A Novel Prognostic Predictor of Immune Microenvironment and Therapeutic Response in Kidney Renal Clear Cell Carcinoma based on Necroptosis-related Gene Signature

Necroptosis-Related Gene Signature Predicts Prognosis in Patients with Advanced Ovarian Cancer

Background/Objectives: This study aimed to construct a risk score (RS) based on necroptosis-associated genes to predict the prognosis of patients with advanced epithelial ovarian cancer (EOC). Methods: EOC data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) series 140082 (GSE140082) were used. Based on known necroptosis-associated genes, clustering was performed to identify molecular subtypes of EOC. A least absolute shrinkage and selection operator (LASSO)-Cox regression analysis identified key genes related to prognosis. The expression of one of them, RIPK3, was analyzed via immunohistochemistry in an EOC cohort. Results: An RS made from ten genes (IDH2, RIPK3, FASLG, BRAF, ITPK1, TNFSF10, ID1, PLK1, MLKL and HSPA4) was developed. Tumor samples were divided into a high-risk group (HRG) and low-risk group (LRG) using the RS. The model is able to predict the overall survival (OS) of EOC and distinguish the prognosis of different clinical subgroups. Immunohistochemical verification of the receptor-interacting serine/threonine-protein kinase (RIPK) 3 confirmed that high nuclear expression is correlated with a longer OS. In addition, the score can predict the response to a programmed death ligand 1 (PD-L1) blockade treatment in selected solid malignancies. Patients from the LRG seem to benefit more from it than patients from the HRG. Conclusions: Our RS based on necroptosis-associated genes might help to predict the prognosis of patients with advanced EOC and gives an idea on how the use of immunotherapy can potentially be guided.

Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study

BACKGROUND

Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)-based model using PCD-related genes.

METHODS

We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model.

RESULTS

The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients.

CONCLUSIONS

The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Identification and validation of a prognostic model based on immune-related genes in ovarian carcinoma

Background

A novel valuable prognostic model has been developed on the basis of immune-related genes (IRGs), which could be used to estimate overall survival (OS) in ovarian cancer (OC) patients in The Cancer Genome Atlas (TCGA) dataset and the International Cancer Genome Consortium (ICGC) dataset.

Methods

This prognostic model was engineered by employing LASSO regression in training cohort (TCGA dataset). The corresponding growth predictive values of this model for individualized survival was evaluated using survival analysis, receiver operating characteristic curve (ROC curve), and risk curve analysis. Combined with clinical characteristics, a model risk score nomogram for OS was well built. Thereafter, depended on the model risk score, patients were divided into high and low risk subgroups. The survival difference between these subgroups was measured using Kaplan-Meier survival method. In addition, correlations containing pathway enrichment, treatment, immune cell infiltration and the prognostic model were also analyzed. We established the ovarian cancer cell line W038 for this study and identified the performances of GBP1P1 knockdown on a series of activities including cellular proliferation, apoptosis, migration, and invasion of W038 cells in vitro.

Results

We constructed a 25-genes prognostic model (TNFAIP8L3, PI3, TMEM181, GBP1P1 (LOC400759), STX18, KIF26B, MRPS11, CACNA1C, PACSIN3, GMPR, MANF, PYGB, SNRPA1, ST7L, ZBP1, BMPR1B-DT, STAC2, LINC02585, LYPD6, NSG1, ACOT13, FAM120B, LEFTY1, SULT1A2, FZD3). The areas under the curves (AUC) of 1, 2 and 3 years were 0.806, 0.773 and 0.762, in the TCGA cohort, respectively. Besides, the effectiveness of the model was verified using ICGC testing data. Univariate and multivariate Cox regression analysis exposes the risk score as an independent prognosis predictor for OS both in the TCGA and ICGC cohort. In summary, we utilized comprehensive bioinformatics analysis to build an effective prognostic gene model for OC patients. These bioinformatic results suggested that GBP1P1 could act as a novel biomarker for OC. GBP1P1 knockdown substantially inhibited the proliferation, migration, and invasion of W038 cells in vitro, and increased the percentage of apoptotic W038 cells.

Conclusions

The analyses of genetic status of patients with 25-genes model might improve the ability to predict the prognosis of patients with OC and help to select patients suit able to therapies. Immune-related gene GBP1P1 might serve as prognostic biomarker for OC.

Identification of a seven-gene prognostic model for renal cell carcinoma associated with CD8+T lymphocyte cell

CD8+ T lymphocytes are important elements of the tumor microenvironment, hence their involvement in the development and progression of tumors is complex. Data on the precise tumor-infiltrating lymphocytes gene signature in renal cell carcinoma (RCC) remain limited. Therefore, this study created a tumor-infiltrating lymphocytes-related predictive model for patients with RCC using data from The Cancer Genome Atlas. The most important genes associated with CD8 + T lymphocytes were identified using weighted gene co-expression network analysis. Functional categories of important genes were revealed using gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes signaling pathway analyses. A CD8 + T lymphocyte-related prognostic model with 7 important genes was simultaneously created using the least absolute shrinkage and selection operator, univariate and multivariate Cox regressions, and the 7 genes were expressed particularly in CD8 + T lymphocytes according to single-cell sequencing data obtained from the Gene Expression Omnibus. This study identified a seven-gene prognostic model associated with CD8 + T lymphocytes that may significantly influence risk stratification in patients with RCC. The genes included in the model are apolipoprotein B mRNA editing catalytic polypeptide 3G, CD3 gamma, eomesodermin, protein tyrosine phosphatase, non-receptor type 7, signal regulatory protein gamma, Fas ligand, and T-cell immunoreceptor with Ig and ITIM domains.

Integrated analysis of histone modification features in clear cell renal cancer for risk stratification and therapeutic prediction

Clear cell renal cell carcinoma (ccRCC) is a common urological malignancy that is involved in tumor genesis and development. However, few studies have focused on the predictive role of the global histone modification status in ccRCC. A total of 621 patients with complete transcript information and corresponding clinical profiles were obtained from TCGA-KIRC, GSE22541, and EMTAB3267 cohorts. A total of 122 histone modification relevant pathways were derived from MSigDB, and their activation status was quantified using GSVA. Differentially expressed genes (DEGs) were identified and filtrated using univariate Cox regression analysis. The signature was built relied on the least absolute shrinkage and selection operator (LASSO) regression analysis, and evaluated from survival difference, chemotherapy response, and activated pathways. A novel nomogram was established to quantify the probability of death in different patients. Seven risky and fifty-eight protective genes were used in LASSO analysis, and six genes were used to build the histone modification gene (HiMG) signature, which showed significant independent prognostic potential in all three cohorts. The nomogram showed acceptable incremental predictions. CKS2 (p = 0.004) and PD1 (p = 0.002) expression were significantly higher in grade 3 ccRCC than in grades 1-2. CKS2 siRNA in renal cancer cells caused reductions in cellular proliferation, migration, and invasion. Patients with low HiMG may be potential responders to rapamycin, erlotinib and FH535, while AZD6482 and CHIR-99,021 may be more suitable for patients with high HiMG levels. ccRCC histone modification distribution and a clinical signature for prognosis prediction, clinical decision making, and molecular mechanism exploration, were established for risk stratification and personalized treatments.

