Immune Score Research Articles

Exploring the role of UMP as a molecular marker in the tumor microenvironment of lung adenocarcinoma.

e20064 Background: Currently, there is a relatively extensive research emphasis on tumor microenvironment based on single-cell transcriptomics. However, the investigation of the metabolic aspects of tumor microenvironment is relatively rare due to technological limitations. To address this gap, our study employes scFEA and MEBOCOST, which are metabolic level prediction tools based on single-cell transcriptomics, to explore the metabolic landscape of tumor microenvironment in lung adenocarcinoma(LUAD). Methods: This study utilized four single-cell sequencing (scRNA-seq) datasets, two targeted metabolomics datasets, and the TCGA-LUAD RNA sequencing dataset obtained from the Cancer Genomics Data Portal. To perform dimensionality reduction, the Seurat package was employed. Cell annotation was carried out using scType, followed by manual correction using the FindAllMarkers function. Tumor cell clusters were identified by utilizing tumor marker genes and the R package inferCNV. Single-cell metabolic levels were inferred using scFEA. The TCGA-LUAD datasets underwent analysis to estimate and analyze immune levels using estimate and cibersort algorithms. Additionally, scFEA was used for metabolic level analysis, and survival analysis was conducted. UMP differential expression analysis was performed on the targeted metabolomics datasets. Results: Across all four single-cell sequence datasets, metabolites such as (GlcNAc)4 (Man)3 (Asn)1[G00015], B-Alanine[C00099], and UMP[C00105] exhibited significantly higher levels in immune cells compared to tumor cells (P < 0.05). These findings were further validated using the TCGA-LUAD datasets. Cox regression analysis was performed using immune scores and tumor purity scores and based on the median value of risk scores, high and low-risk groups were distinguished. it was observed that UMP levels were higher in the low-risk group. Moreover, higher UMP levels were associated with elevated immune levels and decreased tumor purity. Analysis of targeted metabolomics datasets from various pan-cancer cell lines revealed that UMP expression level in tumor cells was lower than that in B and T cells. Furthermore, in different subtypes of LUAD, including AAH, AIS, MIA, and IAC, UMP expression in tissues gradually decreased as the severity of the subtype increased (P < 0.05). Conclusions: The study revealed that UMP metabolites are expressed at higher levels in immune cells compared to tumor cells within LUAD. Moreover, UMP expression levels showed significant variations among different disease severities of LUAD. These findings indicate that UMP has the potential to serve as a molecular marker for distinguishing between different disease severities. Further research is needed to investigate the underlying mechanisms and validate the diagnostic usefulness of UMP in LUAD.

Integrated whole-exome and bulk transcriptome sequencing delineates the dynamic evolution from preneoplasia to invasive lung adenocarcinoma featured with ground-glass nodules.

The genomic and molecular ecology involved in the stepwise continuum progression of lung adenocarcinoma (LUAD) from adenocarcinoma insitu (AIS) to minimally invasive adenocarcinoma (MIA) and subsequent invasive adenocarcinoma (IAC) remains unclear and requires further elucidation. We aimed to characterize gene mutations and expression landscapes, and explore the association between differentially expressed genes (DEGs) and significantly mutated genes (SMGs) during the dynamic evolution from AIS to IAC. Thirty-five patients with ground-glass nodules (GGNs) lung adenocarcinomas were enrolled. Whole-exome sequencing (WES) and transcriptome sequencing (RNA-Seq) were conducted on all patients, encompassing both tumor samples and corresponding noncancerous tissues. Data obtained from WES and RNA-Seq were subsequently analyzed. The findings from WES delineated that the predominant mutations were observed in EGFR (49%) and ANKRD36C (17%). SMGs, including EGFR and RBM10, were associated with the dynamic evolution from AIS to IAC. Meanwhile, DEGs, including GPR143, CCR9, ADAMTS16, and others were associated with the entire process of invasive LUAD. We found that the signaling pathways related to cell migration and invasion were upregulated, and the signaling pathways of angiogenesis were downregulated across the pathological stages. Furthermore, we found that the messenger RNA (mRNA) levels of FAM83A, MAL2, DEPTOR, and others were significantly correlated with CNVs. Gene set enrichment analysis (GSEA) showed that heme metabolism and cholesterol homeostasis pathways were significantly upregulated in patients with EGFR/RBM10 co-mutations, and these patients may have poorer overall survival than those with EGFR mutations. Based on the six calculation methods for the immune infiltration score, NK/CD8+ T cells decreased, and Treg/B cells increased with the progression of early LUAD. Our findings offer valuable insights into the unique genomic and molecular features of LUAD, facilitating the identification and advancement of precision medicine strategies targeting the invasive progression of LUAD from AIS to IAC.

Complex immune microenvironment of chordoma: a road map for future treatment

BackgroundChordoma, a rare bone tumor, presents limited treatment options and patients typically exhibit poor survival outcomes. While immunotherapy has shown promising results in treating various tumors, research on the immune...

