Immune Score Research Articles

Development of the TP53 mutation associated hypopharyngeal squamous cell carcinoma prognostic model through bulk multi-omics sequencing and single-cell sequencing

ObjectiveThe aim of this study was to construct a prognostic model based on the TP53 mutation to calculate prognostic risk scores of patients with HPSCC. MethodsTP53 mutation and transcriptome data were downloaded from the TCGA databases. Gene expression data from GSE65858, GSE41613, GSE3292, GSE31056, GSE39366, and GSE227156 datasets were downloaded from the GEO database. GSEA, univariate, multivariate Cox analyses, and LASSO analysis were employed to identify key genes and construct the prognostic model. ROC curves were utilized to validate the OS and RFS results obtained from the model. The associations between risk scores with various clinicopathological characteristics and immune scores were analyzed via ggplot2, corrplot package, and GSVA, respectively. Single-cell sequencing data was analyzed via unbiased clustering and SingleR cell annotations. ResultsInitially, two key genes, POLD2 and POLR2G, were identified and utilized to construct the prognostic model. Samples were divided into different risk groups via the risk scores obtained from the model, with high-risk group samples exhibiting poorer prognosis. Furthermore, the risk score exhibited a positive correlation with lymphatic metastasis in patients and the immune scores of CD4+ T, CD8+ T, dendritic cell, macrophage, and neutrophil. The immune responses also exhibited notable disparities between the high- and low-risk groups. The results of single-cell sequencing analysis demonstrated that epithelial cells and macrophages were relatively abundant in HPSCC samples. POLD2 and POLR2G exhibited higher expressions in epithelial cells, with most of the identified pathways also enriched in epithelial cells. ConclusionThe prognostic model exhibited a significant capacity for predicting the prognosis of HSPCC samples based on the TP53 mutation conditions and may also predict the cancer characteristics and immune infiltration scores of samples via different risk scores obtained from the model. Level of evidenceLevel 5.

The Enhanced Role of Eosinophils in Radiomics-Based Diagnosis of Microvascular Invasion and Its Association with the Immune Microenvironment in Hepatocellular Carcinoma.

To investigate the role of eosinophil counts (EC) in microvascular invasion (MVI) for enhancing the radiomics based diagnostic model. Additionally, its correlation with early recurrence and tumor immune microenvironment was explored. Propensity score matching was employed to evaluate on 462 cases whether EC was an independent risk factor for MVI. Subgroup analyses examined EC's effect on MVI across varying hypersplenism degrees. Univariate-multivariate logistic regression identified MVI's independent factors to develop a diagnostic model. Univariate-multivariate COX regression determined early recurrence factors. Co-detection by indexing (CODEX) constructed the immune score (IS), and Spearman correlation analyzed its association with peripheral immunity. EC was an independent risk factor for MVI (p=0.038, OR=1.304 (95% CI: 1.014-1.677)), and its effect on MVI disappeared with the severity of hypersplenism. The diagnostic model with EC was significantly improved (AUC=0.787 (95% CI: 0.737-0.836) vs AUC=0.748(95% CI: 0.694-0.802, p=0.005)). MVI was an independent risk factor for early recurrence (p<0.001, HR = 2.254 (95% CI: 1.557-3.263)). IS was negatively correlated with lymphocyte counts (R=-0.311, p=0.022), and positively correlated with EC (R=0.301, p=0.027) and RS (R = 0.315, p = 0.018). EC was an independent risk factor for MVI and was related to the tumor immune microenvironment. EC should be included in the diagnosis of MVI to improve diagnostic efficiency.

548P Simplified immune score based on CD8+ T-cells at the invasive margin provides comparable prognostic value to immune scores in non-metastatic colorectal cancer

548P Simplified immune score based on CD8+ T-cells at the invasive margin provides comparable prognostic value to immune scores in non-metastatic colorectal cancer

Abstract P454: Screening of risk genes for carotid vulnerable plaque based on transcriptomics and experimental verification

