Multi-Omics Approaches Identify Necroptosis‑Related Prognostic Signature and Associated Regulatory Axis in Cervical 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.

Non-apoptotic cell death programs in cervical cancer with an emphasis on ferroptosis

BACKGROUND

Cervical cancer, a pernicious gynecological malignancy, causes the mortality of hundreds of thousands of females worldwide. Despite a considerable decline in mortality, the surging incidence rate among younger women has raised serious concerns. Immortality is the most important characteristic of tumor cells, hence the carcinogenesis of cervical cancer cells pivotally requires compromising with cell death mechanisms.

METHODS

The current study comprehensively reviewed the mechanisms of non-apoptotic cell death programs to provide possible disease management strategies.

RESULTS

Comprehensive evidence has stated that focusing on necroptosis, pyroptosis, and autophagy for disease management is associated with significant limitations such as insufficient understanding, contradictory functions, dependence on disease stage, and complexity of intracellular pathways. However, ferroptosis represents a predictable role in cervix carcinogenesis, and ferroptosis-related genes demonstrate a remarkable correlation with patient survival and clinical outcomes.

CONCLUSION

Ferroptosis may be an appropriate option for disease management strategies from predicting prognosis to treatment.

Comprehensive analysis of co-expressed genes with TDP-43: prognostic and therapeutic potential in lung adenocarcinoma

BACKGROUND

Transactivating DNA-binding protein 43 (TDP-43) is intimately associated with tumorigenesis and progression by regulating mRNA splicing, transport, stability, and non-coding RNA molecules. The exact role of TDP-43 in lung adenocarcinoma (LUAD) has not yet been fully elucidated, despite extensive research on its function in various cancer types. An imperative aspect of comprehending the underlying biological characteristics associated with TDP-43 involves investigating the genes that are co-expressed with this protein. This study assesses the prognostic significance of these co-expressed genes in LUAD and subsequently explores potential therapeutic strategies based on these findings.

METHODS

Transcriptomic and clinical data pertaining to LUAD were retrieved from open-access databases to establish an association between mRNA expression profiles and the presence of TDP-43. A risk-prognosis model was developed to compare patient survival rates across various groups, and its accuracy was also assessed. Additionally, differences in tumor stemness, mutational profiles, tumor microenvironment (TME) characteristics, immune checkpoints, and immune cell infiltration were analyzed in the different groups. Moreover, the study entailed predicting the potential response to immunotherapy as well as the sensitivity to commonly employed chemotherapeutic agents and targeted drugs for each distinct group.

RESULTS

The TDP-43 Co-expressed Gene Risk Score (TCGRS) model was constructed utilizing four genes: Kinesin Family Member 20A (KIF20A), WD Repeat Domain 4 (WDR4), Proline Rich 11 (PRR11), and Glia Maturation Factor Gamma (GMFG). The value of this model in predicting LUAD patient survival is effectively illustrated by both the Kaplan-Meier (K-M) survival curve and the area under the receiver operating characteristic curve (AUC-ROC). The Gene Set Enrichment Analysis (GSEA) revealed that the high TCGRS group was primarily enriched in biological pathways and functions linked to DNA replication and cell cycle; the low TCGRS group showed primary enrichment in immune-related pathways and functions. The high and low TCGRS groups showed differences in tumor stemness, mutational burden, TME, immune infiltration level, and immune checkpoints. The predictions analysis of immunotherapy indicates that the Tumor Immune Dysfunction and Exclusion (TIDE) score (p < 0.001) and non-response rate (74% vs. 51%, p < 0.001) in the high TCGRS group are higher than those in the low TCGRS group. The Immune Phenotype Score (IPS) in the high TCGRS group is lower than in the low TCGRS group (p < 0.001). The drug sensitivity analysis revealed that the half-maximal inhibitory concentration (IC50) values for cisplatin, docetaxel, doxorubicin, etoposide, gemcitabine, paclitaxel, vincristine, erlotinib, and gefitinib (all p < 0.01) in the high TCGRS group are lower than those in the low TCGRS group.