Benchmarking the transcriptomic predictive biomarkers for immunotherapy plus chemotherapy: Results from phase III RATIONALE-309 and ORIENT-11 randomized trials.

e14624 Background: The combination of PD-(L)1 inhibitors and chemotherapy (IO+Chemo) has become the standard first-line treatment for multiple types of cancer. In contrast to PD-(L)1 monotherapy, there is a lack of established biomarkers to distinguish the outcomes of IO+Chemo. The paucity of high-quality omics data in this setting has not allowed for deep biomarker exploration. We utilized RNA-seq data from two in-house randomized controlled trials (RCTs), RATIONALE-309 and ORIENT-11, with the goal to find transcriptomic biomarkers to identify patients who may not derive benefit from IO+Chemo. Methods: RATIONALE-309 (NCT03924986) was a phase 3 study conducted at 42 sites in Asia. Treatment-naive recurrent/metastatic nasopharyngeal cancer patients were randomly allocated to either PD-1 inhibitor tislelizumab or placebo plus gemcitabine and cisplatin. ORIENT-11 (NCT03607539) was a phase 3 study that enrolled patients from 47 centers in China who had previously untreated locally advanced/metastatic nonsquamous non-small cell lung cancer. Patients were randomized to PD-1 inhibitor sintilimab or placebo plus pemetrexed and platinum. We compared several immune-related biomarkers, including IFN-γ signature, PD-L1, cytolytic score (CYT), T-cell inflamed gene expression profile, B cell infiltration, and ESTIMATE immune score, and explored the optimal predictive threshold. Results: In the RATIONALE-309 and ORIENT-11 trials, 247 and 171 patients with RNA-seq data were analyzed, respectively. Although the addition of Chemo to IO could theoretically benefit tumors with low immune activation, we hypothesized that patients might not benefit from IO+Chemo if their activation level is extremely low. We first tested the predictiveness of several thresholds (20-50th quantiles) for each selected biomarker in the two RCTs. We found that low CYT and B cell are the two best indicators for identifying a lack of survival benefits from IO+Chemo versus Chemo, especially when using 20-30th quantile as threshold. To incorporate their distinct biological information and predictive value, we combined their genes into a single gene signature to represent immune activation. We found that patients in the lower quantile (<20th) showed no benefit of IO+Chemo over Chemo in terms of overall survival (OS) (RATIONALE-309: HR=1.01, P=0.99; ORIENT-11: HR=1.54, P=0.31) and progression-free survival (PFS) (RATIONALE-309: HR=0.76, P=0.42; ORIENT-11: HR=1.30, P=0.52). Moreover, this lack of advantage was maintained in key subgroups such as PD-L1-positive patients and former/current smokers across the two RCTs. Conclusions: Based on the two large-scale RCTs, our findings have implications for the selection of biomarkers when identifying suitable candidates for IO+Chemo treatment. B cells and CYT are crucial components required for the efficacy of IO+Chemo.

Identification of Immune-Related Gene Signature in Schizophrenia.

Schizophrenia (SCZ) is a type of psychiatric disorder characterized by multiple symptoms. Our aim is to decipher the relevant mechanisms of immune-related gene signatures in SCZ. The SCZ dataset and its associated immunoregulatory genes were retrieved using Gene Expression Omnibus (GEO) and single-sample gene set enrichment analysis (ssGSEA). Co-expressed gene modules were determined through weighted gene correlation network analysis (WGCNA). To elucidate the functional characteristics of these clusters, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used. Additionally, gene set enrichment analysis (GSEA) and Gene Set Variation Analysis (GSVA) were conducted to identify enriched pathways for the immune subgroups. A protein-protein interaction (PPI) network analysis was performed to identify core genes relevant to SCZ. A significantly higher immune score was observed in SCZ compared to control samples. Seven distinct gene modules were identified, with genes highlighted in green selected for further analysis. Using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) method, degrees of immune cell adhesion and accumulation related to 22 different immune cell types were calculated. Significantly enriched bioprocesses concerning the immunoregulatory genes with differential expressions included interferon-beta, IgG binding, and response to interferon-gamma, according to GO and KEGG analyses. Eleven hub genes related to immune infiltration emerged as key players among the three top-ranked GO terms. This study underscores the involvement of immunoregulatory reactions in SCZ development. Eleven immune-related genes (IFITM1 (interferon induced transmembrane protein 1), GBP1 (guanylate binding protein 1), BST2 (bone marrow stromal cell antigen 2), IFITM3 (interferon induced transmembrane protein 3), GBP2 (guanylate binding protein 2), CD44 (CD44 molecule), FCER1G (Fc epsilon receptor Ig), HLA-DRA (major histocompatibility complex, class II, DR alpha), FCGR2A (Fc gamma receptor IIa), IFI16 (interferon gamma inducible protein 16), and FCGR3B (Fc gamma receptor IIIb)) were identified as hub genes, representing potential biomarkers and therapeutic targets associated with the immune response in SCZ patients.