Purpose and background: This study aims to conduct an in-depth analysis of human carotid atherosclerotic plaques using transcriptomic data to elucidate the relationship between hypoxia-related genes and the stability of carotid artery plaques and explore their connection with immune infiltration. Methods: Four GEO datasets (GSE21545, GSE24495, GSE159677, and GSE189300) were used. Initially, gene expression data from carotid artery plaque samples in GSE21545 and GSE24495 were analyzed. Hypoxia-related gene sets were obtained from MSigDB, and the "HALLMARK_HYPOXIA" gene set was explicitly selected. GSEA software was used for ultimately selecting CDKN1C and HAS1 as characteristic genes. Subsequently, sample clustering analysis was conducted to determin the optimal number of clusters. Gene set enrichment and differential analysis were performed. Real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting experiments were conducted for preliminary validation of CDKN1C and HAS1 expression levels in ApoE knockout mice at 16 and 6 weeks. Subsequently, single-cell data from GSE159677 were processed to determine the differential localization of CDKN1C in carotid atherosclerotic plaques, with functional analysis conducted using GSE189300. Results: CDKN1C and HAS1 genes were found to exhibit differential expression in plaques and were correlated with hypoxia and immune scores. Clustering analysis revealed two main clusters, with patients in cluster A showing a higher risk of adverse events and a stronger association with immunity. Genes with a positive effect on atherosclerosis were highly expressed in poorer prognosis cluster A. Validation dataset GSE24495 also confirmed the grouping ability of CDKN1C and HAS1 genes, showing enrichment in atherosclerosis-related pathways. RT-qPCR and protein immunoblotting experiments demonstrated no significant differences in HAS1 expression levels, while CDKN1C showed lower protein expression in 16-week ApoE knockout mice compared to 6 weeks. Single-cell data from GSE159677 localized differential expression of CDKN1C in plaque cells. Finally, immunofluorescence validation confirmed the coexistence of CDKN1C and vascular smooth muscle cells. Conclusion: This study comprehensively analyzed transcriptomics data from carotid atherosclerotic plaques, revealing the significant roles of CDKN1C and HAS1 genes. Immunofluorescence validation further supported the crucial role of these genes in carotid atherosclerosis.

Comprehensive Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Transcriptional Signatures of Macrophages in Intrahepatic Cholestasis of Pregnancy.

Intrahepatic cholestasis of pregnancy (ICP) is a disorder that characterized by maternal pruritus, abnormal liver function, and an elevation in total bile acid concentrations during pregnancy. Immune factors have been recognized as playing a vital role in the mechanism of ICP. However, the underlying mechanisms regulating dysfunctional immune cells and immune genes remain to be fully elucidated. Single-cell RNA sequencing and bulk RNA sequencing data of the placenta were downloaded from the SRA database. The AUCell package, Monocle package and SCENIC package were utilized to explored immune cell activity, cell trajectory and transcription factor, respectively. GO, KEGG, and GSEA were employed to explore potential biological mechanisms. Cell-cell communications were further investigated using the CellChat package. RT-PCR, and Western blot were used to verify the gene expression in placenta. In placenta cells, macrophages were found to be significantly increased in ICP. Additionally, macrophages exhibited the highest immune gene score and were divided into four subclusters (MF1-4). Our analysis revealed significant elevations in MF2, associated with LPS response and antigen presentation, and MF4, associated with TNF and cytokine production. MF3 displayed an anti-inflammatory phenotype. MF1, closely related to ribosomes and proteins, exhibited a sharp decrease. Although ICP maintained an anti-inflammatory state, macrophage trajectories showed a gradual progression toward inflammation. Subsequently, we confirmed that cytokine- and chemokine-related signaling pathways were emphasized in macrophages. Within the CXCL signaling pathway, the increased expression of CXCL1 in macrophages can interact with CXCR2 in neutrophils, potentially inducing macrophage infiltration, stimulating neutrophil chemotaxis, and leading to an inflammatory response and cellular damage. In conclusion, we firstly revealed the transcriptional signatures of macrophages in ICP and discovered a tendency toward an inflammatory state. This study also provides new evidence that the CXCL1-CXCR2 axis may play an important role in the pathogenesis of ICP.

