Construction of prognostic model of cervical cancer based on necroptosis-related lncRNAs

Jin Cao ,Bu-Hai Wang,* ,Yi-Chen Liang ,J.Juan Gu, ,Yu-Xiang Huang

1Medical College,Yangzhou University,Yangzhou 225000,China.2Institute of Oncology,Northern Jiangsu People's Hospital,Yangzhou 225000,China.3Department of Oncology,Northern Jiangsu People's Hospital,Yangzhou 225000,China.

Abstract Background: Long non-coding RNAs (lncRNAs) can influence the necroptosis process,which is essential in malignant tumors.But no studies have looked at the predictive value of necroptosis-related lncRNAs in cervical cancer (CC).Using necroptosis-related lncRNAs,we developed a prediction signature to predict the prognosis of CC patients.Method: We gathered the RNA-seq and related clinical data for patients with CC from the Cancer Genome Atlas (TCGA) database.To identify lncRNAs linked to necroptosis,we then conducted univariate and multivariate Cox regression analyses.Co-expression network analysis,least absolute shrinkage selection operator (LASSO) regression analysis and multivariate Cox regression analyses were subsequently used to further filter necroptosis-related lncRNAs signature and construct a predictive model.Finally,we examined the medication sensitivity between the two risk groups.We performed a single-sample gene set enrichment analysis(ssGSEA) to investigate the association between the predictive signature and the tumor immune microenvironment.Result: Six necroptosis-related lncRNAs (AL021807.1,AC026803.2,AC015819.1,AC233728.1,AL158166.1,NKILA) that are independently connected to the overall survival(OS)time of CC patients make up the signature we created.For predicting the 1-,3-,and 5-year survival rates,the areas under the receiver operating characteristic (ROC) curve (AUC) were 0.72,0.791,and 0.808.It was determined that the risk score model was a separate prognostic component.The Kaplan-Meier analysis revealed that the prognosis for CC patients in the high-risk category was worse.Low-risk CC patients had more active immune systems and responded better to PD1/L1 immunotherapy.Conclusion: The signature based on necroptosis-related lncRNAs is a reliable biomarker,which is more likely to independently predict the prognosis of patients with CC and offer a foundation for the pathogenesis of necroptosis-related lncRNAs in CC.It can also be utilized to direct the tailored therapy of CC patients.

Keywords: cervical cancer;necroptosis;long noncoding RNAs;prognostic signature;the cancer genome atlas

Introduction

The fourth most frequent cancer in women and the seventh most frequent cancer overall in people is CC [1].The two most typical histological subtypes,squamous cell carcinoma and adenocarcinoma account for 70% and 25%,respectively,of people with CC [2].CC affects more than 500,000 women worldwide and accounts for more than 300,000 fatalities.The high-risk variant of the human papilloma virus (HPV) has often emerged as a significant pathogenic contributor to the formation and progression of the disease [3].CC has recently become a preventable disease thanks to more excellent knowledge of CC screening and the promotion and popularization of the HPV vaccine.However,the 5-year survival rate is still relatively poor,at only 60% [4].Surgical excision is now the standard course of treatment for patients with early-stage CC.The first-line therapy for locally progressed CC is concurrent radiation and chemotherapy based on cisplatin [5].More in-depth molecular-level research is urgently required to identify novel therapeutic targets to direct personalized clinical therapy,even though the survival rate of advanced CC is now higher than it was in the past due to the development of some new technologies and therapeutic drugs.

