High Immune Score Research Articles

Novel Cuproptosis-Related Gene Signature for Precise Identification of High-Risk Populations in Low-Grade Gliomas.

Patients with low-grade glioma (LGG) have wildly varying average lifespans. However, no effective way exists for identifying LGG patients at high risk. Cuproptosis is a recently described form of cell death associated with the abnormal aggregation of lipid acylated proteins. Few investigations have been conducted on cuproptosis-associated genes and LGG thus far. The purpose of this research is to establish a predictive model for cuproptosis-related genes in order to recognise LGG populations at high risk. We analyzed 926 LGGs from 2 public datasets, all of which were RNA sequencing datasets. On the basis of immune scores, the LGG population was split into different risk categories with X-tile. LASSO and Cox regressions were employed to filter cuproptosis-associated genes and construct prediction models. The accuracy of the predictive models was measured by using TCGA internal validation set and the CGGA external validation set. In addition, LGG immune cell infiltration was viewed using CIBERSORT and ssGSEA algorithms and correlation analysis was done with cuproptosis-related genes. Finally, immune escape capacity in LGG low- and high-risk groups was evaluated using the TIDE method. The prediction model constructed by four cuproptosis-related genes was used to identify high-risk populations in LGG. It performed well in training and all validation sets (AUC values: 0.915, 0.894, and 0.774). Meanwhile, we found that FDX1 and ATP7A in the four cuproptosis-related genes were positively correlated with immune response, while GCSH and ATP7B were opposite. In addition, the high immune score group had a lower TIDE score, indicating that their immune escape capacity was weak. High-risk individuals in LGG can be reliably identified by the model based on cuproptosis-related genes. Furthermore, cuproptosis is closely related to tumor immune microenvironment, which gives a novel approach to treating LGG.

Bioinformatics construction and experimental validation of a cuproptosis-related lncRNA prognostic model in lung adenocarcinoma for immunotherapy response prediction

Cuproptosis is a newly form of cell death. Cuproptosis related lncRNA in lung adenocarcinoma (LUAD) has also not been fully elucidated. In the present study, we aimed to construct a prognostic signature based on cuproptosis-related lncRNA in LUAD and investigate its association with immunotherapy response. The RNA-sequencing data, clinical information and simple nucleotide variation of LUAD patients were obtained from TCGA database. The LASSO Cox regression was used to construct a prognostic signature. The CIBERSORT, ESTIMATE and ssGSEA algorithms were applied to assess the association between risk score and TME. TIDE score was applied to reflect the efficiency of immunotherapy response. The influence of overexpression of lncRNA TMPO-AS1 on A549 cell was also assessed by in vitro experiments. The lncRNA prognostic signature included AL606834.1, AL138778.1, AP000302.1, AC007384.1, AL161431.1, TMPO-AS1 and KIAA1671-AS1. Low-risk group exhibited much higher immune score, stromal score and ESTIMATE score, but lower tumor purity compared with high-risk groups. Also, low-risk group was associated with a much higher score of immune cells and immune related function sets, indicating an immune activation state. Low-risk patients had relative higher TIDE score and lower TMB. External validation using IMvigor210 immunotherapy cohort demonstrated that low-risk group had a better prognosis and might more easily benefit from immunotherapy. Overexpression of lncRNA TMPO-AS1 promoted the proliferation, migration and invasion of A549 cell line. The novel cuproptosis-related lncRNA signature could predict the prognosis of LUAD patients, and helped clinicians stratify patients appropriate for immunotherapy and determine individual therapeutic strategies.

Five-hub genes identify potential mechanisms for the progression of asthma to lung cancer.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Identification of immune related gene signature for predicting prognosis of cholangiocarcinoma patients.