Utilizing a novel model of PANoptosis-related genes for enhanced prognosis and immune status prediction in kidney renal clear cell carcinoma

Kidney renal clear cell carcinoma (KIRC) is the most common histopathologic type of renal cell carcinoma. PANoptosis, a cell death pathway that involves an interplay between pyroptosis, apoptosis and necroptosis, is associated with cancer immunity and development. However, the prognostic significance of PANoptosis in KIRC remains unclear. RNA-sequencing expression and mutational profiles from 532 KIRC samples and 72 normal samples with sufficient clinical data were retrieved from the Cancer Genome Atlas (TCGA) database. A prognostic model was constructed using differentially expressed genes (DEGs) related to PANoptosis in the TCGA cohort and was validated in a Gene Expression Omnibus (GEO) cohorts. Incorporating various clinical features, the risk model remained an independent prognostic factor in multivariate analysis, and it demonstrated superior performance compared to unsupervised clustering of the 21 PANoptosis-related genes alone. Further mutational analysis showed fewer VHL and more BAP1 alterations in the high-risk group, with alterations in both genes also associated with patient prognosis. The high-risk group was characterized by an unfavorable immune microenvironment, marked by reduced levels of CD4 + T cells and natural killer cells, but increased M2 macrophages and regulatory T cells. Finally, the risk model was predictive of response to immune checkpoint blockade, as well as sensitivity to sunitinib and paclitaxel. The PANoptosis-related risk model developed in this study enables accurate prognostic prediction in KIRC patients. Its associations with the tumor immune microenvironment and drug efficacy may offer potential therapeutic targets and inform clinical decisions.

Multi-cohort validation: A comprehensive exploration of prognostic marker in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common form of RCC. It is characterized by resistance to traditional radiotherapy and chemotherapy, as well as an unfavorable clinical prognosis. Although TYMP is implicated in the advancement of tumor progression, the role of TYMP in ccRCC is still not understood. Heightened TYMP expression was identified in ccRCC through database mining and confirmed in RCC cell lines. Indeed, TYMP knockdown impacted RCC cell proliferation, migration, and invasion in vitro. TYMP showed a positive correlation with clinicopathological parameters (histological grade, pathological stage). Moreover, patients with high TYMP expression were indicative of poor prognosis in TCGA-ccRCC and external cohorts. The results of single-cell analysis showed that the distribution of TYMP was predominantly observed in monocytes and macrophages. Furthermore, there is a significant association between TYMP and immune status. Methylation analysis further elucidated the relationship between TYMP expression and multiple methylation sites. Drug sensitivity analysis unveiled potential pharmaceutical options. Additionally, mutation analyses identified an association between TYMP and the ccRCC driver genes like BAP1 and ROS1. In summary, TYMP may serve as a reliable prognostic indicator for ccRCC.

Identification of necroptosis-associated miRNA signature for predicting prognosis and immune landscape in stomach adenocarcinoma

PURPOSE

This study aims to identify the potential necroptosis related genes (NRGs)-associated miRNAs signature and explore the impact on the prognosis of stomach adenocarcinoma (STAD).

METHODS

Employing rigorous methodologies, we utilized univariate Cox, Lasso and multivariate Cox regression analyses to develop a prognostic signature. Kaplan-Meier (K-M) and ROC curves were applied to assess the prognostic value of signature in a training group and an independent test group. Furthermore, we conducted Gene Set Enrichment Analysis (GSEA) for enrichment of tumor-related pathways. The risk score was calculated for each patient based on the expression of miRNAs which were enrolled in the signature. Patients were stratified into high- and low-risk groups. The immune cell infiltration and immunotherapy were compared between the two groups. Finally, the diagnostic potential of the miRNA was explored by RT-qPCR.

RESULTS

We constructed a prognostic model based on 6 NRGs-associated miRNAs. K-M plots underscored superior survival outcomes in the low-risk group. GSEA results revealed the enrichment of several tumor-related pathways in the high-risk group. Notably, CD8+ T cells, Tregs and activated memory CD4+ T cells exhibited negative correlations with the risk score. Additionally, a few immune checkpoint genes, such as CTLA4, PD1 and PD-L1, were significantly upregulated in the low-risk group. Furthermore, the serum expression levels of all these 6 miRNAs were significantly elevated in STAD patients.

CONCLUSIONS

Our study identified a robust risk score derived from a signature of 6 NRGs-associated miRNAs, demonstrating high efficacy for prognosis of STAD. These results not only contributed to our understanding of STAD pathogenesis, but also held promise for potential clinical applications, particularly in the realm of personalized immunotherapy for STAD patients.

A model based on immunogenic cell death-related genes predicts prognosis and response to immunotherapy in kidney renal clear cell carcinoma

Background

The prognosis of patients with kidney renal clear cell carcinoma (KIRC), a life-threatening condition, is poor. Immunogenic cell death (ICD) induces regulated cell death via immunogenic signal secretion and exposure. ICD induces regulated cell death through immunogenic signal secretion and exposure. ICD plays an essential role in tumorigenesis, however, the role of ICD in KIRC remains unclear.

Methods

This study examined the expression levels of 34 ICD-related genes in The Cancer Genome Atlas (TCGA) data set. Signature genes linked to KIRC survival were identified using Cox regression. Next, a prognostic risk model (RM) was built. Subsequently, the KIRC patients were divided into low- and high-risk groups. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were plotted. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were carried out to investigate the possible role of differential gene expression between the two groups. The immune microenvironment (IME) was assessed using Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression, CIBERSORT, and single-sample gene-set enrichment analysis algorithms. An enrichment analysis was used to determine the biological significance of these regulatory networks we conducted. The relationship between immune checkpoint gene expression and risk score, and the relationship between treatment outcome and gene expression were assessed using correlation analyses.

Results

We developed a KIRC RM based on five ICD-related genes (i.e., FOXP3, IFNB1, IL6, LY96, and TLR4), which were identified as the prognostic signature genes. Using the TCGA data set, we conducted a survival analysis and found that the 3-year RM had an area under the curve (AUC) of 0.735, which validated the reliability of the signature. Similarly, using the International Cancer Genome Consortium (ICGC) data set, we found that the 3-year RM had an AUC of 0.732.

Conclusions

A RM based on five ICD-related genes was built to predict the prognosis of KIRC patients. This RM predicted patient prognosis and reflected the tumor IME of KIRC patients. Thus, this RM could be used to promote individualized treatments and provide potential novel targets for immunotherapy.

Cuproptosis gene-related, neural network-based prognosis prediction and drug-target prediction for KIRC

BACKGROUND

Kidney renal clear cell carcinoma (KIRC), as a common case in renal cell carcinoma (RCC), has the risk of postoperative recurrence, thus its prognosis is poor and its prognostic markers are usually based on imaging methods, which have the problem of low specificity. In addition, cuproptosis, as a novel mode of cell death, has been used as a biomarker to predict disease in many cancers in recent years, which also provides an important basis for prognostic prediction in KIRC. For postoperative patients with KIRC, an important means of preventing disease recurrence is pharmacological treatment, and thus matching the appropriate drug to the specific patient's target is also particularly important. With the development of neural networks, their predictive performance in the field of medical big data has surpassed that of traditional methods, and this also applies to the field of prognosis prediction and drug-target prediction.