Necroptosis is a type of programmed necrosis that is triggered by a combination of cytokines and pattern recognition receptors (PRRs).Necroptosis is activated by multiple cytokines or PRRs,and it is regulated in a RIPK1 and RIPK3-dependent manner [6].TNF receptor superfamily,T cell receptor,interferon receptor,Toll-like receptor,cell metabolism,and genotoxic stress binding can all cause necroptosis[7].It also plays an essential role in tissue injuries such as heart,brain,or renal ischemia-reperfusion injury.In addition,necroptosis is linked to human inflammatory disorders,such as atherosclerosis,inflammatory bowel disease,neuroinflammation,and autoimmune diseases [6,8,9].Recent research has revealed that necroptosis plays an essential role in the onset and progression of cancers.Metzig et al.reported that pan-caspase inhibitors make drug-resistant colorectal cancer cells vulnerable to 5-fluorouracil by activating necroptosis[10].This study demonstrated that the novel pan-caspase inhibitor IDN-7314 coupled with 5-fluorouracil had a synergistic inhibitory effect on tumor growth compared to 5-fluorouracil alone.Han et al.reported that shikonin,a naturally occurring naphthoquinone,can induce necroptosis in MCF-7 breast cancer cells overexpressing Bcl2 or Bclxl,resulting in resistance to pro-apoptotic drugs and high resistance to a series of anticancer drugs,including an anthracycline,taxane,and vincristine [11].These findings show that necroptosis-based cancer treatment should be regarded as a novel anti-tumor strategy,bringing up a new avenue for cancer treatment.

Non-protein coding RNAs of more than 200 nucleotides in length are known as lncRNAs [12].LncRNA plays various roles in biology,including RNA attenuation,RNA splicing,protein folding,genetic control of gene expression,cell division and differentiation,and microRNA (miRNA) regulation [13].In recent studies,researchers discovered abnormal expression patterns of lncRNAs in CC cells,precancerous lesions,and CC,implying that lncRNAs may play a role in the occurrence,development,and progression of CC [14].By targeting the control of miR-1254,Duanrong Zhu et al.discovered that LncRNA ABHD11-AS1 increases the proliferation,invasion,and migration of CC cells [15].Low expression of LncRNA ZNF667-AS1 has an inhibitory influence on independent prognosis and proliferation of CC,according to L-PZhao et al.Lulu Wo et al.discovered that LncRNA HABON knockdown boosted RIPK1 and MLKL expression and phosphorylation levels in SMMC-7721 and Huh7 hepatoma cells,as well as inhibiting hypoxia-induced necroptosis in hepatocellular carcinoma cells [16,17].However,there are few studies on lncRNAs related to necroptosis in CC.

To predict the prognosis and immune infiltration in patients with CC,we created a predictive model of lncRNAs related to necroptosis using the TCGA database.In parallel,we searched for new treatment targets to forecast the prognosis of CC patients.

Materials and methods

Sample collection and screening

Obtaining of lncRNAs and necroptosis-related genes

The pearson correlation was used to determine the link between lncRNAs and the 205 genes relevant to necroptosis that we got from Genecard (https://www.genecards.org/).The screening standards were |R2|＞0.4 andP＜0.001 [18].

Difference analysis and functional enrichment analysis

To identify genes that are deferentially expressed in necroptosis,we employed the screening criteria |log 2 fold change (FC) ＞1| and a false discovery rate (FDR) ＜0.05 [16].Block diagrams,heat maps,and volcanic maps can all be created using R software’s “heatmap”and “ggpubr” packages.Then,we ran analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology(GO).GO pathway and process concentration analysis includes molecular function (function set),biological process (pathway),and cellular components (structural complex) [19].

Building a predictive signature for necroptosis-related lncRNAs in CC

We collected 609 matrix files of lncRNAs linked to necroptosis(Supplementary Table 1).A total of 50 necroptosis-related lncRNAs were found,of which 48 had positive correlations with prognosis,and 2 had negative correlations,using univariate Cox analysis to determine the lncRNAs connected to the prognosis of CC.Then,to further filter necroptosis-related lncRNAs signature and build a predictive model,Lasso regression analysis and multivariate Cox regression analysis were applied [20].The necroptosis-related prognostic signature is created using the training set,and its accuracy is tested using the testing set.The following formula was used to determine the risk score

Coef denotes the correlation between lncRNAs and patient survival in CC,andxrepresents the expression value of selected necroptosis-related lncRNAs.Each patient with CC has their risk score determined using this formula.Based on their median risk ratings,patients with CC were divided into high-risk and low-risk categories[21].

