Expression and Prognostic Characteristics of Ferroptosis-Related Genes in Colon Cancer

A gene signature related to programmed cell death to predict immunotherapy response and prognosis in colon adenocarcinoma

Background

Tumor development involves the critical role of programmed cell death (PCD), but the correlation between colon adenocarcinoma (COAD) and PCD-related genes is not clear.

Methods

Subtyping analysis of COAD was performed by consensus clustering based on The Cancer Genome Atlas (TCGA), with the AC-ICAM queue from the cBioportal database as a validation set. Immune infiltration of the samples was evaluated using CIBERSORT and Microenvironment Cell Populations (MCP)-counter algorithms. Patients' immunotherapy response was predicted by the TIDE and aneuploidy scores. Pathway enrichment analysis was conducted with gene set enrichment analysis (GSEA). A RiskScore model was established with independent prognostic PCD-related genes filtered by Cox regression analysis. The mafCompare function was used to compare the differences in mutation rates of somatic genes. Wound healing, transwell assays and Flow cytometer were applied to measure the cell migration, invasion and apoptosis.

Results

The patients were grouped into S1 and S2 subtypes based on a total of 21 PCD genes associated with the prognostic outcomes of COAD. Specifically, patients of S1 subtype were mainly related to the pathway activation in tumor invasion and deterioration and had a worse prognosis. A RiskScore model was established based on six prognostic genes, including two protective genes (ATOH1, ZG16) and four risk genes (HSPA1A, SEMA4C, CDKN2A, ARHGAP4). Notably, silencing of CDKN2A inhibited the activity of migration and invasion and promoted apoptosis of tumor cells. Based on the RiskScore model, the patients were grouped into high- and low-risk groups. Independent prognostic factors, namely, Age, pathologic_M, pathologic_stage, and RiskScore, were integrated to develop a nomogram with strong good prediction performance. High-risk group had high-expressed immune checkpoint genes and higher TIDE scores, showing a strong immune escape ability and less active immunotherapy response. Compared to the low-risk group, TP53 exhibited a higher rate of somatic mutation in the high-risk group.

Conclusion

We constructed a RiskScore model with six PCD-related genes for the prognostic assessment of COAD, providing a valuable insight into the exploration of new targets for the prognostic improvement in COAD.

Ferroptosis: A novel therapeutic target of natural products against doxorubicin-induced cardiotoxicity

Doxorubicin (DOX), a commonly used chemotherapy drug, is hindered due to its tendency to induce cardiotoxicity (DIC). Ferroptosis, a novel mode of programmed cell death, has received substantial attention for its involvement in DIC. Recently, natural product-derived ferroptosis regulator emerged as a potential strategy for treating DIC. In this review, a comprehensive search was conducted across PubMed, Web of Science, Google Scholar, and ScienceDirect databases to gather relevant articles on the use of natural products for treating DIC in relation to ferroptosis. The available papers were carefully reviewed to summarize the therapeutic effects and underlying mechanisms of natural products in modulating ferroptosis for DIC treatment. It was found that ferroptosis plays an important role in DIC pathogenesis, with dysregulated expression of ferroptosis-related proteins strongly implicated in the condition. Natural products, such as flavonoids, polyphenols, terpenoids, and quinones can act as GPX4 activators, Nrf2 agonists, and lipid peroxidation inhibitors, thereby enhancing cell viability, attenuating myocardial fibrosis, improving cardiac function, and suppressing ferroptosis in both in vitro and in vivo models of DIC. This review demonstrates a strong correlation between DOX-induced cardiac ferroptosis and key proteins, such as GPX4, Keap1, Nrf2, AMPK, and HMOX1. Natural products are likely to exert therapeutic effects against DIC by modulating the activity of these proteins.