OBJECTIVE

The purpose of this study is to screen for cuproptosis genes related to the prognosis of KIRC and to establish a deep neural network (DNN) model for patient risk prediction, while also developing a personalized nomogram model for predicting patient survival. In addition, sensitivity drugs for KIRC were screened, and a graph neural network (GNN) model was established to predict the targets of the drugs, in order to discover potential drug action sites and provide new treatment ideas for KIRC.

METHODS

We used the Cancer Genome Atlas (TCGA) database, International Cancer Genome Consortium (ICGC) database, and DrugBank database for our study. Differentially expressed genes (DEGs) were screened using TCGA data, and then a DNN-based risk prediction model was built and validated using ICGC data. Subsequently, the differences between high- and low-risk groups were analyzed and KIRC-sensitive drugs were screened, and finally a GNN model was trained using DrugBank data to predict the relevant targets of these drugs.

RESULTS

A prognostic model was built by screening 10 significantly different cuproptosis-related genes, the model had an AUC of 0.739 on the training set (TCGA data) and an AUC of 0.707 on the validation set (ICGC data), which demonstrated a good predictive performance. Based on the prognostic model in this paper, patients were also classified into high- and low-risk groups, and functional analyses were performed. In addition, 251 drugs were screened for sensitivity, and four drugs were ultimately found to have high sensitivity, with 5-Fluorouracil having the best inhibitory effect, and subsequently their corresponding targets were also predicted by GraphSAGE, with the most prominent targets including Cytochrome P450 2D6, UDP-glucuronosyltransferase 1A, and Proto-oncogene tyrosine-protein kinase receptor Ret. Notably, the average accuracy of GraphSAGE was 0.817 ± 0.013, which was higher than that of GAT and GTN.

CONCLUSION

Our KIRC risk prediction model, constructed using 10 cuproptosis-related genes, had good independent prognostic ability. In addition, we screened four highly sensitive drugs and predicted relevant targets for these four drugs that might treat KIRC. Finally, literature research revealed that four drug-target interactions have been demonstrated in previous studies and the remaining targets are potential sites of drug action for future research.

Identification and validation of prognostic and tumor microenvironment characteristics of necroptosis index and BIRC3 in clear cell renal cell carcinoma

Background

Necroptosis is a form of programmed cell death; it has an important role in tumorigenesis and metastasis. However, details of the regulation and function of necroptosis in clear cell renal cell carcinoma (ccRCC) remain unclear. It is necessary to explore the significance of necroptosis in ccRCC.

Methods

Necroptosis-related clusters were discerned through the application of Consensus Clustering. Based on the TCGA and GEO databases, we identified prognostic necroptosis-related genes (NRGs) with univariate COX regression analysis. The necroptosis-related model was constructed through the utilization of LASSO regression analysis, and the immune properties, tumor mutation burden, and immunotherapy characteristics of the model were assessed using multiple algorithms and datasets. Furthermore, we conducted comprehensive GO, KEGG, and GSVA analyses to probe into the functional aspects of biological pathways. To explore the expression and of hub gene (BIRC3) in different ccRCC cell types and cell lines, single-cell sequencing data was analysed and we performed Quantitative Real-time PCR to detect the expression of BIRC3 in ccRCC cell lines. Function of BIRC3 in ccRCC was assessed through Cell Counting Kit-8 (CCK8) assay (for proliferation), transwell and wound healing assays (for migration and invasion).

Results

Distinct necroptosis-related clusters exhibiting varying prognostic implications, and enrichment pathways were identified in ccRCC. A robust necroptosis-related model formulated based on the expression of six prognostic NRGs, presented substantial predictive capabilities of overall survival and was shown to be related with patients' immune profiles, tumor mutation burden, and response to immunotherapy. Notably, the hub gene BIRC3 was markedly upregulated in both ccRCC tissues and cell lines, and showed significant correlations with immunosuppressive cells, immune checkpoints, and oncogenic pathways. Downregulation of BIRC3 demonstrated a negative regulatory effect on ccRCC cell proliferation migration and invasion.

Conclusion

The necroptosis-related model assumed a pivotal role in determining the prognosis, tumor mutation burden, immunotherapy response, and immune cell infiltration characteristics among ccRCC patients. BIRC3 exhibited significant correlations with the immunosuppressive microenvironment, which highlighted its potential for informing the design of innovative immunotherapies for ccRCC patients.

Genomic Fabrics of the Excretory System's Functional Pathways Remodeled in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most frequent form of kidney cancer. Metastatic stages of ccRCC reduce the five-year survival rate to 15%. In this report, we analyze the ccRCC-induced remodeling of the five KEGG-constructed excretory functional pathways in a surgically removed right kidney and its metastasis in the chest wall from the perspective of the Genomic Fabric Paradigm (GFP). The GFP characterizes every single gene in each region by these independent variables: the average expression level (AVE), relative expression variability (REV), and expression correlation (COR) with each other gene. While the traditional approach is limited to only AVE analysis, the novel REV analysis identifies the genes whose correct expression level is critical for cell survival and proliferation. The COR analysis determines the real gene networks responsible for functional pathways. The analyses covered the pathways for aldosterone-regulated sodium reabsorption, collecting duct acid secretion, endocrine and other factor-regulated sodium reabsorption, proximal tubule bicarbonate reclamation, and vasopressin-regulated water reabsorption. The present study confirms the conclusion of our previously published articles on prostate and kidney cancers that even equally graded cancer nodules from the same tumor have different transcriptomic topologies. Therefore, the personalization of anti-cancer therapy should go beyond the individual, to his/her major cancer nodules.

Necroptosis-related genes are associated with prognostic features of kidney renal clear cell carcinoma

INTRODUCTION

Renal clear cell carcinoma is a common type of cancer in the adult urological system. It has a high mortality rate, with 30% of patients developing metastasis and 60% dying within 1-2 years of diagnosis. Recent advancements in tumor immunology and necroptosis have provided new insights into kidney cancer therapy. Therefore, it is crucial to identify potential targets for combining immunotherapy with necroptosis.

MATERIALS AND METHODS

Using the GSE168845 dataset and necroptosis-related genes, we identified genes that are differentially expressed in relation to necroptosis. We analyzed the prognostic value of these genes through differential expression analysis, prognostic analysis, and Cox regression analysis. The expression levels of the MYCN and CDKN2A genes were verified using the GSE53757 dataset. We also examined the association between the differentially expressed genes and clinicopathological features, as well as overall survival in our cohorts. In addition, we constructed a lasso Cox regression model to assess the correlation between these genes and immune score, ICP, and OCLR score. We conducted qRT-PCR to detect the expression of MYCN, CDKN2A, and ZBP1 in different samples of kidney renal clear cell carcinoma (KIRC). The expression levels of these genes were verified in a normal kidney cell line (HK-2 cells) and two KIRC cell lines (786-O, ACHN). The protein levels of MYCN and CDKN2A were detected using immunohistochemistry (IHC). SiRNA was used to silence the expression of MYCN and CDKN2A in the ACHN cell line, and wound healing assays were performed to measure cell migration.

