A Novel Necroptosis-Associated lncRNA Signature Can Impact the Immune Status and Predict the Outcome of Breast Cancer

The fate and function of non-coding RNAs during necroptosis

Necroptosis is a novel form of cell death which is activated when apoptotic cell death signals are disrupted. Accumulating body of observations suggests that noncoding RNAs, which are the lately discovered mystery of the human genome, are significantly associated with necroptotic signaling circuitry. The fate and function of miRNAs have been well documented in human disease, especially cancer. Recently, lncRNAs have gained much attention due to their diverse regulatory functions. Although available studies are currently based on bioinformatic analysis, predicted interactions desires further attention, as these hold significant promise and should not be overlooked. In the light of these, here we comprehensively review and discuss noncoding RNA molecules that play significant roles during execution of necroptotic cell death.

Identifying prognostic characteristics of m6A-related glycolysis gene and predicting the immune infiltration landscape in bladder cancer

BACKGROUNDS

Glucose metabolism is associated with the development of cancers, and m6A RNA methylation regulator-related genes play vital roles in bladder urothelial carcinoma (BLCA). However, the role of m6A-related glucose metabolism genes in BLCA occurrence and development has not yet been reported. Our study aims to integrate m6A- and glycolysis-related genes and find potential gene targets for clinical diagnosis and prognosis of BLCA patients.

METHODS

Sequencing data and clinical information on BLCA were extracted from common databases. Univariate Cox analysis was used to screen prognosis-related m6A glucose metabolism genes; BLCA subtypes were distinguished using consensus clustering analysis. Subsequently, genes associated with BLCA occurrence and development were identified using the "limma" R package. The risk score was then calculated, and a nomogram was constructed to predict survival rate of BLCA patients. Functional and immune microenvironment analyses were performed to explore potential functions and mechanisms of the different risk groups.

RESULTS

Based on 70 prognosis-related m6A glucose metabolism genes, BLCA was classified into two subtypes, and 34 genes associated with its occurrence and development were identified. Enrichment analysis revealed an association of genes in high-risk groups with tricarboxylic acid cycle function and glycolysis. Moreover, significantly higher levels of seven immune checkpoints, 14 immune checkpoint inhibitors, and 32 immune factors were found in high-risk score groups.

CONCLUSIONS

This study identified two biomarkers associated with BLCA prognosis; these findings may deepen our understanding of the role of m6A-related glucose metabolism genes in BLCA development. We constructed a m6A-related glucose metabolism- and immune-related gene risk model, which could effectively predict patient prognosis and immunotherapy response and guide individualized immunotherapy.

Comprehensive Analysis of Cuproptosis Genes and Identification of Cuproptosis Subtypes in Breast Cancer

BACKGROUND

Copper-induced death (cuproptosis) is copper-dependent regulated cell death, which is different from known death mechanisms and is dependent on mitochondrial respiration. However, its effect on breast cancer (BRCA) is unclear.

OBJECTIVE

The objective of this study is to explore the important clinical significance of cuproptosis genes and to provide a new idea for guiding the personalized immunotherapy strategy of BRCA patients.

MATERIALS AND METHODS

We collected cuproptosis genes from published work. The gene alteration, differential expression, and prognostic value of cuproptosis genes were explored in BRCA based on TCGA database. We identified two subtypes (clusters A and B) by performing unsupervised clustering. The difference between two clusters was deeply explored, including clinical features, differential expressed genes (DEGs), pathways, and immune cell infiltration. Based on the DEGs between two clusters, a cuproptosis score was constructed and its predictive capability for overall survival of BRCA patients was validated.

RESULTS AND DISCUSSION

Patients with high cuproptosis score have worse survival status, with an increased infiltration level of most immune cells. Further analysis suggested that BRCA patients with high cuproptosis score may be sensitive to immune checkpoint inhibitor (ICI) treatment.

CONCLUSION

Our findings may improve our understanding of cuproptosis in BRCA and may distinguish patients suitable for ICI treatment.