To identify the gene subtypes related to immune cells of cholangiocarcinoma and construct an immune score model to predict the immunotherapy efficacy and prognosis for cholangiocarcinoma. Based on principal component analysis (PCA) algorithm, The Cancer Genome Atlas (TCGA)-cholangiocarcinoma, GSE107943 and E-MTAB-6389 datasets were combined as Joint data. Immune genes were downloaded from ImmPort. Univariate Cox survival analysis filtered prognostically associated immune genes, which would identify immune-related subtypes of cholangiocarcinoma. Least absolute shrinkage and selection operator (LASSO) further screened immune genes with prognosis values, and tumor immune score was calculated for patients with cholangiocarcinoma after the combination of the three datasets. Kaplan-Meier curve analysis determined the optimal cut-off value, which was applied for dividing cholangiocarcinoma patients into low and high immune score group. To explore the differences in tumor microenvironment and immunotherapy between immune cell-related subtypes and immune score groups of cholangiocarcinoma. 34 prognostic immune genes and three immunocell-related subtypes with statistically significant prognosis (IC1, IC2 and IC3) were identified. Among them, IC1 and IC3 showed higher immune cell infiltration, and IC3 may be more suitable for immunotherapy and chemotherapy. 10 immune genes with prognostic significance were screened by LASSO regression analysis, and a tumor immune score model was constructed. Kaplan-Meier (KM) and receiver operating characteristic (ROC) analysis showed that RiskScore had excellent prognostic prediction ability. Immunohistochemical analysis showed that 6 gene (NLRX1, AKT1, CSRP1, LEP, MUC4 and SEMA4B) of 10 genes were abnormal expressions between cancer and paracancer tissue. Immune cells infiltration in high immune score group was generally increased, and it was more suitable for chemotherapy. In GSE112366-Crohn's disease dataset, 6 of 10 immune genes had expression differences between Crohn's disease and healthy control. The area under ROC obtained 0.671 based on 10-immune gene signature. Moreover, the model had a sound performance in Crohn's disease. The prediction of tumor immune score model in predicting immune microenvironment, immunotherapy and chemotherapy in patients with cholangiocarcinoma has shown its potential for indicating the effect of immunotherapy on patients with cholangiocarcinoma.

Exploration of prognostic biomarkers in head and neck squamous cell carcinoma microenvironment from TCGA database.

Immune checkpoint blockade (ICB) therapies have redefined human cancer treatment, including for head and neck squamous cell carcinoma (HNSCC). However, clinical responses to various immune checkpoint inhibitors are often accompanied by immune-related adverse events (irAEs). Therefore, it is crucial to obtain a comprehensive understanding of the association between different immune tumor microenvironments (TMEs) and the immunotherapeutic response. The research data were obtained from The Cancer Genome Atlas (TCGA) database. We applied RNA-seq genomic data from tumor biopsies to assess the immune TME in HNSCC. As the TME is a heterogeneous system that is highly associated with HNSCC progression and clinical outcome, we relied on the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm to calculate immune and stromal scores that were evaluated based on the immune or stromal components in the TME. Then, the Tumor Immune Dysfunction and Exclusion algorithm (TIDE) was used to predict the benefits of ICB to each patient. Finally, we identified specific prognostic tumor-infiltrating immune cells (TIICs) by quantifying the cellular composition of the immune response in HNSCC and its association to survival outcome, using the CIBERSORT algorithm. Utilizing the HNSCC cohort of the TCGA database and TIDE and ESTIMATE algorithm-derived immune scores, we obtained a list of microenvironment-associated lncRNAs that predicted different clinical outcomes in HNSCC patients. We validated these correlations in a different HNSCC cohort available from the TCGA database and provided insight into the prediction of response to ICB therapies in HNSCC. This study confirmed that CD8+ T cells were significantly associated with better survival in HNSCC and verified that the top five significantly mutated genes (SMGs) in the TCGA HNSCC cohort were TP53, TTN, FAT1, CDKN2A, and MUC16. A high level of CD8+ T cells and high immune and stroma scores corresponded to a better survival probability in HNSCC.

A Novel Prognostic Pyroptosis-Related Gene Signature Correlates to Oxidative Stress and Immune-Related Features in Gliomas.

Gliomas are highly invasive and aggressive tumors having the highest incidence rate of brain cancer. Identifying effective prognostic and potential therapeutic targets is necessitated. The relationship of pyroptosis, a form of programmed cellular death, with gliomas remains elusive. We constructed and validated a prognostic model for gliomas using pyroptosis-related genes. Differentially expressed pyroptosis-related genes were screened using the "limma" package. Based on LASSO-Cox regression, nine significant genes including CASP1, CASP3, CASP6, IL32, MKI67, MYD88, PRTN3, NOS1, and VIM were employed to construct a prognostic model in the TCGA cohort; the results were validated in the CGGA cohort. According to the median risk score, the patients were classified into two risk groups, namely, high- and low-risk groups. Patients at high risk had worse prognoses relative to those at low risk evidenced by the Kaplan-Meier curve analysis. The two groups exhibited differences in immune cell infiltration and TMB scores, with high immune checkpoint levels, TMB scores, and immune cell infiltration levels in the high-risk group. KEGG and GO analyses suggested enrichment in immune-related pathways. Furthermore, we found that the genes in our signature strongly correlated with oxidative stress-related pathways and the subgroups exhibited different ssGSEA scores. Some small molecules targeted the genes in the model, and we verified their drug sensitivities between the risk groups. The scRNA-seq dataset, GSE138794, was processed using the "Seurat" package to assess the level of risk gene expression in specific cell types. Finally, the MYD88 level was lowered in the U87 glioma cell line using si-RNA constructs. Cellular proliferation was impaired, and fewer pyroptosis-related cytokines were released upon exposure to LPS. In summary, we built a pyroptosis-related gene model that accurately classified glioma patients into high- and low-risk groups. The findings suggest that the signature may be an effective prognostic predictive tool for gliomas.