RESULTS

MYCN, CDKN2A, and ZBP1 were identified as necroptosis-related genes with independent prognostic value, leading to the development of a risk prognostic model. The expression of the CDKN2A gene was significantly higher in KIRC tissues compared to normal tissues, while the expression of the MYCN gene was significantly lower in KIRC tissues. The expression of MYCN and CDKN2A was associated with tumor stage, metastasis, and overall survival in our cohort. Furthermore, MYCN, CDKN2A, and ZBP1 were significantly correlated with immune score, ICP, and OCLR score. The expression levels of CDKN2A and ZBP1 were higher in KIRC cells compared to normal kidney cells, while the expression of MYCN was lower in KIRC cells. The protein expression of MYCN and CDKN2A was also higher in KIRC tissues, as confirmed by IHC. The results of the wound healing assay indicated that silencing CDKN2A inhibited cell migration, while silencing MYCN enhanced cell migration.

CONCLUSIONS

MYCN and CDKN2A are potential targets and valuable prognostic biomarkers for combining immunotherapy with necroptosis in kidney renal clear cell carcinoma. CDKN2A promotes the migration of renal cancer cells, while MYCN inhibits their migration.

Construction of PANoptosis signature: Novel target discovery for prostate cancer immunotherapy

PANoptosis pathway gene sets encompassing pyroptosis, apoptosis, and necroptosis were identified from the MSigDB database. We analyzed the perturbations and crosstalk in the PANoptosis pathway in prostate adenocarcinoma (PRAD), including gene mutation, transcription, methylation, and clinical features. By constructing a PANoptosis signature, we accurately predicted the prognosis and immunotherapeutic response of PRAD patients. We further explored the molecular features and immunological roles of the signature, dividing patients into high- and low-score groups. Notably, the high-score group correlated with better survival outcomes and immunotherapeutic responses, as well as a higher mutation frequency and enrichment score in the PANoptosis and HALLMARK pathways. The PANoptosis signature also enhanced overall antitumor immunity, promoted immune cell infiltration, upregulated immune checkpoint regulators, and revealed the cold tumor characteristics of PRAD. We also identified potential drug targets based on the PANoptosis signature. These findings lead the way in identifying novel prognostic markers and therapeutic targets for patients with PRAD.

An immunogenic cell death-related classification predicts prognosis and response to immunotherapy in kidney renal clear cell carcinoma

Introduction

Immunogenic cell death (ICD) is a form of regulated cell death that activates an adaptive immune response in an immunocompetent host and is particularly sensitive to antigens from tumor cells. Kidney clear cell carcinoma (KIRC) is an immunogenic tumor with extensive tumor heterogeneity. However, no reliable predictive biomarkers have been identified to reflect the immune microenvironment and therapeutic response of KIRC.

Methods

Therefore, we used the CIBERSORT and ESTIMATE algorithms to define three ICD clusters based on the expression of ICD-related genes in 661 KIRC patients. Subsequently, we identified three different ICD gene clusters based on the overlap of differentially expressed genes (DEGs) within the ICD clusters. In addition, principal component analysis (PCA) was performed to calculate the ICD scores.

Results

The results showed that patients with reduced ICD scores had a poorer prognosis and reduced transcript levels of immune checkpoint genes regulated with T cell differentiation. Furthermore, the ICD score was negatively correlated with the tumor mutation burden (TMB) value of KICD. patients with higher ICD scores showed clinical benefits and advantages of immunotherapy, indicating that the ICD score is an accurate and valid predictor to assess the effect of immunotherapy.

Discussion

Overall, our study presents a comprehensive KICD immune-related ICD landscape that can provide guidance for current immunotherapy and predict patient prognosis to help physicians make judgments about the patient's disease and treatment modalities, and can guide current research on immunotherapy strategies for KICD.

Genome-Wide Analysis of Kidney Renal Cell Carcinoma: Exploring Differentially Expressed Genes for Diagnostic and Therapeutic Targets

Kidney renal cell carcinoma (KIRC) is the most common type of renal cancer. Kidney malignancies have been ranked in the top 10 most frequently occurring cancers. KIRC is a prevalent malignancy with a poor prognosis. The disease has risen for the last 40 years, and robust biomarkers for KIRC are needed for precision/personalized medicine. In this bioinformatics study, we utilized genomic data of KIRC patients from The Cancer Genome Atlas for biomarker discovery. A total of 314 samples were used in this study. We identified many differentially expressed genes (DEGs) categorized as upregulated or downregulated. A protein-protein interaction network for the DEGs was then generated and analyzed using the Search Tool for the Retrieval of Interacting Genes plugin of Cytoscape. A set of 10 hub genes was selected based on the Maximum Clique Centrality score defined by the CytoHubba plugin. The elucidated set of genes, that is, CALCA, CRH, TH, CHAT, SLC18A3, FSHB, MYH6, CAV3, KCNA4, and GBX2, were then categorized as potential candidates to be explored as KIRC biomarkers. The survival analysis plots for each gene suggested that alterations in CHAT, CAV3, CRH, MYH6, SLC18A3, and FSHB resulted in decreased survival of KIRC patients. In all, the results suggest that genomic alterations in selected genes can be explored to inform biomarker discovery and for therapeutic predictions in KIRC.

Necroptosis-related signatures identify two distinct hepatocellular carcinoma subtypes: Implications for predicting drug sensitivity and prognosis

Background

Necroptosis is associated with oncogenesis, tumor immunity and progression. This research aims to investigate the association of necroptosis-related genes with drug sensitivity and prognosis in hepatocellular carcinoma (HCC).

Methods

Based on necroptosis-related signatures, HCC patients retrieved from the TCGA database were categorized. Survival outcomes, mutation profile, immune microenvironment, and drug sensitivity between HCC subtypes were further compared. Then, LASSO analysis was performed to construct a necroptosis-related prognostic signature, which was further evaluated using another independent cohort.

Results

A total of 371 patients with HCC could be categorized into two necroptosis-related subtypes. About 36% of patients were allocated to subtype A, with worse survival, more mutant TP53, and a lower likelihood of immunotherapy response. In contrast, patients in subtype B had a favorable prognosis, with lower expression of immunosuppressive signatures but a lower abundance of B and CD8⁺ T-cell infiltration. The prognostic risk score calculated using the expression levels of nine genes involved in the necroptosis pathway (MLKL, FADD, XIAP, USP22, UHRF1, CASP8, RIPK3, ZBP1, and FAS) showed a significant association with tumor stage, histologic grade, and Child‒Pugh score. Additionally, the risk score model was proven to be accurate in both the training and independent external validation cohorts and performed better than the TNM staging system and three well-recognized risk score models.

