosis from the lactylation-related gene set. Using clinical data and a COX regression algorithm, we developed an optimized lactylation Riskscore model. This model significantly correlated with prognosis and showed differences in immune cell infiltration, particularly macrophages. High-risk patients showed resistance to chemotherapy drugs but responded well to immunotherapy. HNRNPH1, a lactylation-related gene, influenced patient prognosis, apoptosis, cell cycle distribution in lymphoma cells, and tumor volume in mice. In lymphoma specimens, lactylation levels correlated with Bcl-2, C-myc, and P53 levels. ConclusionsLactylation impacts diffuse large B-cell lymphoma prognosis, tumor immune function, and drug resistance. Our lactylation-based Riskscore model aids in patient stratification and treatment selection. HNRNPH1 regulates lactylation, thereby affecting patient prognosis.

Genome-wide identification of pan-cancer common and cancer-specific alternative splicing events in 9 types of cancer

Alternative splicing (AS) has significant clinical relevance with cancers and is a potential source of neoepitopes. In this study, RNA-seq data of 94 solid tumor and matched adjacent normal tissues from 47 clinical patients covering nine cancer types were comprehensively analyzed using SUVA developed by ourselves. The results identified highly conserved pan-cancer differential alternative splicing (DAS) events and cancer-specific DAS events in a series of tumor samples, which in turn revealed the heterogeneity of AS post-transcriptional regulation across different cancers. The co-disturbed network between spliceosome factors (SFs) and common cancer-associated DAS was further constructed, suggesting the potential possibility of the regulation of differentially expressed SFs on DAS. Finally, the common cancer-associated DAS events were fully validated using the TCGA dataset, confirming the significant correlation between cancer-associated DAS and prognosis. Briefly, our study elucidates new insights into conservatived and specific DAS in cancer, providing valuable resources for cancer therapeutic targets.