Identification of pyroptosis-related lncRNAs for constructing a prognostic model and their correlation with immune infiltration in breast cancer

ENST00000534735 inhibits proliferation and migration, promotes apoptosis and pyroptosis of endometrial cancer via OSBPL3 through APMK/SIRT1/NF-κB pathway

Background

The complete understanding of the biological roles of long non-coding RNAs (lncRNAs) in cancer remains elusive. The findings of this study indicate that the newly discovered lncRNA ENST00000534735 exhibited a decreased expression in both endometrial cancer (EC) tissues and cell lines.

Methods

The expression of ENST00000534735 in EC tissues was detected using RNA-sequencing analysis. The effects of ENST00000534735 on cell proliferation, migration, apoptosis, and pyroptosis were determined via in vitro and in vivo experiments. The proteins that interact with ENST00000534735 were confirmed by RNA pull-down assay. Furthermore, an investigation was conducted on the impact of ENST00000534735 on the in vivo growth of EC through a tumorigenicity assay in nude mice.

Results

We found that ENST00000534735 was significantly down-regulated in EC tissues compared to their adjacent non-cancerous tissues. The ectopic expression of ENST00000534735 drastically inhibited lung cancer cell proliferation and migration ability and facilitated apoptosis and pyroptosis. Knockdown of ENST00000534735 increased OSBPL3 expression, and the tumor-suppressing effects of ENST00000534735 overexpression were reversed by upregulation of OSBPL3 via the APMK/SIRT1/NF-κB pathway. The in vivo tumorigenic assays conducted on nude mice revealed that the excessive expression of ENST00000534735 impeded the growth of EC.

Conclusions

All results elucidated the role and molecular mechanism of ENST00000534735 in the malignant development of EC. ENST00000534735, a new antioncogene in EC, may serve as a survival biomarker or therapeutic target for EC.

Comparative transcriptomic analysis of long noncoding RNAs in Leishmania-infected human macrophages

It is well established that infection with Leishmania alters the host cell's transcriptome. Since mammalian cells have multiple mechanisms to control gene expression, different molecules, such as noncoding RNAs, can be involved in this process. MicroRNAs have been extensively studied upon Leishmania infection, but whether long noncoding RNAs (lncRNAs) are also altered in macrophages is still unexplored. We performed RNA-seq from THP-1-derived macrophages infected with Leishmania amazonensis (La), L. braziliensis (Lb), and L. infantum (Li), investigating a previously unappreciated fraction of macrophage transcriptome. We found that more than 24% of the total annotated transcripts and 30% of differentially expressed (DE) RNAs in Leishmania-infected macrophage correspond to lncRNAs. LncRNAs and protein coding RNAs with altered expression are similar among macrophages infected with the Leishmania species. Still, some species-specific alterations could occur due to distinct pathophysiology in which Li infection led to a more significant number of exclusively DE RNAs. The most represented classes among DE lncRNAs were intergenic and antisense lncRNAs. We also found enrichment for immune response-related pathways in the DE protein coding RNAs, as well as putative targets of the lncRNAs. We performed a coexpression analysis to explore potential cis regulation of coding and antisense noncoding transcripts. We identified that antisense lncRNAs are similarly regulated as its neighbor protein coding genes, such as the BAALC/BAALC-AS1, BAALC/BAALC-AS2, HIF1A/HIF1A-AS1, HIF1A/HIF1A-AS3 and IRF1/IRF1-AS1 pairs, which can occur as a species-specific modulation. These findings are a novelty in the field because, to date, no study has focused on analyzing lncRNAs in Leishmania-infected macrophage. Our results suggest that lncRNAs may account for a novel mechanism by which Leishmania can control macrophage function. Further research must validate putative lncRNA targets and provide additional prospects in lncRNA function during Leishmania infection.

Identification and validation of a new pyroptosis-associated lncRNA signature to predict survival outcomes, immunological responses and drug sensitivity in patients with gastric cancer

BACKGROUND

Gastric cancer (GC) ranks fifth in prevalence among carcinomas worldwide. Both pyroptosis and long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and development of gastric cancer. Therefore, we aimed to construct a pyroptosis-associated lncRNA model to predict the outcomes of patients with gastric cancer.