Improvement of ACK1-targeted therapy efficacy in lung adenocarcinoma using chloroquine or bafilomycin A1

BackgroundActivated Cdc42-associated kinase 1 (ACK1) is a promising druggable target for cancer, but its inhibitors only showed moderate effects in clinical trials. The study aimed to investigate the underlying mechanisms and improve the antitumor efficacy of ACK1 inhibitors.MethodsRNA-seq was performed to determine the downstream pathways of ACK. Using Lasso Cox regression analysis, we built a risk signature with ACK1-related autophagy genes in the lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA) project. The performance of the signature in predicting the tumor immune environment and response to immunotherapy and chemotherapy were assessed in LUAD. CCK8, mRFP-GFP-LC3 assay, western blot, colony formation, wound healing, and transwell migration assays were conducted to evaluate the effects of the ACK1 inhibitor on lung cancer cells. A subcutaneous NSCLC xenograft model was used for in vivo study.ResultsRNA-seq revealed the regulatory role of ACK1 in autophagy. Furthermore, the risk signature separated LUAD patients into low- and high-risk groups with significantly different prognoses. The two groups displayed different tumor immune environments regarding 28 immune cell subsets. The low-risk groups showed high immune scores, high CTLA4 expression levels, high immunophenoscore, and low DNA mismatch repair capacity, suggesting a better response to immunotherapy. This signature also predicted sensitivity to commonly used chemotherapy and targeted drugs. In vitro, the ACK1 inhibitors (AIM-100 and Dasatinib) appeared to trigger adaptive autophagy-like response to protect lung cancer cells from apoptosis and activated the AMPK/mTOR signaling pathway, partially explaining its moderate antitumor efficacy. However, blocking lysosomal degradation with chloroquine/Bafilamycine A1 or inhibiting AMPK signaling with compound C/shPRKAA1 enhanced the ACK1 inhibitor’s cytotoxic effects on lung cancer cells. The efficacy of the combined therapy was also verified using a mouse xenograft model.ConclusionsThe resulting signature from ACK1-related autophagy genes robustly predicted survival and drug sensitivity in LUAD. The lysosomal degradation inhibition improved the therapeutic effects of the ACK1 inhibitor, suggesting a potential role for autophagy in therapy evasion.

Identifying Immune-Specific Subtypes of Adrenocortical Carcinoma Based on Immunogenomic Profiling

The tumor immune microenvironment (TIME) of adrenocortical carcinoma (ACC) is heterogeneous. However, a classification of ACC based on the TIME remains unexplored. We hierarchically clustered ACC based on the enrichment levels of twenty-three immune signatures to identify its immune-specific subtypes. Furthermore, we comprehensively compared the clinical and molecular profiles between the subtypes. We identified two immune-specific subtypes of ACC: Immunity-H and Immunity-L, which had high and low immune signature scores, respectively. We demonstrated that this subtyping method was stable and reproducible by analyzing five different ACC cohorts. Compared with Immunity-H, Immunity-L had lower levels of immune cell infiltration, worse overall and disease-free survival prognosis, and higher tumor stemness, genomic instability, proliferation potential, and intratumor heterogeneity. Furthermore, the ACC driver gene CTNNB1 was more frequently mutated in Immunity-L than in Immunity-H. Several proteins, such as mTOR, ERCC1, Akt, ACC1, Cyclin_E1, β-catenin, FASN, and GAPDH, were more highly expressed in Immunity-L than in Immunity-H. In contrast, p53, Syk, Lck, PREX1, and MAPK were more highly expressed in Immunity-H. Pathway and gene ontology analysis showed that the immune, stromal, and apoptosis pathways were highly enriched in Immunity-H, while the cell cycle, steroid biosynthesis, and DNA damage repair pathways were highly enriched in Immunity-L. ACC can be classified into two stable immune-related subtypes, which have significantly different antitumor responses, molecular characteristics, and clinical outcomes. This subtyping may provide clinical implications for prognostic and immunotherapeutic stratification of ACC.

