The Apoptosis Paradox in Cancer

Gene mutation is a complicated process that influences the onset and progression of cancer, and the most prevalent mutation involves the TP53 gene. One of the ways in which the body maintains homeostasis is programmed cell death, which includes apoptosis, autophagic cell death, pyroptosis, ferroptosis, NETosis, and the more recently identified process of cuprotosis. Evasion of these cell deaths is a hallmark of cancer cells, and our elucidation of the way these cells die helps us better understands the mechanisms by which cancer arises and provides us with more ways to treat it.Studies have shown that programmed cell death requires wild-type p53 protein and that mutations of TP53 can affect these modes of programmed cell death. For example, mutant p53 promotes iron-dependent cell death in ferroptosis and inhibits apoptotic and autophagic cell death. It is clear that TP53 mutations act on more than one pathway to death, and these pathways to death do not operate in isolation. They interact with each other and together determine cell death. This review focuses on the mechanisms via which TP53 mutation affects programmed cell death. Clinical investigations of TP53 mutation and the potential for targeted pharmacological agents that can be used to treat cancer are discussed.

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“…prevent growth and metastasis of tumors (71). As a tumor suppressor gene, TP53 plays an important role in regulating apoptosis.…”

Section: Role Of Tp53 Mutation In Apoptosismentioning

confidence: 99%

Current insights into the regulation of programmed cell death by TP53 mutation in cancer

Sai

Zhou

et al. 2022

Front. Oncol.

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“…The enrichment scores of cell pathways, such as tumour proliferation, epithelialmesenchymal transition (EMT), angiogenesis, and apoptosis, were higher in the high-risk group, also suggesting that the AAM-related gene signature plays an essential role in the tumourigenesis and progression in ccRCC. Some studies demonstrated that tumours with higher levels of apoptosis are more aggressive (Morana et al, 2022). Thus, the above-selected gene, consisting of an AAM-related gene signature, plays an essential role in cancer progression and prognosis prediction for ccRCC.…”

Section: Discussionmentioning

confidence: 99%

Novel amino acid metabolism‐related gene signature to predict prognosis in clear cell renal cell carcinoma

et al. 2022

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Background: Amino acid metabolism (AAM) deregulation, an emerging metabolic hallmark of malignancy, plays an essential role in tumour proliferation, invasion, and metastasis. However, the expression of AAM-related genes and their correlation with prognosis in clear cell renal cell carcinoma (ccRCC) remain elusive. This study aims to develop a novel consensus signature based on the AAM-related genes.Methods: The RNA-seq expression data and clinical information for ccRCC were downloaded from the TCGA (KIRC as training dataset) and ArrayExpress (E-MTAB-1980 as validation dataset) databases. The AAM‐related differentially expressed genes were screened via the “limma” package in TCGA cohorts for further analysis. The machine learning algorithms (Lasso and stepwise Cox (direction = both)) were then utilised to establish a novel consensus signature in TCGA cohorts, which was validated by the E-MTAB-1980 cohorts. The optimal cutoff value determined by the “survminer” package was used to categorise patients into two risk categories. The Kaplan-Meier curve, the receiver operating characteristic (ROC) curve, and multivariate Cox regression were utilised to evaluate the prognostic value. The nomogram based on the gene signature was constructed, and its performance was analysed using ROC and calibration curves. Gene Set Enrichment Analysis (GSEA) and immune cell infiltration analysis were conducted on its potential mechanisms. The relationship between the gene signature and key immune checkpoint, N6-methyladenosine (m6A)-related genes, and sensitivity to chemotherapy was assessed.Results: A novel consensus AMM‐related gene signature consisting of IYD, NNMT, ACADSB, GLDC, and PSAT1 is developed to predict prognosis in TCGA cohorts. Kaplan-Meier survival shows that overall survival in the high-risk group was more dismal than in the low-risk group in the TCGA cohort, validated by the E-MTAB-1980 cohort. Multivariate regression analysis also demonstrates that the gene signature is an independent predictor of ccRCC. Immune infiltration analysis highlighted that the high-risk group indicates an immunosuppressive microenvironment. It is also closely related to the level of key immune checkpoints, m6A modification, and sensitivity to chemotherapy drugs.Conclusion: In this study, a novel consensus AAM-related gene signature is developed and validated as an independent predictor to robustly predict the overall survival from ccRCC, which would further improve the clinical outcomes.

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“…The process of apoptosis can be roughly divided into the following stages: receiving apoptosis signal → interaction between apoptosis regulating molecules → activation of proteolytic enzyme (Caspase) → entering a continuous reaction process ( 13 ). Due to the different initiation stages of apoptosis, the signaling pathway of apoptosis can be divided into three main pathways, namely mitochondrial pathway, endoplasmic reticulum pathway, and death receptor pathway.…”

Section: Discussionmentioning

confidence: 99%

A Signature of Three Apoptosis-Related Genes Predicts Overall Survival in Breast Cancer

et al. 2022

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BackgroundThe commonest malignancy in women is known as breast cancer (BC). Numerous studies demonstrated that apoptosis appears to be critical to the management and clinical outcome of BC patients. The purpose of this study is to explore the potential connection between apoptosis and BC and establish the apoptosis-associated gene signature in BC.MethodsThe data of BC patient transcripts and related clinical information comes from the Cancer Genome Atlas Database (TCGA), and the genes related to apoptosis come from the Molecular Characterization Database (MSigDB). We identified the abnormally expressed apoptosis-related genes in BC samples. The optimal apoptosis-related genes screened by Cox regression analysis were designed to construct a prognostic model for predicting BC patients. Using the Nom Chart to Predict 1-Year, 3-Year, and 5-Year overall survival for BC patients. The gene signature-related functional pathways were explored by gene set enrichment analysis (GSEA).ResultsThree genes [alpha subunit of the interleukin 3 receptor (IL3RA), apoptosis-inducing factor mitochondrial-associated 1 (AIFM1), and phosphatidylinositol-3 kinase catalytic alpha (PIK3CA)] correlated with apoptosis were shown to be strongly linked to the overall survival of BC. Survival analysis shows that the risk score is directly proportional to the poor prognosis of BC patients. Risk assessment based on three genetic characteristics (age, pathological stage N, and pathological stage M) can independently predict the prognosis of patients with BC. The Nom chart is most suitable for assessing the long-term survival rate of BC patients. The results of GSEA demonstrated that numerous cell cycle-related pathways were abundant in the high-risk group.ConclusionWe constructed an apoptosis-associated gene signature in BC, which had a potential clinical application prospect for BC patients.

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The Apoptosis Paradox in Cancer

Cited by 168 publications

References 134 publications

Current insights into the regulation of programmed cell death by TP53 mutation in cancer

Current insights into the regulation of programmed cell death by TP53 mutation in cancer

Novel amino acid metabolism‐related gene signature to predict prognosis in clear cell renal cell carcinoma

A Signature of Three Apoptosis-Related Genes Predicts Overall Survival in Breast Cancer

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