METHODS

Pyroptosis-associated lncRNAs were identified through co-expression analysis. Univariate and multivariate Cox regression analyses were performed using the least absolute shrinkage and selection operator (LASSO). Prognostic values were tested through principal component analysis, a predictive nomogram, functional analysis and Kaplan‒Meier analysis. Finally, immunotherapy and drug susceptibility predictions and hub lncRNA validation were performed.

RESULTS

Using the risk model, GC individuals were classified into two groups: low-risk and high-risk groups. The prognostic signature could distinguish the different risk groups based on principal component analysis. The area under the curve and the conformance index suggested that this risk model was capable of correctly predicting GC patient outcomes. The predicted incidences of the one-, three-, and five-year overall survivals exhibited perfect conformance. Distinct changes in immunological markers were noted between the two risk groups. Finally, greater levels of appropriate chemotherapies were required in the high-risk group. AC005332.1, AC009812.4 and AP000695.1 levels were significantly increased in gastric tumor tissue compared with normal tissue.

CONCLUSIONS

We created a predictive model based on 10 pyroptosis-associated lncRNAs that could accurately predict the outcomes of GC patients and provide a promising treatment option in the future.

Noncoding RNA-mediated regulation of pyroptotic cell death in cancer

Pyroptosis is a newly discovered form of programmed cell death, which is manifested by DNA fragmentation, cell swelling, cell membrane rupture and leakage of cell contents. Previous studies have demonstrated that pyroptosis is tightly associated with the initiation and development of various cancers, whereas the molecular mechanisms underlying pyroptosis remain obscure. Noncoding RNAs (ncRNAs) are a type of heterogeneous transcripts that are broadly expressed in mammalian cells. Owing to their potency of regulating gene expression, ncRNAs play essential roles in physiological and pathological processes. NcRNAs are increasingly acknowledged as important regulators of the pyroptosis process. Importantly, the crosstalk between ncRNAs and pyroptosis affects various hallmarks of cancer, including cell growth, survival, metastasis and therapeutic resistance. The study of the involvement of pyroptosis-associated ncRNAs in cancer pathobiology has become a hot area in recent years, while there are limited reviews on this topic. Herein, we provide an overview of the complicated roles of ncRNAs, mainly including microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs), in modulating pyroptosis, with a focus on the underlying mechanisms of the ncRNA-pyroptosis axis in cancer pathogenesis. Finally, we discuss the potential applications and challenges of exploiting pyroptosis-regulating ncRNAs as molecular biomarkers and therapeutic targets in cancer.

A prognostic signature of pyroptosis-related lncRNAs verified in gastric cancer samples to predict the immunotherapy and chemotherapy drug sensitivity

Background: Pyroptosis is a recently identified mode of programmed inflammatory cell death that has remarkable implications for cancer development. lncRNAs can be involved in cellular regulation through various pathways and play a critical role in gastric cancer (GC). However, pyroptosis -related lncRNAs (PRlncRNAs) have been rarely studied in GC.

Methods: Pyroptosis-related gene were abstracted from the literature and GSEA Molecular Signatures data resource. PRlncRNAs were obtained using co-expression analysis. LASSO Cox regression assessment was employed to build a risk model. Kaplan-Meier (KM), univariate along with multivariate Cox regression analysis were adopted to verify the predictive efficiency of the risk model in terms of prognosis. qRT-PCR was adopted to validate the expression of PRlncRNAs in GC tissues. In addition, immune cell infiltration assessment and ESTIMATE score evaluation were adopted for assessing the relationship of the risk model with the tumor immune microenvironment (TME). Finally, immune checkpoint gene association analysis and chemotherapy drug sensitivity analysis were implemented to assess the worthiness of our risk model in immunotherapy and chemotherapy of GC.

Results: We identified 3 key PRlncRNAs (PVT1, CYMP-AS1 and AC017076.1) and testified the difference of their expression levels in GC tumor tissues and neighboring non-malignant tissues (p < 0.05). PRlncRNAs risk model was able to successfully estimate the prognosis of GC patients, and lower rate of survival was seen in the high-GC risk group relative to the low-GC risk group (p < 0.001). Other digestive system tumors such as pancreatic cancer further validated our risk model. There was a dramatic difference in TMB level between high-GC and low-GC risk groups (p < 0.001). Immune cell infiltration analysis and ESTIMATE score evaluation demonstrated that the risk model can be adopted as an indicator of TME status. Besides, the expressions of immunodetection site genes in different risk groups were remarkably different (CTLA-4 (r = −0.14, p = 0.010), VISTA (r = 0.15, p = 0.005), and B7-H3 (r = 0.14, p = 0.009)). PRlncRNAs risk model was able to effectively establish a connection with the sensitivity of chemotherapeutic agents.