Exploration of Key Genes Combining with Immune Infiltration Level andTumor Mutational Burden in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a lethal malignancy due to its heterogeneity and aggressive behavior. Recently, somatic mutations and tumor cell interactions with the surrounding tumor immune microenvironment (TIME) have been reported to participate in HCC carcinogenesis and predict HCC progression. In this study, we aimed to investigate the association between tumor mutational burden (TMB) and TIME in HCC. Additionally, we sought to identify differentially expressed genes (DEGs) associated with HCC prognosis and progression. The expression, clinical, and mutational data were downloaded from the cancer genome atlas (TCGA) database. The immune infiltration levels and TMB levels of the HCC samples were estimated and the samples were divided into immune cluster (ICR)-1 and 2 based on immune infiltration score and high and low TMB groups based on TMB score. Thereafter, differential gene expression analysis was conducted to identify the DEGs in the ICR1/2 and high/low TMB groups, and the intersecting DEGs were selected. Thereafter, Cox regression analysis was performed on 89 significant DEGs, among which 19 were associated with prognosis. These 19 DEGs were then used to construct a prognostic model based on their expression levels and regression coefficients. Thereafter, we analyzed the DEGs in mutant and wildtype TP53 HCC samples and identified high BCL10 and TRAF3 expression in the mutant TP53 samples. BCL10 and TRAF3 expression was detected by real-time quantitative reverse transcription PCR and immunohistochemistry, and their clinical correlation, biological function, and immune infiltration levels were analyzed by chi-square analyses, Gene Set Enrichment Analysis (GSEA), and "ssGSEA", respectively. The results of our study revealed that immune infiltration level was correlated with TMB and that they synergistically predicted poor prognosis of HCC patients. DEGs enriched in immune-related pathways could serve as indicators of immunotherapy response in HCC. Among these DEGs, BCL10 and TRAF3 were highly expressed in HCC tissues, especially in the mutant TP53 group, and they co-operatively exhibited immunological function, thereby affecting HCC progression and prognosis. In this study, we identified BCL10 and TRAF3 as potential prognostic indicators in HCC patients. Additionally, we found that BCL10 and TRAF3 influence TMB and TIME in HCC patients and can be used for the development of immune-based therapies for improving the long-term survival of HCC patients.

Revealing the key role of cuproptosis in osteoporosis via the bioinformatic analysis and experimental validation of cuproptosis-related genes.

The incidence of osteoporosis has rapidly increased owing to the ageing population. Cuproptosis, a novel mechanism that regulates cell death, may be a new therapeutic approach. However, the relevance of cuproptosis in the immune microenvironment and osteoporosis immunotherapy is still unknown. We intersected the differentially expressed genes from osteoporotic samples with 75 cuproptosis-related genes to identify 16 significantly expressed cuproptosis genes. We further explored the connection between the cuproptosis pattern, immune microenvironment, and immunotherapy. The weighted gene co-expression network analysis algorithm was used to identify cuproptosis phenotype-associated genes, and we used quantitative real-time PCR and immunohistochemistry in mouse femur tissues to verify hub gene (MAP2K2, FDX1, COX19, VEGFA, CDKN2A, and NFE2L2) expression. Six hub genes and 59 cuproptosis phenotype-associated genes involved in immunisation were identified among the osteoporosis and control groups, and the majority of these 59 genes were enriched in the inflammatory response, as well as in signal transducers, Janus kinase, and transcription pathway activators. In addition, two different clusters of cuproptosis were found, and immune infiltration analysis showed that gene Cluster 1 had a greater immune score and immune infiltration level. Further analysis revealed that three key genes (COX19, MAP2K2, and FDX1) were highly correlated with immune cell infiltration, and external experiments validated the association of these three genes with the prognosis of osteoporosis. We used the three key mRNAs COX19, MAP2K2, and FDX1 as a classification model that may systematically elucidate the complex connection between cuproptosis and the immune microenvironment of osteoporosis. New insights into osteoporosis pathogenesis and immunotherapy prospects may be gained from this study.

628. GUSTAVE ROUSSY IMMUNE SCORE IS A PROGNOSTIC MARKER IN PATIENTS WITH ESOPHAGEAL CANCER AFTER NEOADJUVANT THERAPY: A MULTICENTER RETROSPECTIVE STUDY