An evaluation of the prognostic value of lncRNAs related to necroptosis

The survival difference between high-risk and low-risk CC patients were then evaluated by using a survival curve,scatter plot,ROC curve,and heat map.Two forest maps and ROC curves were used to verify the predictive value of prognostic characteristics to show the results.The R software packages used in this process include“survival”,“survminer”,“timeROC”,“pheatmap”.

Construct the network diagram and Sankey diagram of mRNAs and lncRNAs

To clarify the mRNAs and lncRNAs relationship related to necroptosis in patients with CC,we used Cytoscape3.6.1 (https://cytoscape.org/)to construct the network diagram of necroptosis-related lncRNAs and mRNAs.We used the packages of “ggalluvial”,“ggplot2”,“dplyr” in R software to make Sankey diagram,to visualize the mRNAs and lncRNAs relationship related to necroptosis.

Construction of nomogram and calibration curves

We combined the CC patient’s clinical variables to construct the nomogram and calibration curves of patients with CC for 1,3,and 5 years.In this step,we used the“rms” software packages.

KEGG and GO functional enrichment analysis

We explored the corresponding biological process by performing KEGG and GO functional enrichment analyses of necroptosis-related lncRNAs using gene set enrichment analysis (GSEA)4.1.0(http://www.broad.mit.edu/gsea/).The cutoffs for statistical significance wereP＜0.05 and FDR ＜0.25 [20].

But it was all no good, and the Princess suffered so much from their remedies that the King was obliged to send out a second proclamation that anyone who undertook to cure the Princess, and who failed to do it, should be hanged up to the nearest tree

Relationship between immune-related variables and the predictive signature

Due to mounting evidence that immunological traits are crucial in the occurrence and growth of malignant tumors,we used ssGSEA to analyze the connection between risk score and immune-related factors and contrasted the immune checkpoint activation in patients with CC at low-risk and high-risk.The drug sensitivity of CC patients in the high-risk and low-risk groups was also examined [22,23].

Principal component analysis(PCA)

Finally,we tested the risk model’s ability to distinguish between CC groups with high-risk and low-risk using PCA.These procedures use the software packages “l(fā)imma” and “scatterplot3d”.

Statistical analysis

The entire statistical study was performed using R software (version 4.1.0).R software and a Perl language package were used to create the graph.The expression levels of necroptosis-related differentially expressed genes (DEGs) in malignant and non-cancerous tissues were compared using the Wilcoxon test.The relationship between genes associated with necroptosis and OS in CC patients was examined using univariate Cox regression.Searching for genes associated with necroptosis was done using multivariate Cox regression analysis and Lasso regression.Bilateral statistical tests were conducted,and the threshold for statistical significance was set atP＜0.05.

Results

Acquisition of deferentially expressed genes

We obtained RNA-seq data from 306 CC tissues and 3 paracancerous tissues from the TCGA database (Table 1).The DEGs between CC and non-tumor tissues were compared using the Wilcoxon test(|log2FC|＞1,FDR ＜0.05).Fifty-one genes associated with necroptosis were found,including 13 down-regulated genes and 38 up-regulated genes(Figure 1A-C).

