Ingenious conception - iron death regulatory factor classification and prognosis 6+

Introduction

Iron death is a pattern of cell death , It plays a key role in tumorigenesis and progression .

Background introduction

Iron death in recent years + Cancer research has been published , However, iron death chunshengxin articles with novel entry points can still get high scores ！ Today, I'd like to bring you an article on iron death regulators as signature Articles on prognosis classification , Published in real-time impact factor 6.58 Of International Journal of Biological Sciences On . titled Integrative analysis of the molecular mechanisms, immunological features and immunotherapy response of ferroptosis regulators across 33 cancer types.

Data is introduced

from KEGG The iron death pathway identified in the database 41 A gene . Use FerrDb The database identifies the attributes of the identified genes and displays them in PubMed and Google Manually search the academic database for their function in iron death .30 Four known genes are divided into two groups ： Iron death drivers or inhibitors .

33 The gene expression information and related clinical data of three kinds of cancer are from TCGA download . Data for the immunotherapy cohort were obtained from cBioPortal database （http://www.cbioportal.org/） download . from Cistrome A total of... Were obtained from the cancer database 318 Tumor associated transcription factors （TFs）（http://cistrome.org/CistromeCancer/) .

Result analysis

01

To identify the prognosis of iron death regulators in Pan cancer

GPX4,SLC7A11 and GSS Isogenes are highly expressed in some cancer types , Other genes （ Such as ACSL1 and ACSL4） Low expression （ chart 1B-C）. especially GPX4 stay 13 It is significantly overexpressed in four cancers . Besides , stay 33 Risk ratios for each regulator were determined for each cancer type , We found that most regulatory factors were associated with patient survival .TFRC（ Iron death drivers ） yes 9 A risk factor for cancer .GPX4 yes BRCA,CESC,THCA and UCEC Protection factor for .

chart 1

To further elucidate the transcriptome characteristics of iron death regulators , The author carried out PPI Network and correlation heat map analysis （ chart 1D-E）. Among the identified genes ,SLC7A11 Expression and GCLC The expression shows a strong relationship . protein - Protein protein interaction in 30 There are three kinds of iron death regulators , especially SLC7A11 and HMOX1（ Drivers ） as well as MAP1,LC3B and TFRC（ inhibitory factor ）. These results suggest that , There are differences in the expression of iron death regulators in cancer , There is a difference between the expression pattern and the prognosis of patients .

02

Iron death regulators are drugs that can be targeted to treat tumors

Author use Cistrome The database predicts transcription factors directly upstream of iron death regulators .Sankey The picture shows , Some iron death regulators are significantly associated with upstream transcription factors （ chart 1F）. The authors also explored the relationship between iron death and regulatory factors RNA Compound expression related therapeutic drugs （DTP NCI-60） Average activity z score , And predicted 36 A potential iron death drug （ Including cyclophosphamide ）, It is proved that the expression level of iron apoptosis related genes is related to 36 A link between drugs that predict iron apoptosis （ chart 2).

chart 2

Cyclophosphamide is an immunosuppressant and anticancer drug , It selectively targets cancer cells and is widely used as chemotherapy drugs . chart 3A The three-dimensional structure of cyclophosphamide is shown , chart 3B Cyclophosphamide and GPX4 Molecular docking sites of proteins .GPX4 Location 99 Lysine and 100 Isoleucine at position can form a non covalent bond with cyclophosphamide （ chart 3B）.GPX4 Docking with cyclophosphamide at Autodock Vina The binding energy obtained on is -3.91 kcal/mol. Because cyclophosphamide can be mixed with GPX4 Protein binding , So it is a potential iron death inducer .

chart 3

For this 29 Target for GO and KEGG Enrichment analysis （ chart 3D-E）. Most of these genes are enriched in the category of biological processes , especially “ Steady state process ”,“NF-kB Signal path ” and “JAK-STAT Signal path ”（ chart 3D-E）. Sum up , These upstream transcription factors and iron death regulators are therapeutic drugs , Especially cyclophosphamide , It can be regarded as an iron death inducer .

03

stay erastin Treatment and GPX4 Exploring the mechanism of iron death in knockdown cohort

Erastin It is an inducer of iron death , After treatment , If the iron death regulator in hepatoma cells is up-regulated or down regulated , The biological process of these iron death regulators can be determined . The author first studies whether or not erastin Treated GSE104462 Differentially expressed genes in the cohort (DEGs)（ chart 4A）, And carried on GO Functional enrichment analysis and KEGG Path analysis . Results show . access （IL-1 Mediated signaling ） And biological processes （IL- 12,DNA Replication and apoptosis ） stay erastin Highly enriched in the treatment group （ chart 4B-C）.

chart 4

To further explore the molecular mechanism of iron death , The author also used GSE147625 queue （ Wild type and GPX4 Knock down the trophoblast ）.GSE147625 The differentially expressed genes in the cohort are enriched in the biological process of angiogenesis and protein kinase regulator activity （ chart 5B-C）.GSVA The analysis shows that ,G2/M Checkpoints and TNF-α Wait for the signal path to pass NF- kB There is a great difference in the channel scores of , It shows that they are positively correlated with iron death .