Abstract Background GRIm-Score (Gustave Roussy Immune Score), a novel nutritional and inflammatory-based prognostic score, is an unfavorable prognostic factor in patients with esophageal carcinoma (EC). However, the prognostic importance of GRIm-Scores after neoadjuvant chemo-radiotherapy in these patients remains unclear. Methods A retrospective study including 432 EC patients undergoing surgical resection was performed. The GRIm-Score was calculated by lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), and albumin (ALB). The over survival (OS) and disease-free survival (DFS) were analyzed for the current study with Cox regression analyses, Kaplan-Meier methods, and propensity score matching (PSM). Results There were 374 (86.6%) men and 58 (13.4%) women with a mean age of 62.1 ± 7.7 years (range: 39-80 years). After PSM, cohorts consisted of 55 patients in the high GRIm-Score group and 55 in the low GRIm-Score group. Patient with a high GRIm-Score had poor OS (Cohort: p = 0.001; PSM: p = 0.007) and DFS (Cohort: p = 0.001; PSM: p = 0.008). The GRIm-Score, instead of NLR, LDH, or ALB in multivariate analyses, was an independent prognostic factor for OS (p = 0.019) before PSM. However, after PSM, it was a powerful independent prognostic factor for both OS (p = 0.028) and DFS (p = 0.039). Subgroup analyses showed that GRIm-Score could identify cases with worse OS or DFS among pT3-4 patients, indicating the complementary role of GRIm-Score in the decision-making of adjuvant therapy. Conclusion The GRIm-Score was an independent prognostic marker in patients with EC undergoing surgical resection after neoadjuvant therapy. Our study is also the first to discuss the prognostic value of GRIm-Score in patients with EC after neoadjuvant treatment.

Integrated machine learning survival framework to decipher diverse cell death patterns for predicting prognosis in lung adenocarcinoma.

Various forms of programmed cell death (PCD) collectively regulate the occurrence, development and metastasis of tumors. Nevertheless, a comprehensive analysis of the diverse types of PCD in lung adenocarcinoma (LUAD) is currently lacking. The study encompassed a total of 1481 genes associated with the regulation of 13 distinct PCD patterns. Ten machine learning algorithms were amalgamated into 101 combinations, from which the optimal algorithm was chosen to formulate an artificial intelligence-derived prognostic signature based on the average C-index across four multicenter cohorts. The established optimal cell death index (CDI) model emerged as an independent risk factor for overall survival, demonstrating robust and consistent performance. Notably, CDI exhibited significantly higher accuracy compared to traditional clinical variables and molecular features. It exhibited superior performance than other published models. By integrating CDI with relevant clinical features, a nomogram with excellent predictive performance was developed. LUAD patients with low CDI score had a higher immune modulators, TIDE scores and immune scores, indicating a better immunotherapy benefit. More importantly, we found that the regulation of antigen presentation is the crucial mechanism of PCD. SCG2 is a key molecule that inhibits the malignant progression of LUAD. CDI holds great potential as a robust and promising tool for enhancing clinical outcomes in patients with LUAD.

Anoikis-related subtype and prognosis analyses based on bioinformatics, and an expression verification of ANGPTL4 based on experiments of lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high mortality. Anoikis resistance is an important mechanism of tumor cell proliferation and migration. Our research is devoted to exploring the role of anoikis in the diagnosis, classification, and prognosis of LUAD. We downloaded the expression profile, mutation, and clinical data of LUAD from The Cancer Genome Atlas (TCGA) database. The "ConsensusClusterPlus" package was then used for the cluster analysis, and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were used to establish the prognostic model. We verified the reliability of the model using a Gene Expression Omnibus (GEO) data set. A gene set variation analysis (GSVA) was conducted to investigate the functional enrichment differences in the different clusters and risk groups. The CIBERSORT algorithm and a single-sample gene set enrichment analysis (ssGSEA) were used to analyze immune cell infiltration. The tumor mutation burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) scores were used to evaluate the patients' sensitivity to immunotherapy. Immunohistochemical staining of tissue microarrays was used to verify the correlation between ANGPTL4 expression and the clinicopathological characteristics and prognosis of LUAD patients. First, we screened 135 differentially expressed anoikis-related genes (ARGs) and 23 prognosis-related ARGs from TCGA-LUAD data set. Next, 494 LUAD samples were allocated to cluster A and cluster B based on the 23 prognosis-related ARGs. The Kaplan-Meier (K-M) analysis showed the overall survival (OS) of cluster B was better than that of cluster A. The clinicopathological characteristics and functional enrichment analyses revealed significant differences between clusters A and B. The tumor microenvironment (TME) analysis showed that cluster B had more immune cell infiltration and a higher TME score than cluster A. Subsequently, a LASSO Cox regression model of LUAD was constructed with ten ARGs. The K-M analysis showed that the low-risk patients had longer OS than the high-risk patients. The receiver operating characteristic curve, nomogram, and GEO data set verification results showed that the model had high accuracy and reliability. The level of immune cell infiltration and TME score were higher in the low-risk group than the high-risk group. The high-risk group had stronger sensitivity to immune checkpoint block therapy and weaker sensitivity to chemotherapy drugs than the low-risk group. ANGPTL4 expression was correlated with stage, tumor differentiation, tumor size, lymph node metastasis, and OS. We discovered novel molecular subtypes and constructed a novel prognostic model of LUAD. Our findings provide important insights into subtype classification and the accurate survival prediction of LUAD. We also identified ANGPTL4 as a prognostic indicator of LUAD.