Table 1 The clinical characteristics of patients in different cohorts

Enrichment analysis of DEGs related to necroptosis

We performed KEGG and GO functional enrichment analyses of DEGs related to necroptosis.KEGG pathway analysis shows that(Figure 2A)DEGs are mainly concentrated in necroptosis,influenza A,apoptosis,NOD-like receptor signaling pathway,lipid and atherosclerosis,measles,epstein-barr virus infection,salmonella infection,hepatitis B,etc.in the biological process category.According to GO analysis(Figure 2B),DEGs were primarily enriched in responses to the regulation of the extrinsic apoptotic signaling pathway,neuron death,and the regulation of neuron death in the biological process.In the category of cellular components,the DEGs were primarily enriched in the outer membranes of organelles,mitochondria,and other cellular structures.In the category of molecular function,the DEGs were primarily enriched in the binding of ubiquitin-like protein ligases,cytokine receptors,and ubiquitin-protein ligases,among other molecular functions.

Figure 1 Acquisition of differentially expressed 51 necroptosis-related genes in cervical cancer.(A)Heat map of 51 necroptosis-related genes between CC and paracancerous tissue.(B) The boxplot of 51 identified necroptosis-related genes.Orange represents CC tissues,while purple represents normal tissues,respectively.(C)Volcano plot of differentially expressed necroptosis-related genes.Orange represents up-regulated gene,blue represents the down-regulated gene,and black represents no difference between CC and normal tissues.CC,Cervix cancer.

Figure 2 Necroptosis-related DEGs in cervical cancer and surrounding tissues were analyzed using GO and KEGG.(A) KEGG analysis of necroptosis-related DEGs.(B) GO analysis of necroptosis-related DEGs.GO,Gene Ontology;KEGG,Kyoto Encyclopedia of Genes and Genomes;DEGs,differentially expressed genes;CC,Cervix cancer.

Building a predictive signature for necroptosis-related lncRNAs in CC

We obtained a total of 609 lncRNAs related to necroptosis.50 lncRNAs were connected to the prognosis of CC patients,according to a univariate Cox regression analysis.Six lncRNAs associated with necroptosis (AL021807.1,AC026803.2,AC015819.1,AC233728.1,AL158166.1,and NKILA) could be employed as a predictive signature in patients with CC,according to multivariate Cox regression and Lasso regression analysis.Among them,lncRNA AL021807.1,AC026803.2,AC015819.1,and AC233728.1 are protective factors,and lncRNA AL158166.1,NKILA is a risk factors (Figure 3A-C).Then,to display the link between LncRNAs and mRNAs,we created a network diagram of lncRNAs and mRNAs using cytoscape(Figure 3D).At the same time,we created a Sankey diagram (Figure 3E).The risk score for each CC patient was then determined based on the correlation coefficient determined by multivariate Cox regression analysis.The formula we used to determine the risk score is as follows:RiskScore=(-0.603 × AL021807.1 expression)+(-1.053 ×AC026803.2 expression)+(-1.1007 × AC233728.1 expression) +(-1.1555 × -AC015819.1 expression)+(0.719 × NKILA expression)+(0.796 ×AL158166.1 expression).

Necroptosis-related lncRNA signature and CC patients’prognosis:a correlation

First,to establish a connection between the necroptosis-related lncRNA profile and the prognosis of CC patients,we performed a Kaplan-Meier analysis to evaluate the survival durations between high-risk and low-risk CC groups.As shown in the figure (Figure 4A),the high-risk group’s OS time is significantly shorter than that of the low-risk group.The heat map,scatter map,and risk curve are all drawn simultaneously (Figure 4B-D),and it can be seen that over time,the higher the risk score,the greater the number of CC patient deaths and the worse the prognosis.After that,we ran multivariate ROC analysis and univariate and multivariate Cox regression analysis(P＜0.05) to see if the predictive signature is a reliable predictor of prognosis in CC patients (Figure 5A-D).Stage N and risk scores were both independent predictors of OS in CC patients,according to multivariate Cox regression analysis,which also demonstrated a strong correlation between the risk score and OS of CC patients.The risk score’s AUC value was 0.777,which was higher than that of clinical variables in predicting the prognosis of CC patients.The areas under the ROC curve (AUC) values of 0.72,0.791,and 0.808 for predicting 1-,3-,and 5-year survival rates,respectively,demonstrate the effectiveness of the signature in predicting the prognosis of CC.By analyzing the risk score,Kaplan-Meier analysis curve,risk distribution,survival outcome,and expression of lncRNAs related to necroptosis in the training set and the testing set using a single formula,we were able to confirm the accuracy of the signature(Figure 6A-J),the results demonstrate the accuracy of our prediction model.Additionally,the risk score model and the clinical variables were combined to create a nomogram of prognosis (Figure 7A).The calibration curve demonstrates that the actual and expected survival rates at 1,3,and 5 years have a good agreement (Figure 7B-D).