The Composite Immune Risk Score predicts overall survival after allogeneic hematopoietic stem cell transplantation: A retrospective analysis of 1838 cases.

There has been little consensus on how to quantitatively assess immune reconstitution after hematopoietic stem cell transplantation (HSCT) as part of the standard of care. We retrospectively analyzed 11 150 post-transplant immune profiles of 1945 patients who underwent HSCT between 2012 and 2020. 1838 (94.5%) of the cases were allogeneic HSCT. Using the training set of patients (n=729), we identified a composite immune signature (integrating neutrophil, total lymphocyte, natural killer, total T, CD4+ T, and B cell counts in the peripheral blood) during days 91-180 after allogeneic HSCT that was predictive of early mortality and moreover simplified it into a formula for a Composite Immune Risk Score. When we verified the Composite Immune Risk Score in the validation (n=284) and test (n=391) sets of patients, a high score value was found to be associated with hazard ratios (HR) of 3.64 (95% C.I. 1.55-8.51; p=.0014) and 2.44 (95% C.I., 1.22-4.87; p=.0087), respectively, for early mortality. In multivariate analysis, a high Composite Immune Risk Score during days 91-180 remained an independent risk factor for early mortality after allogeneic HSCT (HR, 1.80; 95% C.I., 1.28-2.55; p=.00085). In conclusion, the Composite Immune Risk Score is easy to compute and could identify the high-risk patients of allogeneic HSCT who require targeted effort for prevention and control of infection.

Immunogenic cell death-related long noncoding RNA influences immunotherapy against lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths, accounting for over a million deaths worldwide annually. Immunogenic cell death (ICD) elicits an adaptive immune response. However, the role of ICD-related long noncoding RNAs (lncRNAs) in LUAD is unknown. In this study, we investigated the characteristics of the tumor microenvironment in LUAD, the prognostic significance of ICD-related lncRNAs, and the half-maximal inhibitory concentration (IC50) of possible chemotherapeutic drugs. We sorted prognostic lncRNAs using univariate Cox regression and constructed a risk signature based on them. We then confirmed the model's accuracy and generated a nomogram. Additionally, we performed immune microenvironment analysis, somatic mutation calculation, Tumor Immune Dysfunction and Exclusion (TIDE) analysis, and anticancer pharmaceutical IC50 prediction. Least absolute shrinkage and selection operator Cox regression identified 27 prognostic lncRNAs related to ICD, and a unique risk signature using 10 ICD-related lncRNAs was constructed. The risk score was confirmed to be a reliable predictor of survival, with the highest c-index score. The signature had a remarkable predictive performance with clinical applicability and could accurately predict the overall survival in LUAD. Furthermore, the lncRNA signature was closely associated with immunocyte invasion. We also analyzed the correlation between the risk score, tumor-infiltrating immune cells, and prognosis and identified high immune and ESTIMATE scores in low-risk patients. Moreover, we observed elevated checkpoint gene expression and low TIDE scores in high-risk patients, indicating a good immunotherapy response. Finally, high-risk patients were shown to be susceptible to anticancer medications. Therefore, our unique risk signature comprising 10 ICD-related lncRNAs was demonstrated to indicate the characteristics of the tumor-immune microenvironment in LUAD, predict patients' overall survival, and guide individualized treatment.

M6A RNA methylation modification and tumor immune microenvironment in lung adenocarcinoma.

Lung adenocarcinoma is one of the deadliest tumors. Studies have shown that N6-methyladenosine RNA methylation regulators, as a dynamic chemical modification, affect the occurrence and development of lung adenocarcinoma. To investigate the relationship between mutations and expression levels of m6A regulators in lung adenocarcinoma, we investigated the mutations and expression levels of 38 m6A regulators. We found that mutations in m6A regulatory factors did not affect the changes in expression levels, and 19 differentially expressed genes were identified. All tumor samples were classified into two subtypes based on the expression levels of 19 differentially expressed m6A-regulated genes. Survival analysis showed significant differences in survival between the two subtypes. To explore the relationship between immune cell infiltration and survival in both subtypes, we calculated the infiltration of 23 immune cells in both subtypes, and we found that the subtype with high immune cell infiltration had better survival. We found that subtypes with low tumor purity and high stromal and immune scores had better survival. The m6A-related immune genes were identified by taking the intersection of differentially expressed genes and immune genes in the two isoforms and calculating the Pearson correlation coefficients between the intersecting immune genes and the differentially expressed m6A-regulated genes. Finally, a prognostic model associated with m6A and associated with immunity was developed using prognostic genes screened from m6A-associated immune genes. The predictive power of the model was evaluated and our model was able to achieve good prediction.