Relative refractoriness of breast cancer cells to tumour necrosis factor-α induced necroptosis

Necroptosis, a recently identified programmed cell death pathway, has attracted attention as an alternative route to target apoptosis-resistant cancer cells. The status of the necroptosis pathway in different subtypes of breast cancer has not been well explored. Stimulating the cells by TNF-α can trigger cell survival or death depending on the combination of downstream players involved. In this work, we attempted to induce necroptosis in them using a combination of TNF-α and Z-VAD-FMK with and without chemotherapy. Cell viability, apoptosis, and necroptosis were assessed using MTT and Annexin-V/PI assays, respectively. Gene and protein expression was analysed by qPCR and immunophenotyping. Both the cell lines were resistant to induction of cell death by necroptosis. There was no enhancement in cell death when chemotherapeutic drugs were combined with necroptosis induction. Expression studies showed reduced translational expression of key necroptosis molecules like RIP kinases and MLKL in breast cancer cells compared to positive control cell line L929. Also, cell survival molecules were expressed more in MDA-MB-231 in contrast to death pathway molecules which were expressed more in T47D cells. In this work, the two breast cancer cell lines were observed to be resistant to TNF-α induced necroptosis with or without chemotherapy. Expression of key necroptosis players revealed relative insufficiency of the molecular machinery involved in the above pathway. In our opinion this may be the cause for resistance to necroptosis and novel strategies to upregulate these molecules need to be developed to sensitize the breast cancer cells towards cell death by necroptosis.

Qualification of Necroptosis-Related lncRNA to Forecast the Treatment Outcome, Immune Response, and Therapeutic Effect of Kidney Renal Clear Cell Carcinoma

Background

Kidney renal clear cell carcinoma (KIRC) is considered as a highly immune infiltrative tumor. Necroptosis is an inflammatory programmed cell death associated with a wide range of diseases. Long noncoding RNAs (lncRNAs) play important roles in gene regulation and immune function. lncRNA associated with necroptosis could systematically explore the prognostic value, regulate tumor microenvironment (TME), etc.

Method

The patients' data was collected from TCGA datasets. We used the univariate Cox regression (UCR) to select prediction lncRNAs that are related to necroptosis. Meanwhile, risk models were constructed using LASSO Cox regression (LCR). Kaplan–Meier (KM) analysis, accompanied with receiver operating characteristic (ROC) curves, was performed to assess the independent risk factors of different clinical characteristics. The evaluated factors are age, gender, disease staging, grade, and their related risk score. Databases such as Gene Ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSEA) were used to search the probable biological characteristics that could influence the risk groups, containing signaling pathway and immue-related pathways. The single-sample gene set enrichment analysis (ssGSEA) was chosen to perform gene set variation analysis (GSVA), and the GSEABase package was selected to detect the immune and inflammatory infiltration profiles. The TIDE and IC₅₀ evaluation were used to estimate the effectiveness of clinical treatment on KIRC.

Results

Based on the above analysis, we have got a conclusion that patients who show high risk had higher immune infiltration, immune checkpoint expression, and poorer prognosis. We identified 19 novel prognostic necroptosis-related lncRNAs, which could offer opinions for a deeper study of KIRC.

Conclusion

The risk model we constructed makes it possible to predict the prognosis of KIRC patients and offers directions for further research on the prognostication and treatment strategies for KIRC.

A novel necroptosis-related LncRNA signature for prediction of prognosis and therapeutic responses of head and neck squamous cell carcinoma

Background: Long non-coding RNAs (lncRNAs) play an essential role in the occurrence and prognosis of tumors, and it has great potential as biomarkers of tumors. However, the roles of Necroptosis-related lncRNA (NRLs) in Head and neck squamous cell carcinoma (HNSCC) remain elusive. Methods: We comprehensively analyzed the gene expression and clinical information of 964 HNSCC in four cohorts. LASSO regression was utilized to construct a necroptosis-related lncRNA prognosis signature (NLPS). We used univariate and multivariate regression to assess the independent prognostic value of NLPS. Based on the optimal cut-off, patients were divided into high- and low-risk groups. In addition, the immune profile, multi-omics alteration, and pharmacological landscape of NLPS were further revealed. Results: A total of 21 NRLs associated with survival were identified by univariate regression in four cohorts. We constructed and validated a best prognostic model (NLPS). Compared to the low-risk group, patients in the high group demonstrated a more dismal prognosis. After adjusting for clinical features by multivariate analysis, NLPS still displayed independent prognostic value. Additionally, further analysis found that patients in the low-risk group showed more abundant immune cell infiltration and immunotherapy response. In contrast, patients in the high-risk group were more sensitive to multiple chemotherapeutic agents. Conclusion: As a promising tool, the establishment of NLPS provides guidance and assistance in the clinical management and personalized treatment of HNSCC.