Conclusion: The 3 PRlncRNAs identified in this study could be utilized to predict disease outcome in GC patients. It may also be a potential therapeutic target in GC therapy, including immunotherapy and chemotherapy.

Pyroptosis-related lncRNA pairs to estimate the molecular features and prognostic outcomes of pancreatic ductal adenocarcinoma

Pyroptosis is a form of programmed cell death associated with inflammatory alterations. However, the intrinsic mechanisms and underlying correlation of pyroptosis-related lncRNAs (PRLs) in pancreatic ductal adenocarcinoma (PDAC) remain unclear. The objective of the current research was to identify pyroptosis-related lncRNAs and a prognostic model to predict the prognosis of patients. We extracted pyroptosis-related lncRNAs to construct a risk model and validated them at Fudan University Shanghai Cancer Center. Crosstalk between lncRNA SNHG10 and GSDMD was found to regulate pyroptosis levels. A new algorithm was used to establish a 0 or 1 PRL pair matrix and prognostic model. Six pyroptosis-related lncRNA pairs were identified and utilized to construct a risk model. The low-risk groups exhibited better prognoses than the high-risk groups. The area under the curve (AUC) indicated extremely high accuracy, reaching 0.810 at 1 year, 0.850 at 2 years, and 0.850 at 3 years in the training set. Patients with different risk scores exhibited distinct metabolic, inflammatory, and immune microenvironments as well as tumor mutation landscapes. Additionally, 9 commonly used chemotherapeutic drugs exhibited different sensitivities between the high- and low-risk groups. To conclude, we propose that pyroptosis exhibits a close correlation with PDAC. Our risk model based on PRL pairs may be beneficial for the accurate estimation of prognostic outcomes, the immune microenvironment, and drug sensitivity, bringing therapeutic hope for patients with PDAC.

Characterization of the immune related lncRNAs in bladder cancer to aid immunotherapy

Bladder cancer (BLCA) is the 10th most common form of cancer worldwide. Currently, the response rate of BLCA patients to novel immunotherapy and immune checkpoint inhibitor (ICI) treatment is around 30% or less. Therefore, there is an urgent clinical demand to understand the regulation of immune function in BLCA patients. LncRNAs are known to play fundamental roles in the regulation of the immune system in the tumor microenvironment. In this report, we performed a comprehensive analysis to identify immune-related lncRNAs (IRLs) in BLCA patients using The Cancer Genome Atlas (TCGA) databases. BLCA patients were divided into five TME subtypes. Subtype HMIE was strongly related to survival and high anti-tumor activity of patients. Through a four-step analysis, we identified 34 IRLs as subtype HMIE related lncRNAs (HMIE-lncs).The correlation analysis with immune cell infiltration and target gene pathway enrichment showed that 34 HMIE-lncs were correlated with immune cell activation and tumor cell killing. Among them, 24 lncRNAs were related to good prognosis. We constructed a risk model to predict BLCA. Cross tumor validation was performed, and the results showed that the 34 HMIE-lncs identified in the BLCA patients in this study were highly expressed in the immune-favorable TME subtype (IE) in most of the other cancer types.