HPV status represents dominant trait driving delineation of survival-associated gene co-expression networks in head and neck cancer

Integration of high-dimensional tumor gene expression data with clinicopathological data can increase our understanding of disease diversity, enable retrospective patient stratification, and identify new potential biomarkers and therapeutic targets. Using a systems biology approach, we provide a holistic overview of gene co-expression networks in head and neck squamous cell carcinomas (HNSCC). Weighted gene co-expression network analysis of HNSCC RNA sequencing data from 519 patients from The Cancer Genome Atlas (TCGA) was used to determine correlates of 5-year survival, using regression tree-based optimal threshold calculations. Survival-associated gene sets were transformed to gene set scores that were assessed for correlation with clinicopathological data. We identified 8 gene co-expression modules for HNSCC tumors, each of which contained co-expressed genes associated significantly with 5-year survival. Survival-associated co-expression gene signatures correlated dominantly with tumor HPV and p16 status. Network analysis identified that survival was associated with signaling networks of infection, immunity, epithelial-mesenchymal transition (EMT), hypoxia, glycolysis, focal adhesion, extracellular matrix, MYC signaling, autophagy and transcriptional regulation. EMT-associated gene signatures were expressed dominantly in fibroblasts, and cancer-associated fibroblasts were inversely correlated with immune activity. Interestingly, a high Immune Suppression Score based on expression of 21 genes associated with immune inhibition and including immune checkpoints, cytokines and regulatory T cell factors, was also associated with increased survival probability, and was significantly higher in HPV+ HNSCC. Networks associated with HNSCC survival were further associated with survival in cervical cancer, melanoma and lung cancer. This study defines 5129 genes associated with HNSCC survival, organized into co-expressed networks, their correlation with clinicopathological data, and with gene expression data from other malignant diseases, and provides a source for the discovery of biomarkers and novel therapies for HNSCC.

Analysis of Tumor Microenvironment Heterogeneity among Breast Cancer Subtypes to Identify Subtype-Specific Signatures.

Breast cancer is one of the most frequent malignancies in women worldwide. According to 50-gene signature, Prediction Analysis of Microarray 50 (PAM50), breast cancer can be categorized into five molecular subtypes, and these subtypes are highly heterogeneous in different molecular characteristics. However, the landscape of their tumor microenvironment (TME) heterogeneity has not been fully researched. Using the multi-omics dataset of breast cancer from the METABRIC cohort (n = 1699), we conducted extensive analyses of TME-related features to investigate TME heterogeneity in each breast cancer subtype. We then developed a cell-based subtype set enrichment analysis to identify the subtype-specific TME cells, and further evaluate their prognostic effects. Our results illustrate that different breast cancer subtypes exhibit different TME patterns. Basal-like and HER2-enriched subtypes are associated with high immune scores, expression of most immune regulatory targets, and immune cell infiltration, suggesting that these subtypes could be defined as "immune hot" tumors and suitable for immune checkpoint blockade (ICB) therapy. In contrast, Luminal A and Luminal B subtypes are associated with low immune scores and immune cell infiltration, suggesting that these subtypes could be defined as "immune cold" tumors. Additionally, the Normal-like subtype has relatively high levels of both immune and stromal features, which indicates that the Normal-like subtype may be suitable for more diverse treatment strategies. Our study reveals the breast cancer tumor microenvironment heterogeneity across subtypes. The comprehensive analysis of breast cancer TME-related characteristics may help us to adopt a tailored treatment strategy for different subtypes of patients.

Neutrophil extracellular traps-associated modification patterns depict the tumor microenvironment, precision immunotherapy, and prognosis of clear cell renal cell carcinoma.