CONCLUSIONS

The TCGRS derived from the model exhibits a reliable biomarker for evaluating both prognosis and treatment effectiveness among patients with LUAD. This study is anticipated to offer valuable insights into developing effective treatment strategies for this patient population. It is believed that this study is anticipated to contribute significantly to clinical diagnostics, the development of therapeutic drugs, and the enhancement of patient care.

Conduction and validation of a novel prognostic signature in cervical cancer based on the necroptosis characteristic genes via integrating of multiomics data

The significance of necroptosis in recurrent or metastatic cervical cancer remains unclear. In this study, we utilized various bioinformatics methods to analyze the cancer genome atlas (TCGA) data, gene chip and the single-cell RNA-sequencing (scRNA seq) data. And a necroptosis-related genes signature for prognostic assessment of patients with cervical cancer was constructed successfully. Survival analysis, receiver operating characteristic (ROC) curve, the support vector machine recursive feature elimination (SVM-RFE) algorithm and random forest analysis were performed to validate this signature. Patients in TCGA-CESC cohort were grouped into "high-necroptosis score (H-NCPS)" vs "low-necroptosis score (L-NCPS)" subgroups based on the median of necroptosis score of each patient. Analyses of the tumor microenvironment manifested "H-NCPS" patients associated with lower degree of immune infiltration. Through the utilization of survival analysis, cell communication, and Gene Set Enrichment Analysis (GSEA), PGK1 was determined to be the pivotal gene within the 9-gene signature associated with necroptosis. The high expression of PGK1 in cervical cancer cells was confirmed through the utilization of quantitative real-time polymerase chain reaction (RT-qPCR) and the human protein atlas (HPA). In the interim, PGK1 prompted the transition of M1 macrophages to M2 macrophages and influenced the occurrence and development of necroptosis. In conclusion, the 9-gene signature developed from necroptosis-related genes has shown significant predictive capabilities for the prognosis of cervical cancer, offered valuable guidance for individualized and targeted treatment approaches for patients.

A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs

Background

Polycystic ovary syndrome (PCOS), a common endocrine and reproductive disorder, lacks precise diagnostic strategies. Necroptosis was found to be crucial in reproductive and endocrine disorders, but its function in PCOS remains unclear. We aimed to identify differentially diagnostic genes for necroptosis (NDDGs), construct a diagnostic model to assess the progression of PCOS and explore the potential therapeutic drugs.

Methods

Gene expression datasets were combined with weighted gene co-expression network analysis (WGCNA) and necroptosis gene sets to screen the differentially expressed genes for PCOS. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a necroptosis-related gene signatures. Independent risk analyses were performed using nomograms. Pathway enrichment of NDDGs was conducted with the GeneMANIA database and gene set enrichment analysis (GSEA). Immune microenvironment analysis was estimated based on ssGSEA algorithm analysis. The Comparative Toxicogenomics Database (CTD) was used to explore potential therapeutic drugs for NDDGs. The expression of NDDGs was validated in GSE84958, mouse model and clinical samples.

Results

Four necroptosis-related signature genes, IL33, TNFSF10, BCL2 and PYGM, were identified to define necroptosis for PCOS. The areas under curve (AUC) of receiver operating characteristic curve (ROC) for training set and validation in diagnostic risk model were 0.940 and 0.788, respectively. Enrichment analysis showed that NDDGs were enriched in immune-related signaling pathways such as B cells, T cells, and natural killer cells. Immune microenvironment analysis revealed that NDDGs were significantly correlated with 13 markedly different immune cells. A nomogram was constructed based on features that would benefit patients clinically. Several compounds, such as resveratrol, tretinoin, quercetin, curcumin, etc., were mined as therapeutic drugs for PCOS. The expression of the NDDGs in the validated set, animal model and clinical samples was consistent with the results of the training sets.