Analysis of Pyroptosis-Related Immune Signatures and Identification of Pyroptosis-Related LncRNA Prognostic Signature in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common urinary system malignant tumor with a high incidence and recurrence rate. Pyroptosis is a kind of programmed cell death caused by inflammasomes. More and more evidence had confirmed that pyroptosis plays a very significant part in cancer, and it is controversial whether pyroptosis promotes or inhibits tumors. Consistently, its potential role in ccRCC treatment efficacy and prognosis remains unclear. In this study, we systematically investigated the role of pyroptosis in the ccRCC samples from The Cancer Genome Atlas (TCGA) database. Based on the differentially expressed pyroptosis-related genes (DEPRGs), we identified three pyroptosis subtypes with different clinical outcomes, immune signatures, and responses to immunotherapy. Gene set variation analysis (GSVA), Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that pyroptosis activation meant infiltration of more immune cells that is conducive to tumor progression. To further investigate the immunomodulatory effect of pyroptosis in ccRCC, we constructed a pyroptosis-score based on the common differential prognostic genes of the three pyroptosis subtypes. It was found that patients with high pyroptosis-score were in an unfavorable immune environment and the prognosis was worse. Gene set enrichment analysis suggested that immune-related biological processes were activated in the high pyroptosis-score group. Then, the least absolute shrinkage and selection operator (LASSO) Cox regression was implemented for constructing a prognostic model of eight pyroptosis-related long noncoding RNAs (PRlncRNAs) in the TCGA dataset, and the outcomes revealed that, compared with the low-risk group, the model-based high-risk group was intently associated with poor overall survival (OS). We further explored the relationship between high- and low-risk groups with tumor microenvironment (TME), immune infiltration, and drug therapy. Finally, we constructed and confirmed a robust and reliable PRlncRNA pairs prediction model of ccRCC, identified PRlncRNA, and verified it by experiments. Our findings suggested the potential role of pyroptosis in ccRCC, offering new insights into the prognosis of ccRCC and guiding effectual targeted therapy and immunotherapy.

Characteristics of Prognostic Programmed Cell Death-Related Long Noncoding RNAs Associated With Immune Infiltration and Therapeutic Responses to Colon Cancer

Programmed cell death (PCD) plays an important role in the onset and progression of various cancers. The molecular events surrounding the occurrence of abnormally expressed long noncoding RNAs (lncRNAs) leading to colon cancer (CC) have become a focus. We comprehensively evaluated the roles of PCD-related lncRNAs in the clinical management of CC and their immune responses. Therefore, we screened 41 prognostic PCD-related lncRNAs in The Cancer Genome Atlas database using co-expression analysis and assigned patients to groups according to the results of cluster analysis. The immune response and functions of cluster 2 were substantially suppressed, which might explain the poor prognosis in this group. A prognostic model comprising eight PCD-related lncRNAs was developed, and its effectiveness was verified using an external database. High-and low-risk groups had different epigenetic modifications and changes in immune cell infiltration. Patients in the high-risk group were resistant to immunotherapy and various chemotherapeutic drugs. Studies in vitro and in vivo further confirmed a carcinogenic role of the lncRNA U62317.4. Our findings of the prognostic value of PCD-related lncRNAs revealed their important roles in immune response disorders, thus providing valuable insights into the clinical management and molecular mechanisms of CC.

Construction and validation of an eight pyroptosis-related lncRNA risk model for breast cancer

OBJECTIVE

We developed a risk model based on pyroptosis-related long non-coding RNAs (lncRNAs) and assessed its prognostic value and clinical significance in breast cancer (BRCA).

METHODS

BRCA RNA sequencing data with corresponding clinical information were retrieved from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was used to examine correlations between prognosis of BRCA patients and the expression levels of pyroptosis-related lncRNAs. A prognostic model was developed and validated by identifying the correlation of risk scores with tumor immune infiltration and immune cell function through immune response analysis. Functional analyses of focal dysfunction-related lncRNA were also carried out. Lastly, single sample gene set enrichment analysis (ssGSEA) was conducted to determine the differences in immune responses between the low- and high-risk groups.

RESULTS

We divided the TCGA-BRCA dataset into 3 clusters by consensus clustering, and identified 11 pyroptosis-related lncRNAs that are differentially expressed between tumors and normal tissues. In addition, we determined if PD-L1 expression is associated with clustering and gene expression. The list was further narrowed down to eight pyroptosis-related lncRNAs and their regression coefficients were obtained through LASSO regression analysis. The relative proportion of 22 different immune cells in the BRCA microenvironment was determined using the CIBERSORT algorithm to explore the indicative effects of risk score on the tumor microenvironment (TME). We found that the resting mast cells, M0, and M2 macrophages were positively correlated with the risk scores.

CONCLUSION

The potential role of pyroptosis-related lncRNAs in BRCA prognosis may be exploited as a treatment target for patients with BRCA.

A Novel Pyroptotic and Inflammatory Gene Signature Predicts the Prognosis of Cutaneous Melanoma and the Effect of Anticancer Therapies

Purpose

The purpose of this study was to construct a gene signature comprising genes related to both inflammation and pyroptosis (GRIPs) to predict the prognosis of patients with cutaneous melanoma patients and the efficacy of immunotherapy, chemotherapy, and targeted therapy in these patients.