Necroptosis-related lncRNA signatures determine prognosis in breast cancer patients

Necroptosis is a genetically regulated form of necrotic cell death that has emerged as an important pathway in cancers. Long non-coding RNAs (lncRNAs) are key regulators of breast cancer development. Nevertheless, few studies are reporting the effect of lncRNAs in necroptosis processes and the role of necroptosis-related lncRNAs (NRLs). The present study aimed to construct a prognostic model based on NRLs in breast cancer. NRLs were identified by combining expression profiling data from The Cancer Genome Atlas (TCGA) with necroptosis-related genes. The non-negative matrix factorization (NMF) clustering analysis was conducted to identify molecular subtypes of BC, and the clinical outcome and tumor-infiltrating immune cells (TIICs) in the different molecular subtypes were analyzed. Four molecular subtypes based on NRLs were identified, and these four molecular subtypes could predict clinical features, prognosis, and tumor-infiltrating immune cells (TIICs). A 4-NRLs signature and nomogram were established and validated its predictive capability of overall survival (OS) in breast cancer patients. Analyses of clinicopathological features, prognosis, TIICs, tumor microenvironment (TME), somatic mutations, and drug response revealed significant differences between the two risk groups. In addition, we found that low-risk patients exhibited higher levels of immune checkpoints and showed higher immunogenicity in immunophenoscore (IPS) analysis. In conclusion, we constructed a prognostic model based on the expression profile of NRLs, which may facilitate the assessment of patient prognosis, immunotherapeutic responses, and maybe a promising therapeutic target in clinical practice.

A novel necroptosis-related lncRNA signature for predicting prognosis and immune response of colon cancer

Background: Numerous lncRNAs have been shown to affect colon cancer (CC) progression, and tumor necroptosis is regulated by several of them. However, the prognostic value of necroptosis-related lncRNA in CC has rarely been reported. In this study, a necroptosis-related lncRNA prognostic model was constructed, which can provide a reference for clinical diagnosis and treatment. Methods: The Cancer Genome Atlas (TCGA) database provided gene expression and lncRNA sequencing data for CC patients, and GSEA provided necroptosis gene data. Differentially expressed necroptosis-related lncRNAs related to prognosis were identified by differential expression analysis, Pearson correlation analysis, and least absolute shrinkage and selection operator (LASSO) regression. Based on the results of the multivariate COX regression analysis, a risk scoring model was constructed, A Kaplan-Meier analysis was performed to compare overall survival (OS) between low-risk and high-risk groups. A nomogram was then developed and validated based on the clinical data and risk scores of CC patients. In addition, Gene Set Enrichment Analysis (GSEA) and immune correlation analysis were conducted to explore the possible pathways and immune regulatory effects of these necroptosis-related lncRNAs. Results: In total, we identified 326 differentially expressed necroptosis-related lncRNAs in the TCGA database. Survival analysis showed that the OS of patients in the low-risk group was significantly better than that in the high-risk group (p < 0.05). Finally, 10 prognostic necroptosis-related lncRNAs were used to construct the nomogram. The composite nomogram prediction model evaluated and validated with good prediction performance (3-year AUC = 0.85, 5-years AUC = 0.82, C-index = 0.78). The GSEA and immune correlation analyses indicated that these lncRNAs may participate in multiple pathways involved in CC pathogenesis and progression. Conclusion: We established a novel necroptosis-related lncRNA CC prognosis prediction model, which can provide a reference for clinicians to formulate personalized treatment and review plans for CC patients. In addition, we also found that these necroptosis-related lncRNAs may affect the pathogenesis and progression of colon cancer through multiple pathways, including altering the activity of various immune cells.