Neutrophil extracellular traps (NETs) are web-like structures formed by neutrophils, and their main function is antimicrobial defense. Moreover, NETs have numerous roles in the pathogenesis and progression of cancers. However, the potential roles of NET-related genes in renal cell carcinoma remain unclear. In this study, we comprehensively investigated the NETs patterns and their relationships with tumor environment (TME), clinicopathological features, prognosis, and prediction of therapeutic benefits in the clear cell renal cell carcinoma (ccRCC) cohort. We obtained the gene expression profiles, clinical characteristics, and somatic mutations of patients with ccRCC from The Cancer Genome Atlas database (TCGA), Gene Expression Omnibus (GEO), and ArrayExpress datasets, respectively. ConsensusCluster was performed to identify the NET clusters. The tumor environment scores were evaluated by the "ESTIMATE," "CIBERSORT," and ssGSEA methods. The differential analysis was performed by the "limma" R package. The NET-scores were constructed based on the differentially expressed genes (DEGs) among the three cluster patterns using the ssGSEA method. The roles of NET scores in the prediction of immunotherapy were investigated by Immunophenoscores (TCIA database) and validated in two independent cohorts (GSE135222 and IMvigor210). The prediction of targeted drug benefits was implemented using the "pRRophetic" and Gene Set Cancer Analysis (GSCA) datasets. Real-time quantitative reverse transcription polymerase chain reaction (RT-PCR) was performed to identify the reliability of the core genes' expression in kidney cancer cells. Three NET-related clusters were identified in the ccRCC cohort. The patients in Cluster A had more metabolism-associated pathways and better overall survival outcomes, whereas the patients in Cluster C had more immune-related pathways, a higher immune score, and a poorer prognosis than those in Cluster B. Based on the DEGs among different subtypes, patients with ccRCC were divided into two gene clusters. These gene clusters demonstrated significantly different immune statuses and clinical features. The NET scores were calculated based on the ten core genes by the Gene Set Variation Analysis (GSVA) package and then divided ccRCC patients into two risk groups. We observed that high NET scores were associated with favorable survival outcomes, which were validated in the E-MTAB-1980 dataset. Moreover, the NET scores were significantly associated with immune cell infiltration, targeted drug response, and immunotherapy benefits. Subsequently, we explored the expression profiles, methylation, mutation, and survival prediction of the 10 core genes in TCGA-KIRC. Though all of them were associated with survival information, only four out of the 10 core genes were differentially expressed genes in tumor samples compared to normal tissues. Finally, RT-PCR showed that MAP7, SLC16A12, and SLC27A2 decreased, while SLC3A1 increased, in cancer cells. NETs play significant roles in the tumor immune microenvironment of ccRCC. Identifying NET clusters and scores could enhance our understanding of the heterogeneity of ccRCC, thus providing novel insights for precise individual treatment.

Intratumoral immune heterogeneity of prostate cancer characterized by typing and hub genes.

Discordant abundances of different immune cell subtypes is regarded to be an essential feature of tumour tissue. Direct studies in Prostate cancer (PC) of intratumoral immune heterogeneity characterized by immune cell subtype, are still lacking. Using the single sample gene set enrichment analysis (ssGSEA) algorithm, the abundance of 28 immune cells infiltration (ICI) were determined for PC. A NMF was performed to determine tumour-sample clustering based on the abundance of ICI and PFS information. Hub genes of clusters were identified via weighted gene co-expression network analysis (WGCNA). The multivariate dimensionality reduction analysis of hub genes expression matrix was carried out via principal component analysis (PCA) to obtain immune score (IS). We analysed the correlation between clustering, IS and clinical phenotype. We divided the 495 patients into clusterA (n= 193) and clusterB (n= 302) on the basis of ICI and PFS via NMF. The progression-free survival (PFS) were better for clusterA than for clusterB (p< 0.001). Each immune cell subtypes was more abundant in clusterA than in clusterB (p< 0.001). The expression levels of CTAL-4 and PD-L1 were lower in clusterB than in clusterA (p< 0.001 and p= 0.006). We obtained 103 hub genes via WGCNA. In the training and validation cohorts, the prognosis of high IS group was worse than that of the low IS group (p< 0.05). IS had good predictive effect on 5-year PFS. The expression of immune checkpoint genes was higher in the low IS group than in the high IS group (p< 0.01). Patients with low IS and receiving hormone therapy had better prognosis than other groups. The combination of IS and clinical characteristics including lymph node metastasis and gleason score can better differentiate patient outcomes than using it alone. IS was a practical algorithm to predict the prognosis of patients. Advanced PC patients with low IS may be more sensitive to hormone therapy. CXCL10, CXCL5, MMP1, CXCL12, CXCL11, CXCL2, STAT1, IL-6 and TLR2 were hub genes, which may drive the homing of immune cells in tumours and promote immune cell differentiation.