Conclusions

Based on necroptosis-related signatures, we identified two HCC subtypes with distinctive immune microenvironments, mutation profiles, drug sensitivities, and survival outcomes. A novel well-performing prognostic model was further constructed.

Identification and validation of novel signature associated with hepatocellular carcinoma prognosis using Single-cell and WGCNA analysis

Background: Hepatocellular carcinoma is a rapidly advancing malignancy with a poor prognosis. Therefore, further research is needed on its potential pathogenesis and therapeutic targets. Methods: In this study, the relevant datasets were downloaded from the TCGA database and the key modules were identified using WGCNA in the necroptosis-related gene set, while single-cell datasets were scored using the necroptosis gene set. Differential genes in the high- and low-expression groups were determined using the WGCNA module genes as intersection sets to identify key genes involved in necroptosis in liver cancer. Then, prognostic models were constructed using LASSO COX regression followed by multi-faceted validation. Finally, model genes were found to be correlated with key proteins of the necroptosis pathway and used to identify the most relevant genes, followed by their experimental validation. Subsequently, on the basis of the analysis results, the most relevant SFPQ was selected for cell-level verification. Results: We constructed a prognosis model that included five necroptosis-related genes (EHD1, RAC1, SFPQ, DAB2 and PABPC4) to predict the prognosis and survival of HCC patients. The results showed that the prognosis was more unfavorable in the high-risk group compared to the low-risk group, which was corroborated using ROC curves and risk factor plots. In addition, we further checked the differential genes using GO and KEGG analyses and found that they were predominantly enriched in the neuroactive ligand-receptor interaction pathway. The results of the GSVA analysis demonstrated that the high-risk group was mainly enriched in DNA replication, regulation of the mitotic cycle, and regulation of various cancer pathways, while the low-risk group was predominantly enriched in the metabolism of drugs and xenobiotics using cytochrome P450. SFPQ was found to be the main gene that affects the prognosis and SFPQ expression was positively correlated with the expression of RIPK1, RIPK3 and MLKL. Furthermore, the suppression of SFPQ could inhibit hyper-malignant phenotype HCC cells, while the WB results showed that inhibition of SFPQ expression also resulted in lower expression of necroptosis proteins, compared to the sh-NC group. Conclusions: Our prognostic model could accurately predict the prognosis of patients with HCC to further identify novel molecular candidates and interventions that can be used as alternative methods of treatment for HCC.

ZBP1 and heatstroke

Heatstroke, which is associated with circulatory failure and multiple organ dysfunction, is a heat stress-induced life-threatening condition characterized by a raised core body temperature and central nervous system dysfunction. As global warming continues to worsen, heatstroke is expected to become the leading cause of death globally. Despite the severity of this condition, the detailed mechanisms that underlie the pathogenesis of heatstroke still remain largely unknown. Z-DNA-binding protein 1 (ZBP1), also referred to as DNA-dependent activator of IFN-regulatory factors (DAI) and DLM-1, was initially identified as a tumor-associated and interferon (IFN)-inducible protein, but has recently been reported to be a Z-nucleic acid sensor that regulates cell death and inflammation; however, its biological function is not yet fully understood. In the present study, a brief review of the main regulators is presented, in which the Z-nucleic acid sensor ZBP1 was identified to be a significant factor in regulating the pathological characteristics of heatstroke through ZBP1-dependent signaling. Thus, the lethal mechanism of heatstroke is revealed, in addition to a second function of ZBP1 other than as a nucleic acid sensor.

Prognostic biomarker NEIL3 and its association with immune infiltration in renal clear cell carcinoma

Background

Kidney renal clear cell carcinoma (KIRC) is a malignant tumor with a high degree of immune infiltration. Identifying immune biomarkers is essential for the treatment of KIRC. Studies have identified the potential of NEIL3 to modulate the immune microenvironment and promote tumor progression. However, the role of NEIL3 in KIRC remains uncertain. This study was to investigate the effect of NEIL3 on the prognosis and immune infiltration of patients with KIRC.

Methods

TCGA and GEO databases were used to study the expression of NEIL3 in KIRC. Cox regression analysis was used to examine the relationship between the expression of NEIL3 and clinicopathological variables and survival. Furthermore, Gene Set Cancer Analysis (GSCA) was applied to study the impact of NEIL3 methylation on outcomes of KIRC. Through gene ontology (GO) and Gene set enrichment (GSEA) analysis, the biological processes and signal pathways related to NEIL3 expression were identified. In addition, immune infiltration analysis was conducted via CIBERSORT analysis, ssGSEA analysis and TISIDB database.

Results

NEIL3 was overexpressed in KIRC, and it was significantly related with histologic grade, pathologic stage, T stage, M stage, and vital status of KIRC patients (P < 0.001). The expression of NEIL3 was associated with worse outcomes. Univariate and multivariate Cox analysis showed that NEIL3 may be an indicator of adverse outcomes in KIRC. GSEA analysis revealed that NEIL3 may be involved in signal pathways including cell cycle, DNA replication, mismatch repair, P53 signal pathway, and antigen processing and presentation. In addition, immune infiltration analysis showed a positive correlation between NEIL3 expression and multiple immune cells (activated CD8 T cells, activated dendritic cells, myeloid-derived suppressor cells, follicular helper T cells, and regulatory T cells) and immunoinhibitors (PD1, CTLA4, LAG3, TIGHT, IL10, and CD96).

Conclusion

NEIL3 is a potential independent biomarker of KIRC, which is relevant to immune infiltration.

A Novel 7-Methylguanosine (m7G)-Related Gene Signature for Overall Survival Prediction in Patient with Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common pathology type of renal cancer that has an abysmal prognosis. Although a crucial role for 7-methylguanosine modification in cancer cell development has been reported, its role in ccRCC remains uncertain. This study was conducted to determine the efficacy of predictive biomarkers based on m7G-related genes in ccRCC. Firstly, we extracted clinical data and gene expression profiles of ccRCC patients from publicly accessible databases. It identified that 22 of the m7G-related 34 genes were related to overall survival, and 5 of the 22 genes were significantly expressed differently in tumor tissues. Based on Lasso regression analysis, five optimal genes (CYFIP2, EIF4A1, NUDT1, NUDT10, and NUDT4) were chosen to build a new predictive risk model in the TCGA cohort. Validation was carried out with the E-MTAB-1980 cohort. Then, a prognostic nomogram was erected, including the m7G-related gene risk score, age, histological grade, and stage status. Further studies and analysis showed that immune cell infiltration might be associated with the m7G-related risk genes. In addition, the relationship between gene expression and drug response was evaluated by the Pearson correlation test. Therefore, the risk signature with five selected m7G-related genes may be a promising prognostic biomarker and contribute to standardized prognostic assessment for ccRCC.

Identification and Validation of a Necroptosis-Related Prognostic Signature for Kidney Renal Clear Cell Carcinoma

Background

Necroptosis is progressively becoming an important focus of research because of its role in the pathogenesis of cancer and other inflammatory diseases. Our study is designed to anticipate the survival time of kidney renal clear cell carcinoma (KIRC) by constructing a prognostic signature of necroptosis-related genes.