Figure 3 Establishment of the CC necroptosis-related lncRNA signature.(A) Necroptosis-related lncRNA expression levels in CC and normal tissues.(B) Using tenfold cross-validation,the optimal turning parameters (logλ) are determined.(C) The most minor absolute shrinkage and selection operator (LASSO) algorithm’s 10-fold cross-validation for variable selection.(D) Prognostic necroptosis-related lncRNA co-expression network.(E) Sankey diagram of lncRNAs associated with prognostic necroptosis.lncRNAs,long non-coding RNAs;CC,Cervix cancer.

Figure 4 The relationship between the CC patients’ prognosis and the predicting characteristic.(A) Kaplan-Meier analysis compares the OS for high-risk and low-risk CC patients.(B) The risk score distribution among CC patients.(C) The number of patients with various risk scores that are died and are alive.Orange indicates how many people died,and purple indicates how many people survived.(D) Expression heat map of six lncRNAs associated with necroptosis.lncRNAs,long non-coding RNAs;CC,Cervix cancer;OS,overall survival.

Figure 5 Evaluation of the necroptosis-related lncRNAs’ prognostic signature.(A) A univariate Cox regression analysis was performed on the clinical features and risk score.(B) The risk score and clinical characteristics are subjected to a multivariate Cox regression analysis.(C) The risk score and clinicopathological factors’ROC curves.(D)ROC curve for forecasting the survival rate at the 1-,3-and 5-year.lncRNAs,long non-coding RNAs;CC,Cervix cancer;ROC,receiver operating characteristic;T,tumor;N,lymph node;OS,overall survival.

Relationship of the CC patients’ prognosis in relation to various clinical covariates and the predictive signature

Patients with CC were separated into many groups based on clinicopathological factors in order to examine the impact of various clinical covariates on the prognosis of CC patients (Figure 8A-K).Among them,the low-risk group’s OS was much longer compared to the high-risk group when the age was less than 65 years old and grade3/4 MO,N1,and T1 stage.Other clinical groups may have no statistical significance due to the small number of cases.These findings imply that the signature of necroptosis-related lncRNAs might predict the prognosis of CC patients in a different group of age ＜65 years old,grade3/4,MO,N1,and T1 stage.

Functional analysis of necroptosis-related lncRNAs

We then performed KEGG and GO functional enrichment analysis(Figure 9A-B).The necroptosis-related lncRNAs in the KEGG analysis were primarily clustered in the Adherens junction,Autoimmune thyroid disease,Axon guidance,DNA replication,and ECM receptor interaction.In GO analysis,the necroptosis-related lncRNAs were mainly concentrated in the Glycosyl compound catabolic process,heart valve morphogenesis,and nucleoside catabolic process.

Analysis of immune system components and activity

We looked at the relationship between the risk model and immune-related components because researchers have recently discovered that immunological factors play a significant role in cancers.We analyzed immune cells and immune pathways by using ssGESA.The results showed that(Figure10A-B)aDCs,B cells,CD8+T cells,Neutrophils,NK cells,and pDCs were more expressed in the low-risk CC group.The low-risk group had higher immune function ratings than the high-risk group for APC co-inhibition,APC co-stimulation,Inflammation-promoting,T cell co-inhibition,and Type I IFN Response.This demonstrates that low-risk CC patients have more active immune systems than high-risk patients.We then analyzed the immune checkpoints (Figure 10C-D).From the picture nearly every immune checkpoint has been revealed to be more active in the low-risk CC group,including CD274 (PDL1),LAG3,PDCD1(PD1),TNFRSF18,and LGALS9,suggesting that these individuals may be more responsive to immunotherapy.