Ferroptosis: An important mechanism of disease mediated by the gut-liver-brain axis

AIMS

As a unique iron-dependent non-apoptotic cell death, Ferroptosis is involved in the pathogenesis and development of many human diseases and has become a research hotspot in recent years. However, the regulatory role of ferroptosis in the gut-liver-brain axis has not been elucidated. This paper summarizes the regulatory role of ferroptosis and provides theoretical basis for related research.

MATERIALS AND METHODS

We searched PubMed, CNKI and Wed of Science databases on ferroptosis mediated gut-liver-brain axis diseases, summarized the regulatory role of ferroptosis on organ axis, and explained the adverse effects of related regulatory effects on various diseases.

KEY FINDINGS

According to our summary, the main way in which ferroptosis mediates the gut-liver-brain axis is oxidative stress, and the key cross-talk of ferroptosis affecting signaling pathway network is Nrf2/HO-1. However, there were no specific marker between different organ axes mediate by ferroptosis.

SIGNIFICANCE

Our study illustrates the main ways and key cross-talk of ferroptosis mediating the gut-liver-brain axis, providing a basis for future research.

Cuproptosis-Related Gene Signature Contributes to Prognostic Prediction and Immunosuppression in Multiple Myeloma

Cuproptosis is a type of programmed cell death triggered by accumulation of intracellular copper which was considered closely related to tumor progression. The study of cuproptosis in multiple myeloma (MM) is however limited. To determine the prognostic significance of cuproptosis-related gene signature in MM, we interrogated gene expression and overall survival with other available clinical variables from public datasets. Four cuproptosis-related genes were included to establish a prognostic survival model by least absolute shrinkage and selection operator (LASSO) Cox regression analysis, which showed a good performance on prognosis prediction in both training and validation cohorts. Patients with higher cuproptosis-related risk score (CRRS) exhibited worse prognosis compared with lower risk score. Survival prediction capacity and clinical benefit were elevated after integrating CRRS to existing prognostic stratification system (International Staging System, ISS or Revised International Staging System, RISS) both on 3-year and 5-year survival. Based on CRRS groups, functional enrichment analysis and immune infiltration in bone marrow microenvironment revealed correlation between CRRS and immunosuppression. In conclusion, our study found that cuproptosis-related gene signature is an independent poor prognostic factor and functions negatively on immune microenvironment, which provides another perspective on prognosis assessment and immunotherapy strategy in MM.

A novel investigation into an E2F transcription factor-related prognostic model with seven signatures for colon cancer patients

The pathogenesis of colon cancer, a common gastrointestinal tumour, involves complicated factors, especially a series of cell cycle-related genes. E2F transcription factors during the cell cycle play an essential role in the occurrence of colon cancer. It is meaningful to establish an efficient prognostic model of colon cancer targeting cellular E2F-associated genes. This has not been reported previously. The authors first aimed to explore the links of E2F genes with the clinical outcomes of colon cancer patients by integrating data from the TCGA-COAD (n = 521), GSE17536 (n = 177) and GSE39582 (n = 585) cohorts. The Cox regression and Lasso modelling approach to identify a novel colon cancer prognostic model involving several hub genes (CDKN2A, GSPT1, PNN, POLD3, PPP1R8, PTTG1 and RFC1) were utilised. Moreover, an E2F-related nomogram that efficiently predicted the survival rates of colon cancer patients was created. Additionally, the authors first identified two E2F tumour clusters, which showed distinct prognostic features. Interestingly, the potential links of E2F-based classification and 'protein secretion' issues of multiorgans and tumour infiltration of 'T-cell regulatory (Tregs)' and 'CD56dim natural killer cell' were detected. The authors' findings are of potential clinical significance for the prognosis assessment and mechanistic exploration of colon cancer.