Materials

Clinical information and RNA-seq data were acquired from Renal Cell Cancer-European Union (RECA-EU) and The Cancer Genome Atlas- (TCGA-) KIRC, respectively. ConsensusClusterPlus was used to identify molecular subtypes, and the distribution of immune cell infiltration, anticancer drug sensitivity, and somatic gene mutations was studied in these subtypes. Subsequently, LASSO-Cox regression and univariate Cox regression were also carried out to construct a necroptosis-related signature. Cox regression, survival analysis, clinicopathological characteristic correlation analysis, nomogram, cancer stem cell analysis, and receiver operating characteristic (ROC) curve were some tools employed to study the prognostic power of the signature.

Results

Based on the expression patterns of 66 survival-related necroptosis genes, we classified the KIRC into three subtypes (C1, C2, and C3) that are associated with necroptosis, which had significantly different tumor stem cell components. Among these, C2 patients had a longer survival time and enhanced immune status and were more sensitive to conventional chemotherapeutic drugs. Moreover, in order to predict the prognosis of KIRC patients, five genes (BMP8A, TLCD1, CLGN, GDF7, and RARB) were used to develop a necroptosis-related prognostic signature, which had an acceptable predictive potency. The results from Cox regression and stratified survival analysis revealed that the signature was an independent prognostic factor, whereas the nomogram and calibration curve demonstrated satisfactory survival time prediction based on the risk score.

Conclusions

Three molecular subtypes and five necroptosis-related genes were discovered in KIRC using data from TCGA-KIRC and RECA-EU. Thus, a new biomarker and a potentially effective therapeutic approach for KIRC patients were provided in the current study.

A novel necroptosis-related lncRNAs signature for survival prediction in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common kind of kidney cancer with poor prognosis. Necroptosis is a newly observed type of programmed cell death in recent years. However, the effects of necroptosis-related lncRNAs (NRlncRNAs) on ccRCC have not been widely explored. The transcription profile and clinical information were obtained from The Cancer Genome Atlas. Necroptosis-related lncRNAs were identified by utilizing a co-expression network of necroptosis-related genes and lncRNAs. Univariate Cox regression, least absolute shrinkage, and selection operator regression and multivariate Cox regression were performed to screen out ideal prognostic necroptosis-related lncRNAss and develop a multi-lncRNA signature. Finally, 6 necroptosis-related lncRNA markers were established. Patients were separated into high- and low-risk groups based on the performance value of the median risk score. Kaplan-Meier analysis identified that high-risk patients had poorer prognosis than low-risk patients. Furthermore, the area under time-dependent receiver operating characteristic curve reached 0.743 at 1 year, 0.719 at 3 years, and 0.742 at 5 years, which indicating that they can be used to predict ccRCC prognosis. In addition, the proposed signature was related to immunocyte infiltration. A nomogram model was also established to provide a more beneficial prognostic indicator for the clinic. Altogether, in the present study, the 6-lncRNA prognostic risk signature are trustworthy and effective indicators for predicting the prognosis of ccRCC.

Cuproptosis-related modification patterns depict the tumor microenvironment, precision immunotherapy, and prognosis of kidney renal clear cell carcinoma

Background

Due to the different infiltration abundance of immune cells in tumor, the efficacy of immunotherapy varies widely among individuals. Recently, growing evidence suggested that cuproptosis has impact on cancer immunity profoundly. However, the comprehensive roles of cuproptosis-related genes in tumor microenvironment (TME) and in response to immunotherapy are still unclear.

Methods

Based on 43 cuproptosis-related genes, we employed unsupervised clustering to identify cuproptosis-related patterns and single-sample gene set enrichment analysis algorithm to build a cuproptosis signature for individual patient's immune cell infiltration and efficacy of immune checkpoint blockade (ICB) evaluation. Then, the cuproptosis-related genes were narrowed down using univariate Cox regression model and least absolute shrinkage and selection operator algorithm. Finally, a cuproptosis risk score was built by random survival forest based on these narrowed-down genes.

Results

Two distinct cuproptosis-related patterns were developed, with cuproptosis cluster 1 showing better prognosis and higher enrichment of immune-related pathways and infiltration of immune cells. For individual evaluation, the cuproptosis signature that we built could be used not only for predicting immune cell infiltration in TME but also for evaluating an individual's sensitivity to ICBs. Patients with higher cuproptosis signature scores exhibited more activated cancer immune processes, higher immune cell infiltration, and better curative efficacy of ICBs. Furthermore, a robust cuproptosis risk score indicated that patients with higher risk scores showed worse survival outcomes, which could be validated in internal and external validation cohorts. Ultimately, a nomogram which combined the risk score with the prognostic clinical factors was developed, and it showed excellent prediction accuracy for survival outcomes.

Conclusion

Distinct cuproptosis-related patterns have significant differences on prognosis and immune cell infiltration in kidney renal clear cell carcinoma (KIRC). Cuproptosis signature and risk score are able to provide guidance for precision therapy and accurate prognosis prediction for patients with KIRC.

A novel marker based on necroptosis-related long non-coding RNA for forecasting prognostic in patients with clear cell renal cell carcinoma

Background: The incidence of clear cell renal cell carcinoma (ccRCC) is high and has increased gradually in recent years. At present, due to the lack of effective prognostic indicators, the prognosis of ccRCC patients is greatly affected.Necroptosis is a type of cell death, and along with cell necrosis is considered a new cancer treatment strategy. The aim of this study was to construct a new marker for predicting the prognosis of ccRCC patients based on long non-coding RNA (nrlncRNAs) associated with necroptosis.

Methods: RNA sequence data and clinical information of ccRCC patients from the Cancer Genome Atlas database (TCGA) were downloaded. NrlncRNA was identified by Pearson correlation study. The differentially expressed nrlncRNA and nrlncRNA pairs were identified by univariate Cox regression and Lasso-Cox regression. Finally, a Kaplan-Meier survival study, Cox regression, clinicopathological features correlation study, and receiver operating characteristic (ROC) spectrum were used to evaluate the prediction ability of 25-nrlncrnas for markers. In addition, correlations between the risk values and sensitivity to tumor-infiltrating immune cells, immune checkpoint inhibitors, and targeted drugs were also investigated.

Results: In the current research, a novel marker of 25-nrlncRNAs pairs was developed to improve prognostic prediction in patients with ccRCC. Compared with clinicopathological features, nrlncRNAs had a higher diagnostic validity for markers, with the 1-year, 3-years, and 5-years operating characteristic regions being 0.902, 0.835, and 0.856, respectively, and compared with the stage of 0.868, an increase of 0.034. Cox regression and stratified survival studies showed that this marker could be an independent predictor of ccRCC patients. In addition, patients with different risk scores had significant differences in tumor-infiltrating immune cells, immune checkpoint, and semi-inhibitory concentration of targeted drugs. The feature could be used to evaluate the clinical efficacy of immunotherapy and targeted drug therapy.

Conclusion: 25-nrlncRNAs pair markers may help to evaluate the prognosis and molecular characteristics of ccRCC patients, which improve treatment methods and can be more used in clinical practice.