Drug sensitivity analysis

Since low-risk CC patients are more responsive to immunotherapy,we contrasted the medication sensitivity between groups at high-risk and low-risk (Figure 11A-F).The findings revealed that low-risk CC patients were more responsive to Cisplatin,Camptothecin,and Etoposide but resistant to targeted drug Axitinib,Imatinib.These results prove that investigating specialized treatment plans appropriate for CC groups with high and low risk is beneficial.

The reliability of the necroptosis-related lncRNA signature is further demonstrated by PCA

It was confirmed that there was a difference between high-risk and low-risk CC groups using PCA.The picture shows(Figure 12A-D) that the prognostic risk model can distinguish between high-risk and low-risk CC groups,further illustrating the signature’s accuracy.

Figure 6 Prognostic values of the six lncRNA signatures related to necroptosis in the train and test.The risk curve (A-B),survival scatter diagram (C-D),heat maps of 6 lncRNA expressions (E-F),Kaplan-Meier survival curves (G-H),and ROC curve of CC patients (I-J) between lowand high-risk groups in the train,test,respectively.lncRNAs,long non-coding RNAs;CC,Cervix cancer;ROC,receiver operating characteristic;AUC,area under the curve.

Figure 7 Construction of nomogram.(A) A nomogram that integrates clinicopathological factors and risk score forecasts the 1,3,and 5 years of OS of CC patients.(B-D)The calibration curves examine whether the forecasted survival rates at 1,3,and 5 years are consistent with the actual OS rates at those times.OS,overall survival;CC,Cervix cancer.

Figure 8 Risk curve under different clinicopathological variables.(A-B) Age.(C-D) Stage.(E-F) Grade.(G-H) T stage.(I-J) N stage.(K) M stage.T,tumor;N,lymph node;M,metastasis.

Figure 9 KEGG and GO enrichment analysis.(A) KEGG enrichment analysis.(B) GO enrichment analysis.GO,Gene Ontology;KEGG,Kyoto Encyclopedia of Genes and Genomes.

Figure 10 Analysis of risk score and immune-related factors.(A)Correlation analysis of immune cells.(B)Analysis of immune-related pathway.(C) CD274(PD-L1)expression in high-risk and low-risk groups.(D) The differences in the expression of common immunological checkpoints in the risk populations.ssGSEA,single-sample gene set enrichment analysis;PD-L1,programmed cell death ligand 1;＊P ＜0.05;＊＊P ＜0.01;＊＊＊P ＜0.001;ns,non-significant.

Figure 11 Drug sensitivity analysis.(A)IC50 of cisplatin in high-and low-risk populations.(B)IC50 of etoposide in high and low-risk populations.(C) IC50 of cytarabine in high and low-risk populations.(D) IC50 of axitinib in high and low-risk populations.(E) IC50 of imatinib in high and low-risk populations.(F) IC50 of camptothecin in high-risk and low-risk populations.IC50,half-maximal inhibitory concentration.

Figure 12 Principal component analysis (PCA).(A) The whole gene expression profiles.(B) Genes related to necroptosis.(C) LncRNAs are connected to necroptosis.(D) Predictive signature based on lncRNAs associated with necroptosis.lncRNAs,long non-coding RNAs.