Autophagy mediates an amplification loop during ferroptosis

Ferroptosis, a programmed cell death, has been identified and associated with cancer and various other diseases. Ferroptosis is defined as a reactive oxygen species (ROS)-dependent cell death related to iron accumulation and lipid peroxidation, which is different from apoptosis, necrosis, autophagy, and other forms of cell death. However, accumulating evidence has revealed a link between autophagy and ferroptosis at the molecular level and has suggested that autophagy is involved in regulating the accumulation of iron-dependent lipid peroxidation and ROS during ferroptosis. Understanding the roles and pathophysiological processes of autophagy during ferroptosis may provide effective strategies for the treatment of ferroptosis-related diseases. In this review, we summarize the current knowledge regarding the regulatory mechanisms underlying ferroptosis, including iron and lipid metabolism, and its association with the autophagy pathway. In addition, we discuss the contribution of autophagy to ferroptosis and elucidate the role of autophagy as a ferroptosis enhancer during ROS-dependent ferroptosis.

Establishment and validation of a ferroptosis-related signature predicting prognosis and immunotherapy effect in colon cancer

Background

Ferroptosis, a novel form of regulating cell death, is related to various cancers. However, the role of ferroptosis-related genes (FRGs) on the occurrence and development of colon cancer (CC) needs to be further elucidated.

Method

CC transcriptomic and clinical data were downloaded from TCGA and GEO databases. The FRGs were obtained from the FerrDb database. The consensus clustering was performed to identify the best clusters. Then, the entire cohort was randomly divided into the training and testing cohorts. Univariate Cox, LASSO regression and multivariate Cox analyses were used to construct a novel risk model in training cohort. The testing and the merged cohorts were performed to validate the model. Moreover, CIBERSORT algorithm analyze TIME between high- and low- risk groups. The immunotherapy effect was evaluated by analyzing the TIDE score and IPS between high- and low- risk groups. Lastly, RT-qPCR were performed to analyze the expression of the three prognostic genes, and the 2-years OS and DFS between the high- and low- risk groups of 43 clinical CC samples to further validate the value of the risk model.

Results

SLC2A3, CDKN2A, and FABP4 were identified to construct a prognostic signature. Kaplan-Meier survival curves showed that OS between the high- and low-risk groups were statistically significant (p_merged<0.001, p_training<0.001, p_testing<0.001). TIDE score and IPS were higher in the high-risk group (p_TIDE<0.005, p_Dysfunction<0.005, p_Exclusion<0.001, p_mAb-CTLA-4 = 3e-08, p_mAb-PD-1 = 4.1e-10). The clinical samples were divided into high- and low- risk groups according to the risk score. There was a statistical difference in DFS (p=0.0108).

Conclusion

This study established a novel prognostic signature and provided more insight into the immunotherapy effect of CC.

Seven bacterial response-related genes are biomarkers for colon cancer

BACKGROUND

Colon cancer (CC) is a common tumor that causes significant harm to human health. Bacteria play a vital role in cancer biology, particularly the biology of CC. Genes related to bacterial response were seldom used to construct prognosis models. We constructed a bacterial response-related risk model based on three Molecular Signatures Database gene sets to explore new markers for predicting CC prognosis.

METHODS

The Cancer Genome Atlas (TCGA) colon adenocarcinoma samples were used as the training set, and Gene Expression Omnibus (GEO) databases were used as the test set. Differentially expressed bacterial response-related genes were identified for prognostic gene selection. Univariate Cox regression analysis, least absolute shrinkage and selection operator-penalized Cox regression analysis, and multivariate Cox regression analysis were performed to construct a prognostic risk model. The individual diagnostic effects of genes in the prognostic model were also evaluated. Moreover, differentially expressed long noncoding RNAs (lncRNAs) were identified. Finally, the expression of these genes was validated using quantitative polymerase chain reaction (qPCR) in cell lines and tissues.

RESULTS

A prognostic signature was constructed based on seven bacterial response genes: LGALS4, RORC, DDIT3, NSUN5, RBCK1, RGL2, and SERPINE1. Patients were assigned a risk score based on the prognostic model, and patients in the TCGA cohort with a high risk score had a poorer prognosis than those with a low risk score; a similar finding was observed in the GEO cohort. These seven prognostic model genes were also independent diagnostic factors. Finally, qPCR validated the differential expression of the seven model genes and two coexpressed lncRNAs (C6orf223 and SLC12A9-AS1) in 27 pairs of CC and normal tissues. Differential expression of LGALS4 and NSUN5 was also verified in cell lines (FHC, COLO320DM, SW480).