Exploring the prognosis value, immune correlation, and drug responsiveness prediction of homeobox C6 (HOXC6) in lung adenocarcinoma

BackgroundsHomeobox C6 (HOXC6) is a gene that encodes for a transcription factor involved in various cellular processes, including development and differentiation, and regulates cancer progression. However, the carcinogenesis and effect of HOXC6 in lung adenocarcinoma (LUAD) still need further investigation.MethodsThe differential HOXC6 expression levels at the mRNA and protein level were explored in multiple public datasets, including The Cancer Genome Atlas (TCGA) and Human Protein Atlas (HPA) dataset. Gene Expression Omnibus (GSE31210), International Cancer Genome Consortium (ICGC) datasets and the LUAD sample from Affiliated Hospital of Guangxi Medical University. We also investigated the relation between HOXC6 expression and clinicopathologic indexes. Furthermore, the correlation of immune infiltration, drug responsiveness and HOXC6 were explored.ResultsThe upregulated HOXC6 expressions at mRNA and protein levels were found in LUAD tissues compared to the normal lung tissues. Besides, the relatively shorter overall survival time, worse T and N stages, and lower immune scores were found in the high-expression HOXC6 subgroup. Notably, T cells regulatory (Tregs), Macrophages M0, and Plasma cells had the higher infiltration levels in the high-HOXC6 expression subgroup, while NK cells activated, Monocytes, Dendritic cells resting, and Mast cells resting had the lower infiltration levels. In drug sensitivity analysis, we revealed that LUAD patients with high-HOXC6 expression may be more susceptible to Camptothecin, Cytarabine, Docetaxel, Elesclomol, Rapamycin, Sorafinib, Temsirolimus, and Vorinostat.ConclusionsTaken together, there is a great potential for HOXC6 to become a prognosis biomarker and contribute to develop treatment strategies for LUAD patients. Further mechanism exploration and drug development for HOXC6 are needed.

Integrated single-cell and bulk RNA-seq analysis identifies a prognostic T-cell signature in colorectal cancer

Colorectal cancer (CRC) is a major contributor to global morbidity and mortality, necessitating more effective therapeutic approaches. T cells, prominent in the tumor microenvironment, exert a crucial role in modulating immunotherapeutic responses and clinical outcomes in CRC. This study introduces a pioneering method for characterizing the CRC immune microenvironment using single-cell sequencing data. Unlike previous approaches, which focused on individual T-cell signature genes, we utilized overall infiltration levels of colorectal cancer signature T-cells. Through weighted gene co-expression network analysis, Lasso regression, and StepCox analysis, we developed a prognostic risk model, TRGS (T-cell related genes signatures), based on six T cell-related genes. Multivariate Cox analysis identified TRGS as an independent prognostic factor for CRC, showcasing its superior predictive efficacy compared to existing immune-related prognostic models. Immunoreactivity analysis revealed higher Immunophenoscore and lower Tumor Immune Dysfunction and Exclusion scores in the low-risk group, indicating potential responsiveness to immune checkpoint inhibitor therapy. Additionally, patients in the low-risk group demonstrated heightened sensitivity to 5-fluorouracil-based chemotherapy regimens. In summary, TRGS emerges as a standalone prognostic biomarker for CRC, offering insights to optimize patient responses to immunotherapy and chemotherapy, thereby laying the groundwork for personalized tumor management strategies.

Evidence for Radiation Therapy in Stage III Locoregionally Advanced Cutaneous Melanoma in the Post-Immunotherapy Era: A Literature Review.