Discussion

CC is a common malignant tumor that poses a significant health hazard to women.CC has the fourth highest morbidity and mortality rate among women globally [24].Even though the current treatment has improved survival rates significantly,CC patients with a significant number of metastases or recurrences within two years of treatment have a worse prognosis [25].As a result,new biomarkers are needed to predict the prognosis of individuals with CC.Currently,some studies have shown that lncRNAs have a robust predictive ability in cancer prognosis and diagnosis,and some researchers have used lncRNAs of other signatures to develop the prognostic model of CC.In CC cells,lncRNA SNHG14 is associated with JAK-STAT pathway activation [26-27].LncRNA HIPK1-AS has been shown to be associated with the inflammatory stage of CC [22].Yinliang Lu et al.demonstrated that lncRNAs linked to necroptosis could predict the prognosis of lung adenocarcinoma [28].However,the predictive model for CC based on lncRNAs related to necroptosis has not yet been reported.

Using TCGA database,we established a cohort of 306 CC tissues and three standard tissue samples and screened lncRNAs associated with necroptosis by constructing a co-expression network of lncRNAs and necroptosis-related genes.Lasso and Cox regression analysis were used to identify six lncRNAs linked to necroptosis (AL021807.1,AC026803.2,AC015819.1,AC233728.1,AL158166.1,NKILA).The six necroptosis-related lncRNAs could be used as prognostic signatures and therapeutic targets in CC patients.Two related lncRNAs(AL021807.1,NKILA) were reported to be associated with cancer.LncRNA AL021807.1 has been found to represent a possible prognostic signature linked to immunity and CC prognosis [29].Overexpression of lncRNA NKILA in retinoblastoma cells causes the down-regulation of XIST in retinoblastoma cells,preventing retinoblastoma formation,according to XuemanLyu et al.,Fei Tao et al.found that lncRNA NKILA inhibits rectal cancer cell proliferation,migration,and invasion by reducing NF-kB signal transduction,suggesting that it could be exploited as an anticancer lncRNA in rectal cancer [30-31].According to ShunKe et al.,lncRNA NKILA can suppress IkBαphosphorylation,p65 nuclear translocation,and down-regulate the expression of NF-kB target genes in ESCC cells,as well as limit the malignant growth of ESCC cells by blocking NF-kB signal transmission.In vitro,LncRNA NKILA suppresses esophageal squamous cell carcinoma cell proliferation and migration,tumor growth and lung metastasis in vivo [32].The four remaining lncRNAs(AC026803.2,AC015819.1,AC233728.1,and AL158166.1) linked to necroptosis are not known to play a role in cancer prognosis.More research is therefore required to determine how these lncRNAs impact the prognosis of patients with CC through necroptosis.Patients with CC were separated into low-risk and high-risk groups based on the median.The outcomes demonstrated that the risk score accurately predicted the prognosis for CC patients,and the low-risk group had a better prognosis than the high-risk group.Then,using expected outcomes as a guide,we created a nomogram to estimate the prognosis of CC patients.The risk score can predict survival.It can independently predict the prognostic risk of CC according to the areas under the ROC curve (AUC) for predicting 1-,3-,and 5-year survival rates,which were 0.72,0.791,and 0.808,respectively.The predictive signature has excellent predictive performance,according to internal verification.PCA showed that patients,according to the necroptosis-related lncRNAs classification,had prominent prognostic characteristics,which could distinguish between high-risk and low-risk groups.