CONCLUSIONS

We created a seven-gene bacterial response-related gene signature that can accurately predict the outcomes of patients with CC. This model can provide valuable insights for personalized treatment.

Role of ferroptosis in colorectal cancer

Colorectal cancer (CRC) is the second deadliest cancer and the third-most common malignancy in the world. Surgery, chemotherapy, and targeted therapy have been widely used to treat CRC, but some patients still develop resistance to these treatments. Ferroptosis is a novel non-apoptotic form of cell death. It is an iron-dependent non-apoptotic cell death characterized by the accumulation of lipid reactive oxygen species and has been suggested to play a role in reversing resistance to anticancer drugs. This review summarizes recent advances in the prognostic role of ferroptosis in CRC and the mechanism of action in CRC.

Identification and validation of a novel ferroptosis-related gene signature for prognosis and potential therapeutic target prediction in cholangiocarcinoma

Cholangiocarcinoma (CCA) is a highly heterogeneous and aggressive malignancy of the bile ducts with a poor prognosis and high mortality rate. Effective targeted therapy and accurate prognostic biomarkers are still lacking. Ferroptosis is a form of regulated cell death implicated in cancer progression and has emerged as a potential therapeutic target in various cancers. However, a comprehensive analysis of ferroptosis-related genes (FRGs) for predicting CCA prognosis and therapeutic targets and determining the role of ferroptosis in CCA remain to be performed. Here, we developed a prognostic FRG signature using a least absolute shrinkage and selection operator Cox regression analysis in a training cohort. We then validated it using four independent public datasets. The six-FRG signature was developed to predict CCA patient survival, stratifying them into low-risk and high-risk groups based on survival time. Significantly, the high-risk CCA patients had shorter overall survival. A receiver operating characteristic curve analysis further confirmed the prognostic FRG signature's strong predictive ability, indicating that it was an independent prognostic indicator for CCA patients. Furthermore, the high-risk group was associated with fluke infection and high clinical stages. Cancer-associated fibroblast (CAF) score and CAF markers were significantly higher in the high-risk group than the low-risk group. Moreover, our FRG signature could predict immune checkpoint markers for immunotherapy and drug sensitivity. The mRNA expression levels of the six-FRG signature was validated in 10 CCA cell lines and dividing them into low-risk and high-risk groups using the FRG signature. We further showed that high-risk CCA cell lines were more resistant to ferroptosis inducers, including erastin and RSL3, than the low-risk CCA cell lines. Our study constructed a novel FRG signature model to predict CCA prognoses which might provide prognostic biomarkers and potential therapeutic targets for CCA patients. Ferroptosis sensitivity in high-risk and low-risk CCA cell lines suggests that ferroptosis resistance is associated with high-risk group CCA. Therefore, ferroptosis could be a promising therapeutic target for precision therapy in CCA patients.

Insights on Ferroptosis and Colorectal Cancer: Progress and Updates

Patients with advanced-stage or treatment-resistant colorectal cancer (CRC) benefit less from traditional therapies; hence, new therapeutic strategies may help improve the treatment response and prognosis of these patients. Ferroptosis is an iron-dependent type of regulated cell death characterized by the accumulation of lipid reactive oxygen species (ROS), distinct from other types of regulated cell death. CRC cells, especially those with drug-resistant properties, are characterized by high iron levels and ROS. This indicates that the induction of ferroptosis in these cells may become a new therapeutic approach for CRC, particularly for eradicating CRC resistant to traditional therapies. Recent studies have demonstrated the mechanisms and pathways that trigger or inhibit ferroptosis in CRC, and many regulatory molecules and pathways have been identified. Here, we review the current research progress on the mechanism of ferroptosis, new molecules that mediate ferroptosis, including coding and non-coding RNA; novel inducers and inhibitors of ferroptosis, which are mainly small-molecule compounds; and newly designed nanoparticles that increase the sensitivity of cells to ferroptosis. Finally, the gene signatures and clusters that have predictive value on CRC are summarized.

