A Risk Model Developed Based on Necroptosis Predicts Overall Survival for Hepatocellular Carcinoma and Identification of Possible Therapeutic Drugs

Disulfidptosis-related signatures for prognostic and immunotherapy reactivity evaluation in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world and a nonnegligible health concern on a worldwide scale. Disulfidptosis is a novel mode of cell death, which is mainly caused by the collapse of the actin skeleton. Although many studies have demonstrated that various types of cell death are associated with cancer treatment, the relationship between disulfidptosis and HCC has not been elucidated.

METHODS

Here, we mainly applied bioinformatics methods to construct a disulfidptosis related risk model in HCC patients. Specifically, transcriptome data and clinical information were downloaded from the Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) database. A total of 45 co-expressed genes were extracted between the disulfidptosis-related genes (DRGs) and the differential expression genes (DEGs) of liver hepatocellular carcinoma (LIHC) in the TCGA database. The LIHC cohort was divided into two subgroups with different prognosis by k-mean consensus clustering and functional enrichment analysis was performed. Subsequently, three hub genes (CDCA8, SPP2 and RDH16) were screened by Cox regression and LASSO regression analysis. In addition, a risk signature was constructed and the HCC cohort was divided into high risk score and low risk score subgroups to compare the prognosis, clinical features and immune landscape between the two subgroups. Finally, the prognostic model of independent risk factors was constructed and verified.

CONCLUSIONS

High DRGs-related risk score in HCC individuals predict poor prognosis and are associated with poor immunotherapy response, which indicates that risk score assessment model can be utilized to guide clinical treatment strategy.

Construction and validation model of necroptosis-related gene signature associates with immunity for osteosarcoma patients

Osteosarcoma is the most common malignant tumor in children and adolescents and its diagnosis and treatment still need to be improved. Necroptosis has been associated with many malignancies, but its significance in diagnosing and treating osteosarcoma remains unclear. The objective is to establish a predictive model of necroptosis-related genes (NRGs) in osteosarcoma for evaluating the tumor microenvironment and new targets for immunotherapy. In this study, we download the osteosarcoma data from the TARGET and GEO websites and the average muscle tissue data from GTEx. NRGs were screened by Cox regression analysis. We constructed a prediction model through nonnegative matrix factorization (NMF) clustering and the least absolute shrinkage and selection operator (LASSO) algorithm and verified it with a validation cohort. Kaplan–Meier survival time, ROC curve, tumor invasion microenvironment and CIBERSORT were assessed. In addition, we establish nomograms for clinical indicators and verify them by calibration evaluation. The underlying mechanism was explored through the functional enrichment analysis. Eight NRGs were screened for predictive model modeling. NRGs prediction model through NMF clustering and LASSO algorithm was established. The survival, ROC and tumor microenvironment scores showed significant statistical differences among subgroups (P < 0.05). The validation model further verifies it. By nomogram and calibration, we found that metastasis and risk score were independent risk factors for the poor prognosis of osteosarcoma. GO and KEGG analyses demonstrate that the genes of osteosarcoma cluster in inflammatory, apoptotic and necroptosis signaling pathways. The significant role of the correlation between necroptosis and immunity in promoting osteosarcoma may provide a novel insight into detecting molecular mechanisms and targeted therapy.

Bibliometric analysis of publications on necroptosis from 2001 to 2021

Background: Necroptosis plays an important role in inflammation, cancer, and neurodegenerative diseases. In recent years, the number of studies related to necroptosis has increased and research has become increasingly in-depth. This study aimed to summarize the research conducted since 2001 to discover hotspots and trends in the field of necroptosis.

Methods: The Web of Science Core database was used to identify global publications on necroptosis from 2001 to 2021. Bibliometric analysis was performed using Rstudio, VOSviewer, and CiteSpace.

Results: The number of publications related to necroptosis gradually increased from 2001 to 2021. Vandenabeele P had the most publications at 45. Yuan JY had the most citations at 5,901. Necroptosis research has been dominated by China and Chinese institutions. Cell Death and Disease had the highest number of related publications among the examined journals. Seven of the top 10 most cited papers had more than 500 citations. Necroptosis, cell death, autophagy, injury, cancer, activated B cell nuclear factor kappa-light chain enhancer, and oxidative stress were important keywords in keyword analysis. Recent research has increasingly focused on breast cancer, receptor-interacting serine/threonine protein kinase 1, modulation, pseudokinase mixed lineage kinase domain-like protein, membrane, protection, and cycle.

Conclusion: Interest in necroptosis-related research continues to increase steadily, and there is close cooperation between countries and institutions in the field of necroptosis. The study of necroptosis-related molecules and mechanisms, and the relationship between necroptosis and cancer, may be hotspots and directions in future research.

A necroptosis -related signature for predicting prognosis and immunotherapy in hepatocellular carcinoma

Background: Hepatocellular Carcinoma (HCC) is an aggressive tumor with an inferior prognosis. Necroptosis is a new form of programmed death that plays a dual effect on the tumor. However, the role of necroptosis-related genes(NRGs) in HCC remains unknown.

Methods: All datasets were downloaded from publicly available databases. The consensus clustering analysis was used to classify patients into different subtypes based on NRGs. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression were used to develop a prognostic signature. Tumor Immune Dysfunction and Exclusion (TIDE) was used to predict immunotherapy response.

Results: The genetic and transcriptional changes of NRGs were observed in HCC. Patients were classified into three clusters based on differentially expressed NRGs, of which Cluster-3 had the worst prognosis and the highest immune infiltration. The prognostic signature was developed based on 8-NRGs, which have shown excellent prognostic performance. The high-risk group in the signature presented significantly higher immune infiltration, such as aDCs, iDCs, macrophages, and Treg, compared to the low-risk group. TMB and immune checkpoints were also higher in the high-risk group. Moreover, a lower TIDE score was observed in the high-risk group, indicating the patients with high risk-score may be suitable for immunotherapy. Via the dataset of IMvigor210, we found a higher risk score in the immunotherapy response group.

Conclusion: We developed a new necroptosis-related signature for predicting prognosis with the potential to predict immunotherapy for HCC patients.