Crosstalk of three novel types of programmed cell death defines distinct microenvironment characterization and pharmacogenomic landscape in breast cancer

Background

Prior studies have highlighted that novel programmed cell death (PCD) modalities, including ferroptosis, pyroptosis, and necroptosis, are correlated with tumor progression and antitumor immunity. Nonetheless, comprehensive analysis of tumor microenvironment (TME) profiles mediated by the crosstalk of distinct PCD forms has not been conducted in breast cancer (BC).

Methods

Here, we curated 34 identified PCD-associated genes (PCDAGs) and applied the consensus clustering algorithm to establish PCD-mediated tumor patterns in BC. Subsequently, based on prognostic differentially expressed genes extracted from distinct PCD-mediated patterns, we applied the LASSO algorithm to construct CD_Score. Furthermore, the correlation analysis between CD_Score and TME features, molecular subtypes, clinicopathological characteristics, drug response, and immunotherapeutic efficacy was performed.

Results

Three distinct PCD-clusters were determined among 2,038 BC samples, which did not only display different clinical outcomes but highly correlated to the established immunological tumor phenotypes: “desert,” “excluded,” and “inflamed” immune profiles. Based on the CD_Score derived from the PCD-related gene signature, BC patients could be stratified into CD_Score-low and -high group, of which the former displayed satisfactory survival outcome and enhanced immune infiltration. Further exploration identified that the CD_Score-high group significantly correlated with elevated neoantigen load and higher mutation frequency in SMGs (e.g., TP53 and MAP3K1) and reduced expression of immune checkpoint proteins.

Conclusions

This research is the first to emphasize the close relationship between distinct cell death modalities and the diversity and complexity of immune infiltration in TME. We established the CD_Score, which could help enhance our cognition of TME features and facilitate the clinical application of immunotherapy.

Identification of molecular subtypes, risk signature, and immune landscape mediated by necroptosis-related genes in non-small cell lung cancer

Background

Non-small cell lung cancer (NSCLC) is a highly heterogeneous malignancy with an extremely high mortality rate. Necroptosis is a programmed cell death mode mediated by three major mediators, RIPK1, RIPK3, and MLKL, and has been shown to play a role in various cancers. To date, the effect of necroptosis on NSCLC remains unclear.

Methods

In The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we downloaded transcriptomes of lung adenocarcinoma (LUAD) patients and their corresponding clinicopathological parameters. We performed multi-omics analysis using consensus clustering based on the expression levels of 40 necroptosis-related genes. We constructed prognostic risk models and used the receiver operating characteristic (ROC) curves, nomograms, and survival analysis to evaluate prognostic models.

Results

With the use of consensus clustering analysis, two distinct subtypes of necroptosis were identified based on different mRNA expression levels, and cluster B was found to have a better survival advantage. Correlation results showed that necroptosis was significantly linked with clinical features, overall survival (OS) rate, and immune infiltration. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis confirmed that these differential genes were valuable in various cellular and biological functions and were significantly enriched in various pathways such as the P53 signaling pathway and cell cycle. We further identified three genomic subtypes and found that gene cluster B patients had better prognostic value. Multivariate Cox analysis identified the 14 best prognostic genes for constructing prognostic risk models. The high-risk group was found to have a poor prognosis. The construction of nomograms and ROC curves showed stable validity in prognostic prediction. There were also significant differences in tumor immune microenvironment, tumor mutational burden (TMB), and drug sensitivity between the two risk groups. The results demonstrate that the 14 genes constructed in this prognostic risk model were used as tumor prognostic biomarkers to guide immunotherapy and chemotherapy. Finally, we used qRT-PCR to validate the genes involved in the signature.

Conclusion

This study promotes our new understanding of necroptosis in the tumor microenvironment of NSCLC, mines prognostic biomarkers, and provides a potential value for guiding immunotherapy and chemotherapy.

Identification of a Necroptosis-Related Prognostic Signature and Associated Regulatory Axis in Liver Hepatocellular Carcinoma

Background

Liver hepatocellular carcinoma (LIHC) ranks the sixth in global cancer incidence with poor prognosis. Necroptosis is a kind of regulated cell death and has been proved to be of significance in cancer occurrence and progression. However, few studies comprehensively discuss the potential applications of necroptosis-related genes (NRGs) in the prognostic evaluation and immunotherapy of LIHC.

Methods

The prognostic signature in the present study was built up using LASSO Cox regression analysis. Integrated bioinformatics tools were utilized to explore the potential mRNA-miRNA-lncRNA regulatory axis in LIHC. Furthermore, qRT-PCR method was used to verify the EZH2 expression in LIHC tissues. Furthermore, prognostic performance of EZH2 in LIHC was assessed by Kaplan-Meier method.

Results

A total of 14 NRGs were differentially expressed in LIHC tissues. The overall genetic mutation status of these NRGs in LIHC was also shown. NRGs were significantly correlated with programmed necrotic cell death, as well as Toll-like receptor signaling pathway in GO and KEGG pathway analysis. Kaplan-Meier analysis revealed that ALDH2, EZH2, NDRG2, PGAM5, RIPK1, and TRAF2 were related to the prognosis. A prognostic signature was constructed by these six genes and showed medium to high accuracy in the prediction of LIHC patients' prognosis. Further analysis revealed that NRGs were correlated with pathological stage, immune infiltration, and drug resistance in LIHC. Moreover, we identified a potential lncRNA TUG1/miR-26b-5p/EZH2 regulatory axis in LIHC, which might affect the progression of LIHC. qRT-PCR suggested a higher mRNA level of EZH2 in LIHC tissues. And a poor overall survival rate was detected in LIHC patients with high EZH2 expression. Moreover, EZH2 expression and cancer stage were identified as the independent risk factors affecting LIHC patients' prognosis.

Conclusion

In the present study, we conducted comprehensive bioinformatic analyses and built up a necroptosis-related prognostic signature containing four genes (ALDH2, EZH2, NDRG2, and PGAM5) for patients with LIHC, and this prognostic signature showed a medium to high predictive accuracy. And our study also identified a lncRNA TUG1/miR-26b-5p/EZH2 regulatory axis, which might be of great significance in LIHC progression. In addition, based on the data from our center, the result of qRT-PCR and survival analysis showed a higher mRNA level of EZH2 in LIHC tissues and an unfavorable prognosis in high EZH2 expression group, respectively.