Multiple machine learning methods aided virtual screening of NaV 1.5 inhibitors

Na_v 1.5 sodium channels contribute to the generation of the rapid upstroke of the myocardial action potential and thereby play a central role in the excitability of myocardial cells. At present, the patch clamp method is the gold standard for ion channel inhibitor screening. However, this method has disadvantages such as high technical difficulty, high cost and low speed. In this study, novel machine learning models to screen chemical blockers were developed to overcome the above shortage. The data from the ChEMBL Database were employed to establish the machine learning models. Firstly, six molecular fingerprints together with five machine learning algorithms were used to develop 30 classification models to predict effective inhibitors. A validation and a test set were used to evaluate the performance of the models. Subsequently, the privileged substructures tightly associated with the inhibition of the Na_v 1.5 ion channel were extracted using the bioalerts Python package. In the validation set, the RF-Graph model performed best. Similarly, RF-Graph produced the best result in the test set in which the Prediction Accuracy (Q) was 0.9309 and Matthew's correlation coefficient was 0.8627, further indicating the model had high classification ability. The results of the privileged substructures indicated Sulfa structures and fragments with large Steric hindrance tend to block Na_v 1.5. In the unsupervised learning task of identifying sulfa drugs, MACCS and Graph fingerprints had good results. In summary, effective machine learning models have been constructed which help to screen potential inhibitors of the Na_v 1.5 ion channel and key privileged substructures with high affinity were also extracted.

Identification of cuproptosis-related subtypes, construction of a prognosis model, and tumor microenvironment landscape in gastric cancer

Introduction

Cuproptosis is a novel identified regulated cell death (RCD), which is correlated with the development, treatment response and prognosis of cancer. However, the potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of gastric cancer (GC) remains unknown.

Methods

Transcriptome profiling, somatic mutation, somatic copy number alteration and clinical data of GC samples were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database to describe the alterations of CRGs from genetic and transcriptional fields. Differential, survival and univariate cox regression analyses of CRGs were carried out to investigate the role of CRGs in GC. Cuproptosis molecular subtypes were identified by using consensus unsupervised clustering analysis based on the expression profiles of CRGs, and further analyzed by GO and KEGG gene set variation analyses (GSVA). Genes in distinct molecular subtypes were also analyzed by GO and KEGG gene enrichment analyses (GSEA). Differentially expressed genes (DEGs) were screened out from distinct molecular subtypes and further analyzed by GO enrichment analysis and univariate cox regression analysis. Consensus clustering analysis of prognostic DEGs was performed to identify genomic subtypes. Next, patients were randomly categorized into the training and testing group at a ratio of 1:1. CRG Risk scoring system was constructed through logistic least absolute shrinkage and selection operator (LASSO) cox regression analysis, univariate and multivariate cox analyses in the training group and validated in the testing and combined groups. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to evaluate the expression of key Risk scoring genes. Sensitivity and specificity of Risk scoring system were examined by using receiver operating characteristic (ROC) curves. pRRophetic package in R was used to investigate the therapeutic effects of drugs in high- and low- risk score group. Finally, the nomogram scoring system was developed to predict patients' survival through incorporating the clinicopathological features and CRG Risk score.