In the landscape of Stage III locoregionally advanced cutaneous melanoma treatment, the post-immunotherapy era has sparked a number of questions on the management of the nodal basin. However, much of the available literature is not focused on radiation therapy as an adjuvant therapy. This literature review aims to illuminate the evidence surrounding radiation therapy's potential to mitigate regional recurrences in the adjuvant setting for melanoma. Additionally, it seeks to identify adjunct systemic therapy options and explore the synergy between systemic therapy and radiation. Despite strides in surgical techniques and systemic therapies, controlling regional Stage III melanoma remains a formidable clinical hurdle. While historical data strongly suggest the efficacy of adjuvant radiation therapy in reducing regional recurrence risk, its evaluation predates the advent of MAPK pathway inhibitors and robust immunotherapy options. Notably, clinical trials have yet to definitively demonstrate a survival advantage with adjuvant radiation therapy. Additional research should focus on refining the definition of high risk for regional recurrence through gene expression profiling or tumor immune profiling scores and elucidate the optimal role of adjuvant radiation therapy in patients treated with neoadjuvant systemic therapy.

The construction and experimental verification of a 6-LncRNA model based on Lactic acid metabolism in the tumor microenvironment of Wilms tumor

BackgroundLactic acid (LA) can promote the malignant progression of tumors through the crosstalk with the tumor microenvironment (TME). However, the function of long non-coding RNAs (lncRNAs) related to LA metabolism in Wilms tumor (WT) remains unclear. MethodsGene expression data and clinical data of WT patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Through the ESTIMATE algorithm and Pearson correlation analysis, lncRNAs related to tumor immunity and LA metabolism were screened. Subsequently, Cox regression analysis and Lasso Cox regression analysis were used to construct a model. Furthermore, candidate genes were identified and a competitive endogenous RNA (ceRNA) network was conducted to explore the specific mechanism of characteristic genes. Finally, based on the strong clinical relevance of UNC5B-AS1, its expression and function were experimentally verified. ResultsThe immune score and stromal score were found to be closely related to the prognosis of WT. Eventually, a prognostic model (TME-LA-LM) consisting of 6 lncRNAs was successfully identified. The model demonstrated favorable predictive ability and accuracy, with significant variation in immune infiltration and drug susceptibility observed between risk groups. Additionally, the study revealed the involvement of 2 candidate genes and 5 microRNAs (miRNAs) in the tumor’s development. Notably, UNC5B-AS1 was highly expressed and found to promote the proliferation and migration of tumor cells. ConclusionThis study, for the first time, elucidated the prognostic signatures of WT using lncRNAs related to TME and LA metabolism. The fundings of this research offer valuable insights for future studies on immunotherapy, personalized chemotherapy and mechanism research.

Development and Validation of a Comprehensive Analysis of the Competing Endogenous circRNA/miRNA/mRNA Network for the Identification of Immune-Related Targets in Esophageal Squamous Cell Carcinoma.

Immunotherapy for esophageal squamous cell carcinoma (ESCC) exhibits notable variability in efficacy. Concurrently, recent research emphasizes circRNAs' impact on the ESCC tumor microenvironment. To further explore the relationship, we leveraged circRNA, microRNA, and mRNA sequence datasets to construct a comprehensive immune-related circRNA-microRNA-mRNA network, revealing competing endogenous RNA (ceRNA) roles in ESCC. The network comprises 16 circular RNAs, 13 microRNAs, and 1,560 mRNAs. Weighted gene co-expression analysis identified immune-related modules, notably cancer-associated fibroblast (CAF) and myeloid-derived suppressor cell modules, correlating significantly with immune and stemness scores. Among them, the CAF module plays a crucial role in extracellular matrix function and effectively discriminates ESCC patients. Four hub collagen family genes within CAF correlated robustly with CAF, macrophage infiltration, and T-cell exclusion. In-house sequencing and RT-qPCR validated their elevated expression. We also identified CAF module-targeting drugs as potential ESCC treatments. In summary, we established an immune-related circRNA-miRNA-mRNA network that not only illuminates ceRNA functionality but also highlights circRNAs' involvement in the CAF through collagen gene targeting. These findings hold promise to predict ESCC immune landscapes and therapy responses, ultimately aiding in more personalized and effective clinical decision-making.