Conclusion

In this study, 4 NDDGs were identified to be highly effective in assessing the progression and prognosis of PCOS and exploring potential targets for PCOS treatment.

Omics approaches to better understand the molecular mechanism of necroptosis and their translational implications

Necroptosis is a type of programed cell death characterized by an inflammatory phenotype due to extensive membrane permeabilization and rupture. Initiation of necroptosis involves activation of tumor necrosis factor receptors by tumor necrosis factor alpha (TNFα) followed by coordinated activities of receptor-interacting protein kinases and mixed lineage kinase-like protein (MLKL). Subsequently, MLKL undergoes phosphorylation and translocates to the plasma membrane, leading to permeabilization. Such permeabilization results in the release of various cytokines and causes extensive inflammatory activity at the organismal level. This inflammatory activity is one of the major differences between apoptosis and necroptosis and links necroptosis to several human pathologies that exhibit inflammation, in addition to the ultimate cell death phenotype. Given the crosstalk between the activation of cell death pathway and inflammatory activity, approaches that provide insights on the regulation of transcripts, proteins and their processing at the global level have substantially improved our understanding of necroptosis and its involvement in different disease states. In this review, we highlight recent omic studies probing the transcriptome, proteome and lipidome which elucidate potential new mechanisms and signaling pathways during necroptosis and the necroptosis-associated inflammatory activity observed in various diseases. We specifically focus on studies investigating the transcriptome and intracellular and released proteome that contribute to inflammatory nature of necroptotic cells. We also highlight different lipids that have been implicated in necroptosis and lipidomic studies identifying lipid players in necroptosis. Finally, we review studies which suggest certain necroptosis-related genes as potential prognosis markers for different cancers and discuss their translational implications.

A novel necroptosis-related lncRNA signature for predicting prognosis and anti-cancer treatment response in endometrial cancer

BACKGROUND

Necroptosis, a form of programmed cell death, underlies tumorigenesis and the progression of cancers. Anti-cancer strategies targeting necroptosis have increasingly been shown to present a potential cancer therapy. However, the predictive utility and anticancer sensitivity value of necroptosis-related lncRNAs (NRLs) for endometrial cancer (EC) are currently unknown.

METHODS

EC patient gene expression profiles and the corresponding clinical information collected from The Cancer Genome Atlas were used to identify NRLs that constituted a predictive signature for EC. The functional pathways, immune status, clinicopathological correlation, and anticancer drug sensitivity of the patients relative to the NRLs signatures were analyzed.

RESULTS

A signature composed of 7 NRLs (AC019080.5, BOLA3-AS1, AC022144.1, AP000345.2, LEF1-AS1, AC010503.4, and RPARP-AS1) was identified. The high-risk patient group with this signature exhibited a poorer prognosis and lower survival rate than low-risk group lacking this signature. This necroptosis-related lncRNA signature had a higher predictive accuracy compared with other clinicopathological variables (area under the receiver operating characteristic curve of the risk score: 0.717). Additionally, when patients were stratified based on other clinicopathological variables, the overall survival was significantly shorter in the high-risk versus low-risk group across all cohorts. Gene set enrichment analysis (GSEA) revealed that immune- and tumor-related signaling pathways and biological processes were enriched in the high-risk group compared to the low-risk group. Single-sample gene set enrichment analysis (ssGSEA) additionally showed that the resulting risk score was strongly correlated with EC patient immune status. Finally, patients with high-risk scores were more sensitive to the anti-cancer drugs such as Docetaxel, Mitomycin.C, Vinblastine, AZD.2281 (olaparib), AZD6244, and PD.0332991 (Palbociclib).

CONCLUSION

These findings reveal a novel necroptosis-related lncRNA signature for predicting EC patient prognosis and shed new light on anticancer therapy strategies for EC.

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.