A Combined Risk Score Model to Assess Prognostic Value in Patients with Soft Tissue Sarcomas.

The prognostic model was established via the least absolute shrinkage and selection operator (LASSO) algorithm as well as multivariate Cox regression analysis. The stromal scores, immune scores, ESTIMA scores, and tumor purity of sarcoma patients were evaluated by the ESTIMATE algorithm. Functional analyses were performed to investigate the underlying mechanisms of immune cell infiltration and the prognosis of CRGs in sarcoma. Two molecular subgroups with different CRG expression patterns were recognized, which showed that patients with a higher immune score and more active immune status were prone to have better prognostic survival. Moreover, GO and KEGG analyses showed that these differentially expressed CRGs were mainly enriched in metabolic/ions-related signaling pathways, indicating that CRGs may have impacts on the immune cell infiltration and prognosis of sarcoma via regulating the bioprocess of mitochondria and consequently affecting the immune microenvironment. The expression levels of CRGs were closely correlated to the immunity condition and prognostic survival of sarcoma patients. The interaction between cuproptosis and immunity in sarcoma may provide a novel insight into the study of molecular mechanisms and candidate biomarkers for the prognosis, resulting in effective treatments for sarcoma patients.

An autophagy-related long non-coding RNA prognostic model and related immune research for female breast cancer.

Breast cancer (BRCA) is the most common malignancy among women worldwide. It was widely accepted that autophagy and the tumor immune microenvironment play an important role in the biological process of BRCA. Long non-coding RNAs (lncRNAs), as vital regulatory molecules, are involved in the occurrence and development of BRCA. The aim of this study was to assess the prognosis of BRCA by constructing an autophagy-related lncRNA (ARlncRNA) prognostic model and to provide individualized guidance for the treatment of BRCA. The clinical data and transcriptome data of patients with BRCA were acquired from the Cancer Genome Atlas database (TCGA), and autophagy-related genes were obtained from the human autophagy database (HADb). ARlncRNAs were identified by conducting co‑expression analysis. Univariate and multivariate Cox regression analysis were performed to construct an ARlncRNA prognostic model. The prognostic model was evaluated by Kaplan-Meier survival analysis, plotting risk curve, Independent prognostic analysis, clinical correlation analysis and plotting ROC curves. Finally, the tumor immune microenvironment of the prognostic model was studied. 10 ARlncRNAs(AC090912.1, LINC01871, AL358472.3, AL122010.1, SEMA3B-AS1, BAIAP2-DT, MAPT-AS1, DNAH10OS, AC015819.1, AC090198.1) were included in the model. Kaplan-Meier survival analysis of the prognostic model showed that the overall survival(OS) of the low-risk group was significantly better than that of the high-risk group (p< 0.001). Multivariate Cox regression analyses suggested that the prognostic model was an independent prognostic factor for BRCA (HR = 1.788, CI = 1.534-2.084, p < 0.001). ROCs of 1-, 3- and 5-year survival revealed that the AUC values of the prognostic model were all > 0.7, with values of 0.779, 0.746, and 0.731, respectively. In addition, Gene Set Enrichment Analysis (GSEA) suggested that several tumor-related pathways were enriched in the high-risk group, while several immune‑related pathways were enriched in the low-risk group. Patients in the low-risk group had higher immune scores and their immune cells and immune pathways were more active. Patients in the low-risk group had higher PD-1 and CTLA-4 levels and received more benefits from immune checkpoint inhibitors (ICIs) therapy. The ARlncRNA prognostic model showed good performance in predicting the prognosis of patients with BRCA and is of great significance to guide the individualized treatment of these patients.

A prognostic nomogram for papillary thyroid cancer lymph node metastasis based on immune score.