Construction and Evaluation of a Prognostic Risk Prediction Model of Pancreatic Ductal Adenocarcinoma Based on Immune-Related Genes

Objective To construct a risk prediction model by integrating the molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) and immune-related genes.Methods With GSE71729 data set (n=145) as the training set,the differentially expressed genes and differential immune-related genes between the squamous and non-squamous subtypes of PDAC were integrated to construct a regulatory network,on the basis of which five immune marker genes regulating the squamous subtype were screened out.An integrated immune score (IIS) model was constructed based on patient survival information and immune marker genes to predict the clinical prognosis of PDAC patients,and its predictive performance was tested with 5 validation sets (n=758).Results PDAC patients were assigned into high risk and low risk groups according to the IIS.In both training and validation sets,the overall survival of patients in the high risk group was shorter than that in the low risk group (both P<0.001).The multivariable Cox regression showed that IIS was an independent prognostic factor for PDAC (HR=2.16,95%CI=1.50-3.10,P<0.001).Conclusion IIS can be used for risk stratification of PDAC patients and may become a potential prognostic marker for PDAC.

Integrative analyses of multi-omics data constructing tumor microenvironment and immune-related molecular prognosis model in human colorectal cancer

The increasing prevalence and incidence of colorectal cancer (CRC), particularly in young adults, underscore the imperative to comprehend its fundamental mechanisms, discover novel diagnostic and prognostic markers, and enhance therapeutic strategies. Here, we integrated multi-omics data, including gene expression, somatic mutation data and DNA methylation data, to unravel the intricacies of tumor microenvironment (TME) in CRC and search for novel prognostic markers. By calculating the immune score for each patient from the expression profile, we delineated the differential immune cell fraction, constructed an immune-related multi-omics atlas, and identified molecular characteristics. The entire colorectal dataset (n = 343) was randomly divided into training (n = 249) and testing datasets (n = 94). We screened 144 immune-related genes, 6 mutant genes, and 38 methylation probes associated with overall survival (OS). These makers were then incorporated into a 10-gene prognostic model using Lasso and Cox regression in the training dataset, and the model's performance was evaluated in an independent validation dataset. The model exhibited satisfactory results (average concordance index [C-index] = 0.77), with the average 1-year, 3-year, and 5-year AUCs being 0.79, 0.76, and 0.76 in the training dataset and 0.74, 0.80, and 0.90 in the testing dataset. Furthermore, the prognostic model demonstrated applicability in guiding chemotherapy for CRC patients and exhibited a degree of pan-cancer utility in risk stratification. In conclusion, our integrated analysis of multi-omics data revealed immune-related genetic and epigenetic characteristics of the TME. We propose an integrative prognostic model that can stratify risk and guide chemotherapy for CRC patients. The generalizability of the model in risk stratification across different cancer types was validated in Pan-Cancer cohort.

A disulfidptosis-related classification and risk signature identifies immunotherapy biomarkers and predicts prognosis in gastric cancer: An observational study.

Gastric cancer (GC) is one of the most prevalent types of cancer globally, often detected at advanced stages. However, its prognosis remains poor, necessitating the exploration of new biomarkers. Disulfidptosis, a recently identified form of programmed cell death, has not yet been investigated in relation to GC and its associated mechanisms. We analyzed and identified potential associations between disulfidptosis genes and GC clinical risk using TCGA (The Cancer Genome Atlas)-STAD (stomach adenocarcinoma) as the training set and GSE84433 as the validation set. In addition, we explored the prognostic value and potential biological mechanisms of disulfide genes in GC by consensus clustering, enrichment analysis, mutation histology analysis and immune infiltration analysis. Finally, we constructed a disulfidptosis-related risk signature (DRRS) to assess the association between risk class, survival prognosis, and immune infiltration. By utilizing data from 19 disulfidptosis-related genes, we successfully identified subgroups of C1 and C2 patients through consensus clustering. Notably, the 2 groups exhibited significant variations in terms of survival rates, immune scores, and immune cell infiltration. Subsequently, we developed a DRRS via LASSO (least absolute shrinkage and selection operator) regression analysis, incorporating PRICKLE1, NRP1, APOD, MISP3, and SERPINE1. This scoring system effectively distinguished individuals with high and low risks, as verified with a validation set. These findings strongly indicate a close association between disulfidptosis and the immune microenvironment of GC tumors. Moreover, the DRRS demonstrated commendable predictive capabilities for the survival outcomes of GC patients. In this study, we have identified the association between different subtypes of disulfidptosis and alterations in the GC immunotumour microenvironment. Furthermore, we have developed and verified the accuracy of the DRRS, a valuable tool for predicting survival, biological function, and immune infiltration in patients with GC. These findings contribute to a better comprehension of disulfidptosis and offer potential opportunities for innovative approaches in GC treatment.

RPN1: a pan-cancer biomarker and disulfidptosis regulator.