Computational Characterizing Necroptosis Reveals Implications for Immune Infiltration and Immunotherapy of Hepatocellular Carcinoma

Necroptosis is a programmed form of necrotic cell death in regulating cancer ontogenesis, progression, and tumor microenvironment (TME) and could drive tumor-infiltrating cells to release pro-inflammatory cytokines, incurring strong immune responses. Nowadays, there are few identified biomarkers applied in clinical immunotherapy, and it is increasingly recognized that high levels of tumor necroptosis could enhance the response to immunotherapy. However, comprehensive characterization of necroptosis associated with TME and immunotherapy in Hepatocellular carcinoma (HCC) remains unexplored. Here, we computationally characterized necroptosis landscape in HCC samples from TCGA and ICGA cohorts and stratified them into two necroptosis clusters (A or B) with significantly different characteristics in clinical prognosis, immune cell function, and TME-landscapes. Additionally, to further evaluate the necroptosis levels of each sample, we established a novel necroptosis-related gene score (NRGscore). We further investigated the TME, tumor mutational burden (TMB), clinical response to immunotherapy, and chemotherapeutic drug sensitivity of HCC subgroups stratified by the necroptosis landscapes. The NRGscore is robust and highly predictive of HCC clinical outcomes. Further analysis indicated that the high NRGscore group resembles the immune-inflamed phenotype while the low score group is analogous to the immune-exclusion or metabolism phenotype. Additionally, the high NRGscore group is more sensitive to immune checkpoint blockade-based immunotherapy, which was further validated using an external HCC cohort, metastatic melanoma cohort, and advanced urothelial cancer cohort. Besides, the NRGscore was demonstrated as a potential biomarker for chemotherapy, wherein the high NRGscore patients with more tumor stem cell composition could be more sensitive to Cisplatin, Doxorubicin, Paclitaxel-based chemotherapy, and Sorafenib therapy. Collectively, a comprehensive characterization of the necroptosis in HCC suggested its implications for predicting immune infiltration and response to immunotherapy of HCC, providing promising strategies for treatment.

Characterization of Necroptosis-Related Molecular Subtypes and Therapeutic Response in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors with high morbidity and mortality and is usually associated with therapeutic resistance and poor prognosis because of individual biological heterogeneity. There is an unmet need to screen for reliable parameters, especially immunotherapy-related biomarkers to predict the patient’s outcomes. Necroptosis is a special caspase-independent form of necrotic cell death associated with the pathogenesis, progression, and prognosis of multiple tumors but the potential connection between necroptosis-related genes (NRGs) and LUAD still remains unclear. In this study, we expounded mutational and transcriptional alterations of 67 NRGs in 522 LUAD samples and proposed a consensus-clustering subtype of these patients into two cohorts with distinct immunological and clinical prognosis characteristics. Cluster B patients were associated with a better prognosis and characterized by relatively lower expression of NRGs, higher immune scores in the tumor microenvironment (TME), more mild clinical stages, and downregulated expression of immunotherapy checkpoints. Subsequently, the NRG score was further established to predict the overall survival (OS) of LUAD patients using univariate Cox, LASSO, and multivariate Cox regression analyses. The immunological characteristics and potential predictive capability of NRG scores were further validated by 583 LUAD patients in external datasets. In addition to better survival and immune-activated conditions, low-NRG-score cohorts exhibited a significant positive correlation with the mRNA stem index (mRNAsi) and tumor mutation burden (TMB) levels. Combined with classical clinical characteristics and NRG scores, we successfully defined a novel necroptosis-related nomogram to accurately predict the 1/3/5-year survival rate of individual LUAD patients, and the potential predictive capability was further estimated and validated in multiple test datasets with high AUC values. Integrated transcriptomic analysis helps us seek vital NRGs and supplements a novel clinical application of NRG scores in predicting the overall survival and therapeutic benefits for LUAD patients.

A Novel M7G-Related MicroRNAs Risk Signature Predicts the Prognosis and Tumor Microenvironment of Kidney Renal Clear Cell Carcinoma

Background: M7G modification is extremely vital for the development of many cancers, especially tumor immunity. M7G modification is a novel functional regulator of miRNA, and the researches on m7G-related miRNAs in kidney renal clear cell carcinoma (KIRC) are still insufficient. This research aims to establish a risk signature on the foundation of m7G-associated miRNAs, which can precisely forecast the prognosis of KIRC patients.

Methods: Transcriptome data and clinical data used in this study come from The Cancer Genome Atlas database. Our team utilized univariable Cox, Lasso and multivariable Cox analyses to construct a m7G-associated miRNAs risk signature that can forecast the prognosis of KIRC patients. Kaplan-Meier method, time-dependent receiver operating characteristic (ROC) curve, and the independent analysis of risk signatures were employed to verify the predictability and accuracy of the risk signature. Subsequently, based on CIBERSORT, ESTIMATE and ssGSEA algorithms, we speculated the potential impact of the proposed risk signature on tumor immune microenvironment. Ultimately, by virtue of the risk signature and tumor immunity, the hub genes affecting the prognosis of KIRC patients were screened out.

Results: Our team established and verified a prognostic signature comprising 7 m7G-associated miRNAs (miR-342-3p, miR-221-3p, miR-222-3p, miR-1277-3p, miR-6718-5p, miR-1251-5p, and miR-486-5p). The results of the Kaplan-Meier survival analysis revealed that the prognosis of KIRC sufferers in the high-risk group was often unsatisfactory. The accuracy of the prediction ability of the risk signature was verified by calculating the area under the ROC curve. Univariate-multivariate Cox analyses further showed that this risk signature could be utilized as an independent prognosis-related biomarker for KIRC sufferers. The results of the immune analysis revealed that remarkable diversities existed in immune status and tumor microenvironment between high-risk and low-risk groups. On the foundation of the proposed risk signature and other clinical factors, a nomogram was established to quantitatively forecast the survival of KIRC sufferers at 1, 3 and 5 years.

Conclusion: Based on m7G-related miRNAs, a risk signature was successfully constructed, which could precisely forecast the prognosis of sufferers and guide personalized immunotherapy for KIRC patients.

Clinical Implications of Necroptosis Genes Expression for Cancer Immunity and Prognosis: A Pan-Cancer Analysis

Background

Necroptosis, a form of programmed cell death, is increasingly being investigated for its controversial role in tumorigenesis and progression. Necroptosis suppresses tumor formation and tumor development by killing tumor cells; however, the necrotic cells also promote tumor formation and tumor development via the immunosuppressive effect of necroptosis and inflammatory response caused by cytokine release. Thus, the exact mechanism of necroptosis in pan-cancer remains unknown.

Methods

The data of 11,057 cancer samples were downloaded from the TCGA database, along with clinical information, tumor mutation burden, and microsatellite instability information of the corresponding patients. We used the TCGA data in a pan-cancer analysis to identify differences in mRNA level as well as single nucleotide variants, copy number variants, methylation profiles, and genomic signatures of miRNA-mRNA interactions. Two drug datasets (from GDSC, CTRP) were used to evaluate drug sensitivity and resistance against necroptosis genes.

Results

Necroptosis genes were aberrantly expressed in various cancers. The frequency of necroptosis gene mutations was highest in lung squamous cell carcinoma. Furthermore, the correlation between necroptosis gene expression in the tumor microenvironment and immune cell infiltration varied for different cancers. High necroptosis gene expression was found to correlate with NK, Tfh, Th1, CD8_T, and DC cells. These can therefore be used as biomarkers to predict prognosis. By matching gene targets with drugs, we identified potential candidate drugs.

Conclusion

Our study showed the genomic alterations and clinical features of necroptosis genes in 33 cancers. This may help clarify the link between necroptosis and tumorigenesis. Our findings may also provide new approaches for the clinical treatment of cancer.