Papillary thyroid cancer (PTC) is the most common subtype of thyroid cancer and is characterized by an overall good prognosis and early-stage lymph node metastasis. The immune microenvironment is believed to play a crucial role in PTC initiation, progression and metastasis. However, to our knowledge, prognostic tools for thyroid cancer metastasis based on immune scores have not been adequately explored. This study aimed to construct a clinical nomogram to predict lymph node metastasis in patients with PTC. The genomic data and clinical-pathological characteristics of 447 PTC subjects were obtained from TCGA (The Cancer Genome Atlas data). Logistic regression models were performed for univariate and multivariate analyses to identify significant prediction factors. A prognostic nomogram was built based on the multivariate analysis results. The concordance index (C-index) and calibration curve were used to assess the predictive accuracy and discriminative ability of the model. The patients were divided into two subgroups based on immune scores. We found that patients with high immune scores had significantly higher lymph node metastasis risks (OR and 95% confidence interval [CI]: 1.774[1.130-2.784]) than those with low immune scores. The C-index for lymph node metastasis was 0.722 (95% CI, 0.671-0.774), which had a favorable performance for clinical prediction. The calibration curve for lymph node metastasis showed significant agreement between the nomogram prediction and actual observation. High immune scores are significantly correlated with higher lymph node metastasis risk in patients with PTC. Immune score-based prognostic nomograms may help to predict lymph node metastasis and have potential clinical application possibilities.

Characteristics of Adenosine-to-Inosine RNA editing-based subtypes and novel risk score for the prognosis and drug sensitivity in stomach adenocarcinoma.

Stomach adenocarcinoma (STAD) is always characterized by high mortality and poor prognosis with drug resistance and recrudescence due to individual genetic heterogeneity. Adenosine-to-Inosine RNA editing (ATIRE) has been reported associated with multiple tumors but the potential connection between ATIRE-related signatures and STAD remains unclear. In this study, we comprehensively elevated the genetic characteristics of ATIRE in STAD patients and first screened five vital survival-related ATIRE sites to identify a novel ATIRE-Risk score. Based on the risk scores, we further divided the patients into two different subtypes with diverse clinical characteristics and immune landscapes including immune cell infiltration (ICI), tumor microenvironment (TME), and immune checkpoint expression analysis. The low-risk subgroups, associated with better survival prognosis, were characterized by activated immune-cells, higher immune scores in TME, and down-expression of immunotherapy checkpoints. Moreover, different expressional genes (DEGs) between the above subtypes were further identified and the activation of immune-related pathways were found in low-risk patients. The stratified survival analysis further indicated patients with low-risk and high-tumor mutation burden (TMB) exhibited the best prognosis outcomes, implying the role of TMB and ATIRE-Risk scores was synergistic for the prognosis of STAD. Interestingly, anti-tumor chemotherapeutic drugs all exhibited lower IC50 values in low-risk subgroups, suggesting these patients might obtain a better curative response from the combined chemotherapy of STAD. Finally, combined with classical clinical features and ATIRE-Risk scores, we successfully established a promising nomogram system to accurately predict the 1/3/5-years survival ratio of STAD and this model was also estimated with high diagnostic efficiency and stable C-index with calibration curves. These significant ATIRE sites are promising to be further explored and might serve as a novel therapeutic target for STAD treatment.

Identification of the prognostic and immunological roles of aquaporin 4: A potential target for survival and immunotherapy in glioma patients.

Recent studies have revealed the critical role of AQP4 in the occurrence and development of gliomas. However, the role of AQP4 in immune regulation has not yet been reported. Many recent reports have identified the lymphatic system's occurrence within the central nervous system (CNS) and the vital role of immune regulation in treating brain tumors. Therefore, the present study aimed to explore the role of AQP4 in the immune regulation of glioma. We used bioinformatics analysis to investigate the immunoregulatory function of AQP4, including its correlation with immunity, anti-tumor immune processes, immunotherapy, immune infiltration, tumor mutational burden (TMB), stemness, mutation, and pan-cancer. The results revealed that AQP4 was significantly associated with the expression of multiple immune checkpoints, immune cells, as well as multiple immune cell effector genes, and antigen presentation and processing abilities. Although no significant correlation was found between the AQP4 gene and IDH mutation and MGMT, AQP4 demonstrated substantial expression differences in different immunophenotypes and molecular types. Using the TTD database, we discovered that EGFR, ABAT, and PDGFRA are strongly associated with AQP4 expression in the glioblastoma (GBM) classification, and these factors could be the potential AQP4-related immunotherapy targets. Afterward, we screened the differential genes in the high and low AQP4 gene expression group, the high and low immune score group, and the high and low matrix score group and took the intersection as the candidate factor. Finally, univariate Cox analysis was used to find eight prognostic variables with significant differences across the candidate genes. After lasso dimensionality reduction, three genes built the model (RARRES1, SOCS3, and TTYH1). The scoring model generated by the three genes was eventually obtained after the multi-factor screening of the three genes. Finally, combined with clinical information and cox regression analysis, it was further confirmed that the model score could be used as an independent prognostic factor.