Elevated expression of SLC7A11, in conjunction with glucose deprivation, has revealed disulfidptosis as an emerging cell death modality. However, the prevalence of disulfidptosis across tumor cell lines, irrespective of SLC7A11 levels, remains uncertain. Additionally, deletion of the ribophorin I (RPN1) gene imparts resistance to disulfidptosis, yet the precise mechanism linking RPN1 to disulfidptosis remains elusive. The aim of this study is to determine the mechanism of RPN1-induced disulfidptosis and to determine the possibility of RPN1 as a pan-cancer marker. We hypothesized the widespread occurrence of disulfidptosis in various tumor cells, and proposed that RPN1-mediated disulfidptosis may be executed through cell skeleton breakdown. Experimental validation was conducted via flow cytometry, immunofluorescence, and western blot techniques. Furthermore, given RPN1's status as an emerging cell death marker, we utilized bioinformatics to analyze its expression in tumor tissues, clinical relevance, mechanisms within the tumor microenvironment, and potential for immunotherapy. Conducting experiments on breast cancer (MDA-MB-231) and lung cancer (A549) cell lines under glucose-starved conditions, we found that RPN1 primarily induces cell skeleton breakdown to facilitate disulfidptosis. RPN1 demonstrated robust messenger RNA (mRNA) expression across 16 solid tumors, validated by data from 12 tumor types in the Gene Expression Omnibus (GEO). Across 12 cancer types, RPN1 exhibited significant diagnostic potential, particularly excelling in accuracy for glioblastoma (GBM). Elevated RPN1 expression in tumor tissues was found to correlate with improved overall survival (OS) in certain cancers [diffuse large B-cell lymphoma (DLBC) and thymoma (THYM)] but poorer prognosis in others [adrenocortical carcinoma (ACC), kidney chromophobe (KICH), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), and pancreatic adenocarcinoma (PAAD)]. RPN1 is enriched in immune-related pathways and correlates with immune scores in tumor tissues. In urothelial carcinoma (UCC), RPN1 demonstrates potential in predicting the efficacy of anti-programmed cell death ligand 1 (PD-L1) immune therapy. This study underscores RPN1's role in facilitating disulfidptosis, its broad relevance as a pan-cancer biomarker, and its association with the efficacy of anti-PD-L1 immune therapy.

Enhanced immunity effect of Korean Red Ginseng capsule: A randomized, double-blind and placebo-controlled clinical trial

BackgroundAs a physiological function of body, immunity can maintain health by identifying itself and excluding others. With economic development and increasingly fierce social competition, the number of sub-healthy population is gradually increasing, and the most basic problem exposed is human hypoimmunity. Hypoimmunity can be manifested as often feeling tired, catching colds, mental depression, etc. In order to enhance immunity, eating healthy foods with the effect of enhancing immunity may become an effective choice. KRG has pharmacological effects of enhancing immunity. Because the screening and evaluation method of immune population are not unified, there are relatively few KRG immunity tests for sub-health population. It is of great significance to study the effect of KRG on people with hypoimmunity to improve sub-health status. MethodsThis was a 180-day, randomized, double-blind, placebo-controlled clinical trial. According to the trial scheme design, 119 qualified subjects were included and randomly divided into the test group taking KRG and the placebo control group. Subjects need to check safety indicators (blood pressure and heart rate, blood routine, liver and kidney function, urine routine and stool routine) and efficacy indicators (main and secondary) inspection at baseline, efficacy indicators inspection during the mid-term of the test (90th days of administration), safety and efficacy indicators inspection after the test (180th days of administration). ResultsAfter the test, the safety indicators of placebo control group and KRG test group were basically within the normal range, and there is no significant difference in fireness score between the two groups. Through follow-up interviews, it was found that the subjects in the test group and the control group had no adverse reactions and allergic reactions such as nausea, flatulence, diarrhea, and abdominal pain during the test period. Self-comparison of the test group, the results of the main efficacy indicators: (1) immune related health scores were significantly improved in the mid-term and after the test (P < 0.01), (2) CD3 and CD4/CD8 increased significantly after the test (P < 0.05), (3) IgG, IgA, IgM and WBC increased significantly in the mid-term and after the test (P < 0.01); the results of the secondary efficacy indicators: (1) TNF-α decreased significantly in the mid-term (P < 0.05), IFN-γ decreased significantly in the mid-term (P < 0.01), (2) NK increased significantly in the mid-term and after the test (P < 0.05), (3) monocyte increased significantly in the mid-term and after the test (P < 0.01). Inter-group comparison of the test group and the control group, the results of the main efficacy indicators: (1) immune related health scores were higher than that of the control group in the mid-term and after the test (P < 0.01), (2) IgA of the test group was higher than that of the control group in the mid-term and after the test (P < 0.05); the results of the secondary efficacy indicators: (1) WBC of the test group was higher than that of the control group in the mid-term (P < 0.05); (2) monocytes of the test group were higher than that of the control group in the mid-term and after the test (P < 0.05), neutrophils of the test group were higher than that of the control group in the mid-term (P < 0.05). ConclusionTaking KRG has no adverse effects on the health of the subjects. According to the standard of clinical trial scheme, the immune related health scores and IgA in the main efficacy indicators were positive, which shows that KRG is helpful in enhancing human immunity.