Construction of a prognostic model for disulfidptosis-related long noncoding RNAs in R0 resected hepatocellular carcinoma and analysis of their impact on malignant behavior

BackgroundDisulfidptosis is an emerging form of cellular death resulting from the binding of intracellular disulfide bonds to actin cytoskeleton proteins. This study aimed to investigate the expression and prognostic significance of hub disulfidptosis-related lncRNAs (DRLRs) in R0 resected hepatocellular carcinoma (HCC) as well as their impact on the malignant behaviour of HCC cells.MethodsA robust signature for R0 resected HCC was constructed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression and was validated in an independent internal validation cohort to predict the prognosis of R0 HCC patients. Comprehensive bioinformatics analysis was performed on the hub DRLRs (KDM4A-AS1, MKLN1-AS, and TMCC1-AS1), followed by experimental validation using quantitative real-time polymerase chain reaction (qRT‒PCR) and cellular functional assays.ResultsThe signature served as an independent prognostic factor applicable to R0 HCC patients across different age groups, tumour stages, and pathological characteristics. Gene Ontology (GO) and gene set enrichment analysis (GSEA) revealed hub pathways associated with this signature. The high-risk group presented an increased abundance of M0 macrophages and activated memory CD4 T cells as well as elevated macrophage and major histocompatibility complex (MHC) class I expression. High-risk R0 HCC patients also presented increased tumour immune dysfunction and exclusion scores (TIDEs), mutation frequencies, and tumour mutational burdens (TMBs). Drug sensitivity analysis revealed that high-risk patients were more responsive to drugs, including GDC0810 and osimertinib. High expression levels of the three hub DRLRs were detected in R0 HCC tissues and HCC cell lines. Functional assays revealed that the three hub DRLRs enhanced HCC cell proliferation, migration, and invasion.ConclusionsA signature was constructed on the basis of three DRLRs, providing novel insights for personalized precision therapy in R0 HCC patients.Graphical

Epithelial cell-related prognostic risk model in breast cancer based on single-cell and bulk RNA sequencing

ObjectiveThis study aims to construct an epithelial cell-related prognostic risk model for breast cancer (BRCA) and explore its significance. MethodsGSE42568, GSE10780, GSE245601, and TCGA-BRCA datasets were sourced from public databases. Epithelial cell-related differentially expressed genes were identified using single-cell data analysis. Venn diagrams determined the intersecting genes between epithelial cell-related and BRCA-related genes. Batch Kaplan-Meier (K-M) survival analysis identified core intersecting genes for BRCA overall survival. Consensus clustering, enrichment, LASSO, and COX regression analyses were performed on the core intersecting genes, and then a prognostic risk model was constructed. The diagnostic and prognostic effectiveness of the risk model was subsequently evaluated and immune infiltration analysis was conducted. Finally, qRT-PCR was used to verify the expression of genes in the risk model. ResultsThere were 374 intersecting genes between epithelial cell-related and BRCA-related genes, among which 51 core intersecting genes were associated with BRCA prognosis. Consensus clustering categorized TCGA-BRCA into C1 and C2, with shared regulation of the estrogen signaling pathway. Three genes (DIRC3, SLC6A2, TUBA3D) were independent predictors of BRCA prognosis, forming the basis for a risk model. Except for exhibiting satisfactory diagnostic efficacy, the risk score elevation correlated with poor prognosis, elevated matrix, immune, and ESTIMATE scores, and negative correlation with microsatellite instability. The in vitro results confirmed the differential expression levels of DIRC3, SLC6A2, and TUBA3D. ConclusionThe prognostic risk model associated with epithelial cells demonstrates effective diagnostic performance in BRCA, serving as an independent prognostic factor for BRCA patients. Additionally, it exhibits a correlation with immune scores.

Identification of DNA methylation-regulated WEE1 with potential implications in prognosis and immunotherapy for low-grade glioma.