We used KEGG and GO functional enrichment analysis of these necroptosis-related lncRNAs to investigate the potential biological roles of our signature.According to KEGG analysis,the necroptosis-related l ncRNAs were primarily concentrated in the Adherens junction,Autoimmune thyroid disease,Axon guidance,DNA replication,and Ecm receptor interaction.Increased SIX1 expression has been found in previous research to speed up the progression from G1 to the S phase by boosting DNA replication and promoting the proliferation and development of CC cells.SIX1 knockdown,on the contrary,may decrease DNA replication,slow the transition from G1 to S phase,and reduce tumor cell proliferation and growth[33].According to a recent study,the Ecm receptor interaction pathway can alter tumor growth,migration,differentiation,and prognosis by changing the makeup of the tumor microenvironment(TME) [34].In GO analysis,the necroptosis-related lncRNAs were mainly concentrated in the Glycosyl compound catabolic process,Heart valve morphogenesis,Hippo signaling,Homophilic cell adhesion via plasma membrane adhesion molecules,and Nucleoside catabolic process.According to research by Lijun Wang et al.,NGF suppresses the Hippo signaling pathway and activates the Yes-associated protein (YAP),which causes CC cells to proliferate and migrate [35].UM-6 induces autophagy and apoptosis in CC cells by activating the Hippo signaling pathway,promoting cytoplasmic retention and phosphorylation-dependent degradation of YAP inhibiting YAP-TEAD binding and transcriptional activity,and suppressing the expression of downstream target genes.These findings are reported by Dongying Wang et al.[36].These conclusions are helpful for us to explore the mechanism of necroptosis-related lncRNAs and guide the clinical treatment of patients with CC.

Given the tight association between necroptosis and immune-mediated pathways,we used ssGSEA to investigate the connection between immune cell subsets and related functions using TCGA-CESC data.The results show that low-risk CC patients have higher expression of ads,B cells,CD8+T cells,NK cells,and immune function scores of APC co inhibition,Checkpoint,Inflammationpromoting,T cell inhibition,Type I IFN Response was higher in the low-risk groups than in the high-risk groups,some of them are closely related to necroptosis.According to some studies,through a connection between FasL and Fas,IL-2-activated CD8+lymphocytes gain the capacity to cleave tumor cells that express this antigen and kill them,and induce necroptosis in HLA-negative cells so that they can kill tumor cells that escape apoptosis after coming into contact with tumor antigen [37].Yang Li et al.have shown that Baumanella triggers the production of type I IFN that is TRIF-dependent and then induces the expression of Zbp1,Mlk1,caspase-11,and Gsdmd genes through KAT2B-mediated and P300-mediated H3K27ac modification,resulting in NLRP3 inflammatory body activation and may contribute to GSDMD-mediated necroptosis [38].Han-Hee Park et al.examined the relationship between CD8+T cells or dendritic cells (DC) and RIPK3/TRIM28 levels in all TCGA tumors,noting that RIPK3 is necessary to govern DC cytokines and is engaged in both the innate and acquired immune systems [39].RIPK3 is also involved in activating immune ligands,which leads to necroptosis,and then activates MLKL by phosphorylation [40].These studies demonstrate that necroptosis may contribute to the development of CC by regulating tumor immunity.Finally,we performed drug sensitivity analysis and immune checkpoint expression analysis.The findings indicated that most low-risk CC patients had elevated immune checkpoint expression,indicating that patients with low-risk CC would respond better to PD-1/L1 treatment.

Meanwhile,combining immunotherapy with chemotherapy may be beneficial for low-risk CC patients.The drug sensitivity analysis revealed that they were more sensitive to the chemotherapy drugs cisplatin,camptothecin,and etoposide but resistant to the targeted drugs axitinib and imatinib.This information was crucial for clinical guidance and provided proof for an accurate and individualized treatment of CC patients.

In short,we studied the prognosis of necroptosis-related LncRNAs in CC.The six lncRNAs and their markers related to necroptosis may be molecular biomarkers and therapeutic targets for CC patients.However,The study has several limitations.In the beginning,we conducted data mining from the database and performed bioinformatics analysis through some algorithms,which still require experimental confirmation.Second,we only use the TCGA database for internal verification,which may lead to errors due to the small amount of data;therefore we require additional data sets to confirm our research.

Conclusion

The signature based on necroptosis-related lncRNAs is a reliable biomarker,which are more likely to independently predict the prognosis of patients with CC and offer a foundation for the pathogenesis of necroptosis-related lncRNAs in CC.It can also be utilized to direct the tailored therapy of CC patients.