Prognostic Value of the Gustave Roussy Immune Score in Lung Cancer: A Meta-Analysis

Purpose To clarify the prognostic role of the Gustave Roussy immune (GRIm) score in lung cancer. Methods The PubMed, Embase, Web of Science, and China National Knowledge Infrastructure databases were searched up to March 30, 2024. The primary outcomes included overall survival (OS) and progression-free survival (PFS). Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to evaluate the associations between the GRIm score and survival, and subgroup analyses were performed based on pathological type (non-small cell lung cancer vs. small cell lung cancer), tumor stage (advanced vs. limited stage) and treatment approach (immune checkpoint inhibitor vs. surgery vs. chemotherapy). Results Eight studies with 1,333 participants were included. The pooled results showed that a higher GRIm score predicted worse OS (HR = 1.96, 95% CI: 1.54–2.49, P < 0.001) and PFS (HR = 1.64, 95% CI: 1.22–2.21, P = 0.001). Subgroup analyses for OS and PFS showed similar results. However, subgroup analyses for PFS indicated that the association between the GRIm score and PFS was nonsignificant among patients with small cell lung cancer (P = 0.114) and among patients treated with chemotherapy (P = 0.276). Conclusion The GRIm score might serve as a novel prognostic factor for lung cancer. Additional studies are still needed to verify these findings.

Prediction of the survival status and tumor microenvironment in colorectal cancer through genotyping analysis based on toll-like receptors.

Colorectal cancer (CRC) ranks third in both the incidence and mortality rates among male and female cancers, and it is the leading digestive system cancer. Due to the inter- and intratumor heterogeneity of cancer, the TNM system is insufficient for predicting prognosis, necessitating the use of molecular biomarkers for prognostic prediction. Toll-like receptors (TLRs) have been associated with CRC survival rates. This study focused on the investigation of the role and potential value of TLRs in CRC genotyping to aid in immunotherapy for CRC patients. Differential gene expression analysis was performed on CRC transcriptomic data from The Cancer Genome Atlas database. TLRs were referred from the literature, and their intersection with differentially expressed genes (DEGs) in CRC yielded TLR-DEGs. The expression patterns of TLR-DEGs were predicted using the STRING website, and copy number variations of TLR-DEGs were analyzed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on TLR-DEGs. ConsensusClusterPlus R package was used for clustering CRC patients, and ESTIMATE and GSEAbase were employed to analyze immune characteristics of different subtypes. Immune phenotyping scores and tumor immune dysfunction and exclusion scores were evaluated. DEGs of different subtypes were analyzed, followed by GO and KEGG enrichment analyses, the protein-protein interaction (PPI) network analysis, and further selection of hub genes. The sensitivity of drugs was assessed using the identified hub genes. We identified 37 TLR-DEGs, and the PPI analysis revealed their coexpression, although they were distributed on different chromosomes. Enrichment analyses indicated that the 37 TLR-DEGs were linked to cancer cell immune response. Based on these TLR-DEGs, CRC patients were classified into three subtypes. Cluster2 exhibited lower survival rates and higher immune infiltration levels and predicted poorer response to immune checkpoint inhibitor therapy. The intersection of DEGs from cluster2 and cluster1 with DEGs from cluster2 and cluster3 yielded a set of 426 commonly shared DEGs. Enrichment analyses revealed that these shared DEGs might regulate immune cell viability. Eight common hub genes for different subtypes were further identified to predict drug-related correlations. The developed TLR genotyping was used to predict the survival status and tumor microenvironment of CRC, providing a foundation for understanding the molecular mechanisms of TLR signaling and deepening its clinical significance.

A novel fatty acid metabolism-related signature identifies MUC4 as a novel therapy target for esophageal squamous cell carcinoma