Methods

Gene expression profiles were collected from The Cancer Genome Atlas. Weighted gene co-expression network analysis was performed to identify GRIPs. Univariable Cox regression and Lasso regression further selected key prognostic genes. Multivariable Cox regression was used to construct a risk score, which stratified patients into high- and low-risk groups. Areas under the ROC curves (AUCs) were calculated, and Kaplan-Meier analyses were performed for the two groups, following validation in an external cohort from Gene Expression Omnibus (GEO). A nomogram including the GRIP signature and clinicopathological characteristics was developed for clinical use. Gene set enrichment analysis illustrated differentially enriched pathways. Differences in the tumor microenvironment (TME) between the two groups were assessed. The efficacies of immune checkpoint inhibitors (ICIs), chemotherapeutic agents, and targeted agents were predicted for both groups. Immunohistochemical analyses of the GRIPs between the normal and CM tissues were performed using the Human Protein Atlas data. The qRT-PCR experiments validated the expression of genes in CM cell lines, Hacat, and PIG1 cell lines.

Results

A total of 185 GRIPs were identified. A novel gene signature comprising eight GRIPs (TLR1, CCL8, EMP3, IFNGR2, CCL25, IL15, RTP4, and NLRP6) was constructed. The signature had AUCs of 0.714 and 0.659 for predicting 3-year overall survival (OS) in the TCGA entire and GEO validation cohorts, respectively. Kaplan-Meier analyses revealed that the high-risk group had a poorer prognosis. Multivariable Cox regression showed that the GRIP signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The nomogram showed good accuracy and reliability in predicting 3-year OS (AUC = 0.810). GSEA and TME analyses showed that the high-risk group had lower levels of pyroptosis, inflammation, and immune response, such as lower levels of CD8+ T-cell infiltration, CD4+ memory-activated T-cell infiltration, and ICI. In addition, low-risk patients whose disease expressed PD-1 or CTLA-4 were likely to respond better to ICIs, and several chemotherapeutic and targeted agents. Immunohistochemical analysis confirmed the distinct expression of five out of the eight GRIPs between normal and CM tissues.

Conclusion

Our novel 8-GRIP signature can accurately predict the prognosis of patients with CM and the efficacies of multiple anticancer therapies. These GRIPs might be potential prognostic biomarkers and therapeutic targets for CM.

Pyroptosis-Related Risk Signature Exhibits Distinct Prognostic, Immune, and Therapeutic Landscapes in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a common abdominal cancer. The existing therapeutic approaches often fail to achieve satisfactory results. Pyroptosis, an inflammatory form of programmed cell death, provides new ideas for anticancer treatment. However, the roles of pyroptosis-related (PR) genes (PRGs) in HCC remain elusive. Methods: Differentially expressed genes (DEGs) (n = 22) were screened out using TCGA and GTEx databases. A novel PR risk signature was constructed through Lasso regression analysis. Its prognostic value was evaluated through a series of survival analyses and was tested in ICGC and GSE14520 cohorts. CIBERSORT, ssGSEA, and ESTIMATE methods were employed to determine the effects of the PR risk score on the tumor immune microenvironment (TIM). The TIDE scoring system, IMvigor210 cohort, GSE109211 dataset, and GSDC database were applied to explore the associations of the PR risk score with therapeutic effects. The biofunctions of WNK1 in hepatocellular cancer (HC) cells were confirmed through qPCR, colony formation, and Transwell assays. Results: Overall, 22 of 45 PRGs (48.9%) were abnormally expressed in HCC samples. Then, a PR risk signature consisting of eight PRGs was constructed. A high PR risk score led to an unfavorable prognosis. The PR risk score was identified as an independent prognostic factor of HCC and could increase the decision-making benefit of the traditional TNM model. In addition, we established a nomogram containing the clinical stage and PR risk score to predict the survival rates of HCC patients. The prognostic value of the PR model was successfully validated in ICGC and GSE14520 cohorts. Moreover, high PR risk conferred the decreased infiltration level of CD8⁺ T cells and weakened the activities of "cytolytic activity" pathways. As for therapeutic correlation, a high PR risk score seemed to imply a poor efficacy of PD-1/L1 inhibitors and sorafenib. Finally, the overexpression of WNK1 could promote the proliferation, migration, and invasion of HC cells. Conclusions: The PR risk score was closely related to the prognosis, antitumor immune process, therapeutic outcomes, and malignant progression of HCC. WNK1, the core regulator of pyroptosis, possesses pro-oncogenic abilities, showing promise as a novel treatment target.