Results

Most CRGs were up-regulated in tumor tissues and showed a relatively high mutation frequency. Survival and univariate cox regression analysis revealed that LIAS and FDX1 were significantly associated with GC patients' survival. After consensus unsupervised clustering analysis, GC patients were classified into two cuproptosis molecular subtypes, which were significantly associated with clinical features (gender, age, grade and TNM stage), prognosis, metabolic related pathways and immune cell infiltration in TME of GC. GO enrichment analyses of 84 DEGs, obtained from distinct molecular subtypes, revealed that DEGs primarily enriched in the regulation of metabolism and intracellular/extracellular structure in GC. Univariate cox regression analysis of 84 DEGs further screened out 32 prognostic DEGs. According to the expression profiles of 32 prognostic DEGs, patients were re-classified into two gene subtypes, which were significantly associated with patients' age, grade, T and N stage, and survival of patients. Nest, the Risk score system was constructed with moderate sensitivity and specificity. A high CRG Risk score, characterized by decreased microsatellite instability-high (MSI-H), tumor mutation burden (TMB) and cancer stem cell (CSC) index, and high stromal and immune score in TME, indicated poor survival. Four of five key Risk scoring genes expression were dysregulated in tumor compared with normal samples. Moreover, CRG Risk score was greatly related with sensitivity of multiple drugs. Finally, we established a highly accurate nomogram for promoting the clinical applicability of the CRG Risk scoring system.

Discussion

Our comprehensive analysis of CRGs in GC demonstrated their potential roles in TME, clinicopathological features, and prognosis. These findings may improve our understanding of CRGs in GC and provide new perceptions for doctors to predict prognosis and develop more effective and personalized therapy strategies.

Integration of Bulk and Single-Cell RNA-Seq Data to Construct a Prognostic Model of Membrane Tension-Related Genes for Colon Cancer

Background: The plasma membrane provides a highly dynamic barrier for cancer cells to interact with their surrounding microenvironment. Membrane tension, a pivotal physical property of the plasma membrane, has attracted widespread attention since it plays a role in the progression of various cancers. This study aimed to identify a prognostic signature in colon cancer from membrane tension-related genes (MTRGs) and explore its implications for the disease. Methods: Bulk RNA-seq data were obtained from The Cancer Genome Atlas (TCGA) database, and then applied to the differentially expressed gene analysis. By implementing a univariate Cox regression and a LASSO-Cox regression, we developed a prognostic model based on four MTRGs. The prognostic efficacy of this model was evaluated in combination with a Kaplan–Meier analysis and receiver operating characteristic (ROC) curve analysis. Moreover, the relationships between the signature and immune cell infiltration, immune status, and somatic mutation were further explored. Lastly, by utilizing single-cell RNA-seq data, cell type annotation, pseudo-time analysis, drug sensitivity, and molecular docking were implemented. Results: We constructed a 4-MTRG signature. The risk score derived from the model was further validated as an independent variable for survival prediction. Two risk groups were divided based on the risk score calculated by the 4-MTRG signature. In addition, we observed a significant difference in immune cell infiltration, such as subsets of CD4 T cells and macrophages, between the high- and low-risk groups. Moreover, in the pseudo-time analysis, TIMP1 was found to be more highly expressed with the progression of time. Finally, three small molecule drugs, elesclomol, shikonin, and bryostatin-1, exhibited a binding potential to TIMP-1. Conclusions: The novel 4-MTRG signature is a promising biomarker in predicting clinical outcomes for colon cancer patients, and TIMP1, a member of the signature, may be a sensitive regulator of the progression of colon cancer.

A novel m7G-related lncRNA risk model for predicting prognosis and evaluating the tumor immune microenvironment in colon carcinoma