Fatty acid metabolism has been identified as an emerging hallmark of cancer, which was closely associated with cancer prognosis. Whether fatty acid metabolism-related genes (FMGs) signature play a more crucial role in biological behavior of esophageal squamous cell carcinoma (ESCC) prognosis remains unknown. Thus, we aimed to identify a reliable FMGs signature for assisting treatment decisions and prognosis evaluation of ESCC. In the present study, we conducted consensus clustering analysis on 259 publicly available ESCC samples. The clinical information was downloaded from The Cancer Genome Atlas (TCGA, 80 ESCC samples) and Gene Expression Omnibus (GEO) database (GSE53625, 179 ESCC samples). A consensus clustering arithmetic was used to determine the FMGs molecular subtypes, and survival outcomes and immune features were evaluated among the different subtypes. Kaplan–Meier analysis and the receiver operating characteristic (ROC) was applied to evaluate the reliability of the risk model in training cohort, validation cohort and all cohorts. A nomogram to predict patients’ 1-year, 3-year and 5-year survival rate was also studied. Finally, CCK-8 assay, wound healing assay, and transwell assay were implemented to evaluate the inherent mechanisms of FMGs for tumorigenesis in ESCC. Two subtypes were identified by consensus clustering, of which cluster 2 is preferentially associated with poor prognosis, lower immune cell infiltration. A fatty acid (FA) metabolism-related risk model containing eight genes (FZD10, TACSTD2, MUC4, PDLIM1, PRSS12, BAALC, DNAJA2 and ALOX12B) was established. High-risk group patients displayed worse survival, higher stromal, immune and ESTIMATE scores than in the low-risk group. Moreover, a nomogram revealed good predictive ability of clinical outcomes in ESCC patients. The results of qRT-PCR analysis revealed that the MUC4 and BAALC had high expression level, and FZD10, PDLIM1, TACSTD2, ALOX12B had low expression level in ESCC cells. In vitro, silencing MUC4 remarkably inhibited ESCC cell proliferation, invasion and migration. Our study fills the gap of FMGs signature in predicting the prognosis of ESCC patients. These findings revealed that cluster subtypes and risk model of FMGs had effects on survival prediction, and were expected to be the potential promising targets for ESCC.

Exploring PD-1+ and tissue resident memory T cells in atherosclerotic diseases

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): Health Holland Visual Sonics Background Checkpoint inhibitor therapy (ICI), a potent oncologic treatment, has been associated with an enhanced risk for atherosclerotic cardiovascular events and increased plaque progression. Among the key players affected by ICI are tissue resident memory T cells (Trm), a subset residing in tissues and characterized by CD44, CD69, PD-1, and CD103. Although Trms have been associated with chronic inflammatory conditions, their role in atherosclerosis and accelerated atherosclerosis, as observed in postinterventional diseases such as vein graft disease, remains elusive Here, we present an investigation into the role of PD-1 positive cells and Trms in accelerated atherosclerosis as found in vein graft disease. Methods Human carotid atherosclerotic plaques were stained with a MOVAT stain and scored to determine plaque stability. 25 Stable and 25 unstable plaques were stained for CD3+ T cells and PD-1. Hypercholesterolemic ApoE3*Leiden mice underwent vein bypass surgery and were sacrificed at t7, t14, t28 (n=6 per time point). The vein grafts were processed for flow cytometry to quantify the Trms population, using markers including CD4+, CD8+, CD44+, CD25-, CD69+, CD103+ and PD-1+. Hypercholesterolemic ApoE3*Leiden mice underwent vein bypass surgery. Biweekly, mice received ICI (anti-PD-L1) or control antibody (n=7). After 28 days vein graft morphology was assed using MOVAT staining and vein graft inflammation by staining MAC3 and CXCL10. Results Within human atherosclerotic plaques, PD-1-expressing T cells were predominantly located in the shoulder regions of the necrotic core. Unstable lesions exhibited significantly higher levels of PD-1 expressing CD3+ T cells compared to stable lesions (p=0.001). Furthermore, the percentage of PD-1 expression positively correlated with immune cell infiltration scores (p=0.019). In murine vein grafts, Trms were identified through flow cytometry. A trend was observed indicating an increase in the number of CD4 and CD8 Trms at t14 compared to t7 Trms (CD8+, CD44+, CD69+/CD103+). At t28, the number of CD4 and CD8 Trms decreased compared to t14, with PD1+ expressing Trms showing a similar trend, peaking at t14. Vein grafts in mice receiving anti-PD-L1 mAb showed no morphological differences compared to the control group. However, a significant difference in inflammation was noted in the anti-PD-L1 treated group, characterized by a higher number of macrophages (p=0.02) and increased expression of CXCL10 (p=0.008). Conclusion Here we demonstrate the association of PD-1+ T cells with unstable lesions and reveals that Trms increase during the inflammatory phase and decrease during the stabilizing phase of accelerated atherosclerosis. Trms emerge as potential detrimental targets of ICI therapy, which is characterized by increased inflammation.

Multi-omics comprehensive analysis reveals the predictive value of N6-methyladenosine- related genes in prognosis and immune escape of bladder cancer.

N6-methyladenosine (m6A) is the most frequent RNA modification in mammals, and its role in bladder cancer (BC) remains rarely revealed. To predict the value of m6A-related genes in prognosis and immunity in BC. We performed multiple omics analysis of 618 TCGA and GEO patients and used principal component analysis (PCA) to calculate the m6A score for BC patients. We described the multiple omics status of 23 m6A methylation-related genes (MRGs), and four m6A clusters were identified, which showed significant differences in immune infiltration and biological pathways. Next, we intersected the differential genes among m6A clusters, and 11 survival-related genes were identified, which were used to calculate the m6A score for the patients. We found that the high-score (HS) group showed lower tumor mutation burden (TMB) and TP53 mutations and better prognosis than the low-score (LS) group. Lower immune infiltration, higher expression of PD-L1, PD-1, and CTLA4, and higher immune dysfunction and immune exclusion scores were identified in the LS group, suggesting a higher possibility of immune escape. Finally, the experimental verification shows that the m6A related genes, such as IGFBP1, plays an important role in the growth and metastasis of bladder cancer. These findings revealed the important roles of m6A MRGs in predicting prognosis, TMB status, TP53 mutation, immune functions and immunotherapeutic response in BC.