WEE1 is a critical kinase in the DNA damage response pathway and has been shown to be effective in treating serous uterine cancer. However, its role in gliomas, specifically low-grade glioma (LGG), remains unclear. The impact of DNA methylation on WEE1 expression and its correlation with the immune landscape in gliomas also need further investigation. This study used data from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus (GEO) and utilized various bioinformatics tools to analyze gene expression, survival, gene correlation, immune score, immune infiltration, genomic alterations, tumor mutation burden, microsatellite instability, clinical characteristics of glioma patients, WEE1 DNA methylation, prognostic analysis, single-cell gene expression distribution in glioma tissue samples, and immunotherapy response prediction based on WEE1 expression. WEE1 was upregulated in LGG and glioblastoma (GBM), but it had a more significant prognostic impact in LGG compared to other cancers. High WEE1 expression was associated with poorer prognosis in LGG, particularly when combined with wild-type IDH. The WEE1 inhibitor MK-1775 effectively inhibited the proliferation and migration of LGG cell lines, which were more sensitive to WEE1 inhibition. DNA methylation negatively regulated WEE1, and high DNA hypermethylation of WEE1 was associated with better prognosis in LGG than in GBM. Combining WEE1 inhibition and DNA methyltransferase inhibition showed a synergistic effect. Additionally, downregulation of WEE1 had favorable predictive value in immunotherapy response. Co-expression network analysis identified key genes involved in WEE1-mediated regulation of immune landscape, differentiation, and metastasis in LGG. Our study shows that WEE1 is a promising indicator for targeted therapy and prognosis evaluation. Notably, significant differences were observed in the role of WEE1 between LGG and GBM. Further investigation into WEE1 inhibition, either in combination with DNA methyltransferase inhibition or immunotherapy, is warranted in the context of LGG.

Cell Polarity-related Gene PTK7, a Potential Diagnostic Biomarker in Pan-cancer.

PTK7 (Protein Tyrosine Kinase 7), a member of the receptor protein tyrosine kinase family, was originally discovered in colon cancer cells. It plays a pivotal role in numerous developmental and physiological processes, particularly in the regulation of cell polarity. Despite accumulating evidence of PTK7's significant influence on tumor development, a comprehensive pan-cancer analysis of PTK7 has yet to be conducted. We conducted a comprehensive analysis of PTK7's expression, prognostic value, and mutational patterns across various tumor types. We further explored the correlations between PTK7 expression and tumor stemness, immune-related genes, immune scores, and immune cell infiltration. Enrichment analysis revealed PTK7's critical involvement in pan-cancer functions and processes, including the WNT pathway, Epithelial-Mesenchymal Transition (EMT), and cell polarity regulation. Additionally, we validated PTK7's expression in gastric cancer via immunohistochemistry. Our study indicates that PTK7 holds promise as an ideal pan-cancer biomarker due to its involvement in tumor progression and tumor immunity.

A Novel Cuproptosis-Related Gene Signature Predicts Prognosis in Papillary Thyroid Carcinoma Patients.

Cuproptosis, a novel form of cell death mediated by protein lipoylation, is intricately linked to mitochondrial metabolism. However, the clinical association of cuproptosis- related genes (CRGs) in thyroid cancer remains unclear. In this study, we performed a systematic investigation on the differential expression and genetic alterations of CRGs in papillary thyroid cancer (PTC) and constructed a CRG signature to predict the prognosis of PTC patients. We integrated the data of The Cancer Genome Atlas (TCGA) database and analyzed the expression of 10 CRGs in PTC. CRG signature was constructed using univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression. In addition, the signature-related molecular features were validated by a combination of functional enrichment, Cox regression, and immune infiltration analysis. Independent validation cohort and quantitative real-time polymerase chain reaction (qRT-PCR) were used to validate the expression of differentially expressed CRG (CDKN2A). Thyroid cancer patients could be divided into two subtypes (high and low CRG score groups). We found that the overall survival (OS) of patients was lower in the high CRG score group (HCSG) than in the low CRG score group (LCSG) (P < 0.001). The area under the curve (AUC) values for 3 years, 5 years, and 8 years were 0.872, 0.941, and 0.976, respectively. Cox regression analysis indicated that the CRG score could serve as an independent prognostic indicator for PTC. Functional enrichment analysis indicated that the CRG prognostic signature was also associated with the tumor immune microenvironment. In HCSG, the immune suppression cell score was significantly higher than in LCSG. In addition, we identified the expression of CRG (CDKN2A) by qRT-PCR, and the results aligned with the TCGA database. Our CRG signature demonstrates excellent predictive capabilities for the prognosis of PTC patients. CRGs may play an important role in tumorigenesis and could be used to predict the immunotherapy efficacy of PTC.