N7-Methylguanosine (m7G) modifications are a common type of posttranscriptional RNA modifications. Its function in the tumor microenvironment (TME) has garnered widespread focus in the past few years. Long non-coding RNAs (lncRNAs) played an essential part in tumor development and are closely associated with the tumor immune microenvironment. In this study, we employed a comprehensive bioinformatics approach to develop an m7G-associated lncRNA prognostic model based on the colon adenocarcinoma (COAD) database from The Cancer Genome Atlas (TCGA) database. Pearson’s correlation analysis was performed to identify m7G-related lncRNAs. Differential gene expression analysis was used to screen lncRNAs. Then, we gained 88 differentially expressed m7G-related lncRNAs. Univariate Cox analysis and Lasso regression analysis were performed to build an eight-m7G-related-lncRNA (ELFN1-AS1, GABPB1-AS1, SNHG7, GS1-124K5.4, ZEB1-AS1, PCAT6, C1RL-AS1, MCM3AP-AS1) risk model. Consensus clustering analysis was applied to identify the m7G-related lncRNA subtypes. We also verified the risk prediction effect of a gene signature in the GSE17536 test set (177 patients). A nomogram was constructed to predict overall survival rates. Furthermore, we analyzed differentially expressed genes (DEGs) between high-risk and low-risk groups. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted with the analyzed DEGs. At last, single-sample gene set enrichment analysis (ssGSEA), CIBERSORT, MCP-COUNTER, and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithms were utilized to discover the relationship between the risk model and the TME. Consequently, the m7G-related lncRNA risk model for COAD patients could be a viable prognostic tool and treatment target.

Non-Canonical Programmed Cell Death in Colon Cancer

Simple Summary

Non-canonical PCD is an important player in colon cancer cell suicide. It influences colon cancer in many ways, such as through tumorigenesis, treatment, and prognosis. In this review, we present the mechanism, application, and prospect of different types of non-canonical PCD in colon cancer.

Abstract

Programmed cell death (PCD) is an evolutionarily conserved process of cell suicide that is regulated by various genes and the interaction of multiple signal pathways. Non-canonical programmed cell death (PCD) represents different signaling excluding apoptosis. Colon cancer is the third most incident and the fourth most mortal worldwide. Multiple factors such as alcohol, obesity, and genetic and epigenetic alternations contribute to the carcinogenesis of colon cancer. In recent years, emerging evidence has suggested that diverse types of non-canonical programmed cell death are involved in the initiation and development of colon cancer, including mitotic catastrophe, ferroptosis, pyroptosis, necroptosis, parthanatos, oxeiptosis, NETosis, PANoptosis, and entosis. In this review, we summarized the association of different types of non-canonical PCD with tumorigenesis, progression, prevention, treatments, and prognosis of colon cancer. In addition, the prospect of drug-resistant colon cancer therapy related to non-canonical PCD, and the interaction between different types of non-canonical PCD, was systemically reviewed.

Comprehensive Analysis of Programmed Cell Death Signature in the Prognosis, Tumor Microenvironment and Drug Sensitivity in Lung Adenocarcinoma

Programmed cell death (PCD) is a process that regulates the homeostasis of cells in the body, and it plays an important role in tumor immunity. However, the expression profile and clinical characteristics of PCD-related genes remain unclear. In this study, we comprehensively analysed the PCD genes with the tumor microenvironment (TME), drug sensitivity, immunothearapy response, and evaluated their prognostic value through systematic bioinformatics methods.We identified 125 PCD-related regulatory factors, which were expressed differently in lung adenocarcinoma (LUAD) and normal lung tissues. 32 PCD related prognostic genes associated with LUAD were identified by univariate Cox analysis. 23 PCD-related gene signature was constructed, and all LUAD patients in the Cancer Genome Atlas (TCGA) dataset were stratified as low-risk or high-risk groups according to the risk score. This signature had a powerful prognostic value, which was validated in three independent data sets and clinical subtypes. Additionally, it has unique properties in TME. Further analysis showed that different risk groups have different immune cell infiltration, immune inflammation profile, immune pathways, and immune subtypes. In addition, the low-risk group had a better immunotherapy response with higher levels of multiple immune checkpoints and lower Tumor immune dysfunction and exclusion (TIDE) score, while the high-risk group was sensitive to multiple chemotherapeutic drugs because of its lower IC50. In short, this is the first model to predict the prognosis and immunological status of LUAD patients based on PCD-related genes. It may be used as a predictor of immunotherapy response to achieve customized treatment of LUAD.