Prognostic impact and immunotherapeutic implications of NETosis-related prognostic model in clear cell renal cell carcinoma

BackgroundThe ramifications of necroptosis on the prognostication of clear cell renal cell carcinoma (ccRCC) remain inadequately expounded.MethodsA prognostic model delineating the facets of necroptosis in ccRCC was constructed, employing a compendium of algorithms. External validation was effectuated using the E-MTAB-1980 dataset. The exploration of immune infiltration scores was undertaken through the exploitation of multiple algorithms. Single-cell RNA sequencing data were procured from the GSE171306 dataset. Real-time quantitative PCR (RT-qPCR) was engaged to scrutinize the differential expression of SLC25A37 across cancer and paracancer tissues, as well as diverse cell lines. Assessments of proliferative and metastatic alterations in 769-P and 786-O cells were accomplished through Cell Counting Kit-8 (CCK8) and wound healing assays.ResultsThe necroptosis-related signature (NRS) emerges as a discerning metric, delineating patients’ immune attributes, tumor mutation burden, immunotherapy response, and drug susceptibility. Single-cell RNA sequencing analysis unveils the marked enrichment of SLC25A37 in tumor cells. Concurrently, RT-qPCR discloses the overexpression of SLC25A37 in both ccRCC tissues and cell lines. SLC25A37 knockdown mitigates the proliferative and metastatic propensities of 769-P and 786-O cells, as evidenced by CCK8 and wound healing assays.ConclusionThe NRS assumes a pivotal role in ascertaining the prognosis, tumor mutation burden, immunotherapy response, drug susceptibility, and immune cell infiltration features of ccRCC patients. SLC25A37 emerges as a putative player in immunosuppressive microenvironments, thereby providing a prospective avenue for the design of innovative immunotherapeutic targets for ccRCC.

Identification of a Macrophage marker gene signature to evaluate immune infiltration and therapeutic response in hepatocellular carcinoma

BackgroundOnly a minority of hepatocellular carcinoma (HCC) patients can benefit from systemic regimens. Macrophages, which abundantly infiltrate in HCC, could mediate tumour microenvironment remodelling and immune escape, proving to be powerful weapons in combating HCC. Thus, a deeper understanding of macrophages is necessary for improving existing antitumour treatments. MethodsWith a series of bioinformatic approaches, we comprehensively explored the role of macrophage-related genes in human HCCs from multiple single-cell and bulk RNA sequencing datasets. Unsupervised clustering was performed to cluster the macrophage marker genes (MMGs). GSVA and functional enrichment analysis were used to elucidate the functional differences among the MMG-associated clusters. Subsequently, a component analysis algorithm was used to construct a Macrosig score, and the prognosis, biological characteristics, mutation profile, TME cell infiltration status and drug response of patients with different Macrosig scores were further analysed. ResultsWe identified 13 MMGs in 574 HCC samples, based on which three MMG-associated clusters were defined. Overall survival time, clinicopathological features and immune infiltration scores differed among the different clusters. On this basis, 12 hub genes were identified among these clusters; subsequently, a scoring system was constructed to determine the Macrosig score. Importantly, patients with low-Macrosig scores, characterized by increased immune infiltration, increased mutation frequency and increased immune checkpoint expression, including CTLA-4, LAG3, PDCD1 and TIGIT, exhibited enhanced efficacy of immunotherapy when validated in an external database. Moreover, a low-Macrosig score indicates increased sensitivity to AZD.2281, A.443654, ABT.263, ABT.888, AG.014699 and ATRA, while a high Macrosig score indicates increased sensitivity to AZD6482, AKT inhibitor VIII, AS601245, AZ628, AZD.0530 and AZD6244. ConclusionsA novel scoring system was constructed to guide more effective prognostic evaluation and tailoring therapeutic regimens for HCC patients.

The N6-methyladenosine methylation landscape stratifies breast cancer into two subtypes with distinct immunological characteristics.