Pyroptosis-Related lncRNAs for Predicting the Prognosis and Identifying Immune Microenvironment Infiltration in Breast Cancer Lung Metastasis

Breast cancer (BC) is the second leading cause of death among women and is highly heterogeneous. Three pyroptosis (PR) subtypes were identified in patients with BC from the Cancer Genome Atlas Database (TCGA) cohorts using 20 PR-related regulators, which illustrate a strong association between BC prognosis and PR. Lung metastasis commonly occurs in the advanced stages of BC, resulting in a poor quality of life. Eight differentially expressed (DE) lncRNAs were identified using LASSO–Cox analysis between PR-related and BC lung metastasis. Moreover, a BRCA risk-score (RS) model was established using multivariate Cox regression, which correlated with prognosis in TCGA-BRCA. Clinical characteristics, tumor mutation burden, and tumor immune cell infiltration were extensively investigated between high- and low-RS groups. Similarly, a lower RS implied longer overall survival, greater inflammatory cell infiltration, and better immunotherapeutic response to PD-1 blockers. Our findings provide a foundation for future studies targeting PR and confirme that RS could predict the prognosis of patients with BC.

Necroptosis-related lncRNA to establish novel prognostic signature and predict the immunotherapy response in breast cancer

Background

Necroptosis is a type of programmed cell death, and recent researches have showed that lncRNAs could regulate the process of necroptosis in multiple cancers. We tried to screen necroptosis‐related lncRNAs and investigate the immune landscape in breast cancer (BC).

Methods

The samples of breast normal and cancer tissue were acquired from TCGA and GTEx databases. A risk prognostic model was constructed based on the identified necroptosis‐related lncRNAs by Cox regression and least absolute shrinkage and selection operator (LASSO) method. Moreover, the forecast performance of this model was verified and accredited by synthetic approach. Subsequently, an accurate nomogram was constructed to predict the prognosis of BC patients. The biological differences were investigated through GO, GSEA, and immune analysis. The immunotherapy response was estimated through tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) score.

Results

A total of 251 necroptosis‐related lncRNAs were identified by differential coexpression analysis, and SH3BP5‐AS1, AC012073.1, AC120114.1, LINC00377, AL133467.1, AC036108.3, and AC020663.2 were involved in the risk model, which had an excellent concordance with the prediction. The pathway analyses showed that immune‐related pathways were relevant to the necroptosis‐related lncRNAs risk model. And the risk score was significantly correlated with immune cell infiltration, as well as the ESTIMATE score. Most notably, the patients of higher risk score were characterized with increased TMB and decreased TIDE score, indicating that these patients showed better immune checkpoint blockade response.

Conclusion

These findings were conducive to understand the function of necroptosis‐related lncRNAs in BC and provide a potential promising therapeutic strategy for BC.

Identification of pyroptosis-related lncRNAs for constructing a prognostic model and their correlation with immune infiltration in breast cancer

The inflammasome-dependent cell death, which is denoted as pyroptosis, might be abnormally regulated during oncogenesis and tumour progression. Long non-coding RNAs (LncRNAs) are pivotal orchestrators in breast cancer (BC), which have the potential to be a biomarker for BC diagnosis and therapy. The present study aims to explore the correlation between pyroptosis-related lncRNAs and BC prognosis. In this study, a profile of 8 differentially expressed lncRNAs was screened in the TCGA database and used to construct a prognostic model. The BC patients were divided into high- and low-risk groups dependent on the median cutoff of the risk score in the model. Interestingly, the risk model significantly distinguished the clinical characteristics of BC patients between high- and low-risk groups. Then, the risk score of the model was identified to be an excellent independent prognostic factor. Notably, the GO, KEGG, GSEA and ssGSEA analyses revealed the different immune statuses between the high- and low-risk groups. Particularly, the 8 lncRNAs expressed differentially in BC tissues between two risk subgroups in vitro validation. Collectively, this constructed well-validated model is of high effectiveness to predict the prognosis of BC, which will provide novel means that is applicable for BC prognosis recognition.