chart 5

To further understand how iron death regulators affect cancer , The relationship between iron death regulators and 50 The relationship between cancer marker related pathways ( chart 6A). Results show TP53、FTL、SLC40A1 And inflammatory reaction and IL2/STAT5 Signal pathways are positively correlated . by comparison ,ALOX15、TP53 and SLC40A1 And MYC Target and DNA The repair pathway is negatively correlated ( chart 6A). Sum up , Iron death regulators are closely related to cancer signaling pathways .

chart 6

04

Association between iron death and immunogenic characteristics of cancer

First , The authors evaluated the relationship between iron death regulators and cancer stroma and immune scores . Use ESTIMATE The algorithm can be used to predict the proportion of stromal cells and immune cells infiltrating tumor tissue . The author has calculated 33 Immune scores for cancer types 、 Matrix score 、ESTIMATE Score and tumor purity , The scores and 30 Correlation of gene expression （ chart 6B). HMOX1、FTL、SAT1 and ATG7 The level of gene expression is highly correlated with the above indicators in each cancer data set （ chart 6B）.

then , The author passes CIBERSORT Regulation of expression levels and immune cell infiltration levels determine the relationship between iron death （ chart 6C,E）.GPX4, And macrophages （M1 and M2） Significant positive correlation , Activate mast cells and resting memory CD4+ T cells , And M0 Macrophages 、 Resting mast cells 、 Follicle T Cellular and regulatory T cells （Tregs） There is a negative correlation （ chart 6C).SAT1 and TFRC Respectively with CD8+ T Cells and CD4+ memory T There is a strong positive correlation between cells （ chart 2E）.

And then , Studied 47 The interaction between the expression of four common immune checkpoint genes and the level of iron death regulators （ chart 6D-F）. for example ,HMOX1 Expression and PDCD1,LG2 and CD86 The level of expression is closely and positively correlated （ chart 6F）. These results confirm that iron death regulators play a key role in tumor immunity .

05

Construct a risk scoring model for iron death prognosis

To determine whether iron death regulators contribute to clinical risk prediction , First, use genome variation analysis (GSVA) Calculate the iron death score for each genome . except TP53、GPX4 and GSS And so on , Most iron death regulators showed good positive correlation with iron death scores of different cancer types （ chart 7A）. take TCGA11000 Iron death scores of multiple cancer samples were compared with normal tissues , It was found that the overall state of iron death in tumor samples was down regulated （ chart 7C）.

Besides , The authors are in three data sets GSE121689、GSE31060 and GSE104462 The changes of iron death scores of cancer samples treated with iron death promoters and inhibitors were verified in （ chart 7E-G). It turns out that , In both types of iron death stimulants erastin And sorafenib , Iron death score increased , But using iron death inhibitors ferrostatin-1 Down regulation , It is consistent with the activation or inhibition of iron death in cancer cells . Combined with the above , The iron death score can effectively represent the iron death status of cancer cells .

chart 7

Next , The authors analyzed the clinical significance of iron death score in cancer . Use X-tile The software divides the samples into the following two groups ： High iron death score group and low iron death score group . A higher iron death score indicates KIRP、LAML、THCA、THYM、UCS and UVM The survival rate of patients is poor , but SKCM The survival rate of patients is higher （ chart 8A）. adopt K-M The curve analyzed the overall survival time of patients , except SKCM Outside the patient , The prognosis of patients with high iron death score was significantly worse than that of patients with low iron death score （ chart 8C）.

The authors tested the independent prognostic power of iron death score among cancers , Also on THCA The queue was subsequently analyzed .THCA Two groups in the queue THCA Risk ratio of stratified patients and time-dependent clustering ROC The analysis supports the preliminary finding that iron death score may be a prognostic factor （ chart 8B）.2 year 、4 year 、6 Years and 8 Area under the curve of annual survival rate （AUC） Respectively 0.64、0.79、0.67 and 0.67（ chart 8D-E）.

chart 8

Last , To determine whether the expression of iron death regulators correlates with clinical features , Find out SLC3A2、VDAC2 and SLC7A11 The expression of follows TNM The number of installments continues to increase , and SLC40A1 Decreased expression of （ chart 8F）. Besides ,FTH1 and LPLCAT3 The expression of the protein increased continuously with the pathological grade .

The authors also assessed their association with iron death scores for various cancer types , To explore whether iron death regulators can provide new insights into the effectiveness of immunotherapy （ chart 8G-I）.MSI And THCA and SKCM Iron death of cells is closely related , and TMB With iron death UCEC and TGCT Cells are closely related （ chart 8G and H）. Immune checkpoint gene expression also showed a general correlation with iron death scores （ chart 8I). Sum up , Clinical relevance , In particular, the iron death score, which is closely related to immunogenic characteristics, can be used as an independent prognostic indicator of cancer related to immune response .

Editor's summary

This paper investigated the role of iron death regulator in Pan cancer , And build the iron death risk scoring model . The authors also added several related to the treatment of iron death GEO Queue as external validation set , Enrich the content of the article , It's worth learning .