N6-methyladenosine (m6A) methylation modification affects the tumorigenesis and metastasis of breast cancer (BC). This study investigated the association between m6A regulator-mediated methylation modification patterns and characterization of the tumour microenvironment in BC, as well as their prognostic importance. Public gene expression data and clinical annotations were collected from The Cancer Genome Atlas (TCGA) database, the Gene Expression Omnibus website and the METABRIC program. We analysed the genetic expression, gene-gene interactions, gene mutations and copy number variations using R software. The data were screened for risk genes using the Cox risk regression model, and we developed an algorithm for risk score and its predictive value. Compared to adjacent normal tissue, we identified 16 differentially expressed m6A regulators in BC, including six writers and 10 readers. Under unsupervised clustering, two distinguished modification patterns were identified, cluster C1 and C2. Compared to m6A cluster C2, cluster C1 was found to be more involved in immune-related pathways, with a relatively higher immune score and stromal score (P < 0.05). Patients were divided into two groups based on their risk scores for survival analysis. The patients in the high-risk score group had significantly worse overall survival than patients in the low-risk score group, (P < 0.0001). The TCGA database validation revealed the same prognostic tendency. In summary, our study showed distinct m6A regulator modification patterns contribute to the immunological heterogeneity and diversity of BC. The development of m6A gene signatures and the m6A score aid in the prognostic prediction of patients with BC.

Stem cell index-based RiskScore model for predicting prognosis in thyroid cancer and experimental verification

ObjectiveAn mRNA expression-based stemness index (mRNAsi) has been developed to characterize cancer stemness. However, the predictive value of mRNAsi-based signature in therapeutic resistance and immunotherapy in thyroid cancer (THCA) remains unclarified. This study evaluated and validated the role of mRNAsi in drug sensitivity, its relationship between mRNAsi and THCA clinical features and immunity based on bioinformatics. MethodsBased on transcriptome data of THCA patients from the Tumor Genome Atlas Project (TCGA) database, and expression data of multifunctional stem cell samples from the Progenitor Cell Biology Consortium (PCBC) databases, mRNAsi was calculated by the " one class logistic regression (OCLR)" method, Molecular subtypes of TCGA-THCA samples were identified with mRNAsi-related genes using ConsensusClusterPlus method. The gene mutation, clinical characteristics, immune characteristics, TIDE and drug sensitivity were compared among molecular subtypes. A prognostic model was designed with Lasso cox method. Modulation of malignant phenotype of THCA cell lines by model characterization genes is validated by CCK-8, flow cytometry. DNA methylation disorder in promoter region was analyzed between risk groups. The model was validated for survival in the internal Test dataset, while TCGA pan-cancer and immunotherapy datasets were further employed to validate the performance of this model. ResultsWe obtained a total of 78 stem cell samples, each containing the expression profile of 8087 mRNA genes. Based on mRNAsi, THCA was divided into 3 subtypes. Subtype C2 had the poorest prognosis and highest immune score, while subtype C3 had the best prognosis, lowest mRANsi and highest TIDE score. Patients in subtype C2 showed higher sensitivity to Cisplatin, Erlotinib, Paclitaxel, and Lapatinib. The prognostic signature was generated using 5 mRNAsi-related genes, which could predict prognosis for THCA. qRT-PCR results showed that the expression of 5 genes were various in Hth7 and KTC-1 cells, and inhibition CELSR3 expression increased percentage of apoptosis in Hth7 and KTC-1 cells. mRNAsi related DNA methylation sites were mainly enriched in tumor related pathways. Good performance of this model was validated in Test dataset, pan-cancer and immunotherapy datasets. ConclusionThis study identified three subtypes for classification and developed a prognostic model with mRNAsi-related genes, which provided great potential for prognosis and immunotherapy prediction.

Identification and clinical validation of diverse cell-death patterns-associated prognostic features among low-grade gliomas

Low-grade glioma (LGG) is heterogeneous at biological and transcriptomic levels, and it is still controversial for the definition and typing of LGG. Therefore, there is an urgent need for specific and practical molecular signatures for accurate diagnosis, individualized therapy, and prognostic evaluation of LGG. Cell death is essential for maintaining homeostasis, developing and preventing hyperproliferative malignancies. Based on diverse programmed cell death (PCD) related genes and prognostic characteristics of LGG, this study constructed a model to explore the mechanism and treatment strategies for LGG cell metastasis and invasion. We screened 1161 genes associated with PCD and divided 512 LGG samples into C1 and C2 subtypes by consistent cluster analysis. We analyzed the two subtypes' differentially expressed genes (DEGs) and performed functional enrichment analysis. Using R packages such as ESTIMATE, CIBERSOTR, and MCPcounter, we assessed immune cell scores for both subtypes. Compared with C1, the C2 subtype has a poor prognosis and a higher immune score, and patients in the C2 subtype are more strongly associated with tumor progression. LASSO and COX regression analysis screened four characteristic genes (CLU, FHL3, GIMAP2, and HVCN1). Using data sets from different platforms to validate the four-gene feature, we found that the expression and prognostic correlation of the four-gene feature had a high degree of stability, showing stable predictive effects. Besides, we found downregulation of CLU, FHL3, and GIMAP2 significantly impairs the growth, migration, and invasive potential of LGG cells. Take together, the four-gene feature constructed based on PCD-related genes provides valuable information for further study of the pathogenesis and clinical treatment of LGG.