Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer

Integration of transcriptomics and machine learning for insights into breast cancer: exploring lipid metabolism and immune interactions

Background

Breast cancer (BRCA) represents a substantial global health challenge marked by inadequate early detection rates. The complex interplay between the tumor immune microenvironment and fatty acid metabolism in BRCA requires further investigation to elucidate the specific role of lipid metabolism in this disease.

Methods

We systematically integrated nine machine learning algorithms into 184 unique combinations to develop a consensus model for lipid metabolism-related prognostic genes (LMPGS). Additionally, transcriptomics analysis provided a comprehensive understanding of this prognostic signature. Using the ESTIMATE method, we evaluated immune infiltration among different risk subgroups and assessed their responsiveness to immunotherapy. Tailored treatments were screened for specific risk subgroups. Finally, we verified the expression of key genes through in vitro experiments.

Results

We identified 259 differentially expressed genes (DEGs) related to lipid metabolism through analysis of the cancer genome atlas program (TCGA) database. Subsequently, via univariate Cox regression analysis and C-index analysis, we developed an optimal machine learning algorithm to construct a 21-gene LMPGS model. We used optimal cutoff values to divide the lipid metabolism prognostic gene scores into two groups according to high and low scores. Our study revealed distinct biological functions and mutation landscapes between high-scoring and low-scoring patients. The low-scoring group presented a greater immune score, whereas the high-scoring group presented enhanced responses to both immunotherapy and chemotherapy drugs. Single-cell analysis highlighted significant upregulation of CPNE3 in epithelial cells. Moreover, by employing molecular docking, we identified niclosamide as a potential targeted therapeutic drug. Finally, our experiments demonstrated high expression of MTMR9 and CPNE3 in BRCA and their significant correlation with prognosis.

Conclusion

By employing bioinformatics and diverse machine learning algorithms, we successfully identified genes associated with lipid metabolism in BRCA and uncovered potential therapeutic agents, thereby offering novel insights into the mechanisms and treatment strategies for BRCA.

ELK4 targets CHMP6 to inhibit ferroptosis and enhance malignant properties of skin cutaneous melanoma cells

Ferroptosis, a key factor in tumor progression, is poorly understood at the molecular level. This study investigates how ELK4 and CHMP6 regulate skin cutaneous melanoma (SKCM) cell proliferation and ferroptosis. Analysis of TCGA data reveals high expression of ELK4 and CHMP6 in SKCM. Overexpression of ELK4 or CHMP6 enhances cell proliferation, invasion, and migration while reducing ROS and Fe2 + levels. It also increases GPX4 and xCT expression and decreases ACSL4 levels in SKCM cells. The opposite effects are observed with ELK4 or CHMP6 knockdown. ELK4 binds to the CHMP6 promoter, promoting CHMP6 transcription. Knockdown of CHMP6 reverses the oncogenic effects of ELK4 overexpression. In conclusion, ELK4 enhances proliferation, invasion, and migration while inhibiting ferroptosis in SKCM cells by upregulating CHMP6 transcription. This study sheds light on the intricate mechanisms involved in SKCM progression and identifies potential therapeutic targets in melanoma treatment.

Identification and Validation of a Necroptosis-Related Prognostic Model in Tumor Recurrence and Tumor Immune Microenvironment in Breast Cancer Management

Background

Breast cancer is the leading cause of cancer-related death in women. Necroptosis, a form of programmed necrotic cell death, occurs in many solid tumors, including breast cancer, and influences anti-tumor immunity. The role of necroptosis in managing breast cancer recurrence remains unclear.

Methods

Gene expression profiles and clinical data of breast cancer patients were obtained from the GEO (GSE20685, GSE21653, GSE25055) and TCGA databases. Data analysis and visualization were performed using R. Unsupervised Consensus Clustering and LASSO-COX regression stratified breast cancer patients. GO, KEGG, GSVA, ESTIMATE, and ROC analyses were used to investigate necroptotic signatures. In vitro and in vivo experiments validated necroptosis's role in breast cancer immunity.

Results

The potential function of necroptotic signature in immunity was first indicated with GO analysis in BRCA cohort. Next, two prognostic models based on the necroptotic profiles both suggested a link between low-risk group with a particular necroptotic immune signature. And a variety of immune cells and immune pathways were shown to be positively associated with a patient's risk score. As an altered immune checkpoint pattern was observed after regulating necroptotic genes, where TIM-3 and LAGLS9 elevated significantly in low-risk group, further validation in vitro and in vivo demonstrated that manipulating a subset of necroptotic gene set could sensitize tumor response to the co-blockade immunotherapy of anti-TIM-3 and anti-PD-1.

Conclusion

We demonstrated two strategies to stratify breast cancer patients based on their necroptotic profiles and showed that necroptotic signature could assign patients with different tumor immune microenvironment patterns and different recurrence-related prognosis. A subset of necroptotic gene set, composed of TLR3, RIPK3, NLRP3, CASP1, ALDH2 and EZH2, was identified as a biomarker set for predicting immunotherapy-response and recurrence-related prognosis. Targeting necroptosis could helpfacilitate the development of novel breast cancer treatments and tailor personalized medical treatment.

Revolutionizing breast cancer treatment: Harnessing the related mechanisms and drugs for regulated cell death (Review)

Breast cancer arises from the malignant transformation of mammary epithelial cells under the influence of various carcinogenic factors, leading to a gradual increase in its prevalence. This disease has become the leading cause of mortality among female malignancies, posing a significant threat to the health of women. The timely identification of breast cancer remains challenging, often resulting in diagnosis at the advanced stages of the disease. Conventional therapeutic approaches, such as surgical excision, chemotherapy and radiotherapy, exhibit limited efficacy in controlling the progression and metastasis of the disease. Regulated cell death (RCD), a process essential for physiological tissue cell renewal, occurs within the body independently of external influences. In the context of cancer, research on RCD primarily focuses on cuproptosis, ferroptosis and pyroptosis. Mounting evidence suggests a marked association between these specific forms of RCD, and the onset and progression of breast cancer. For example, a cuproptosis vector can effectively bind copper ions to induce cuproptosis in breast cancer cells, thereby hindering their proliferation. Additionally, the expression of ferroptosis‑related genes can enhance the sensitivity of breast cancer cells to chemotherapy. Likewise, pyroptosis‑related proteins not only participate in pyroptosis, but also regulate the tumor microenvironment, ultimately leading to the death of breast cancer cells. The present review discusses the unique regulatory mechanisms of cuproptosis, ferroptosis and pyroptosis in breast cancer, and the mechanisms through which they are affected by conventional cancer drugs. Furthermore, it provides a comprehensive overview of the significance of these forms of RCD in modulating the efficacy of chemotherapy and highlights their shared characteristics. This knowledge may provide novel avenues for both clinical interventions and fundamental research in the context of breast cancer.

Bioinformatics analysis and validation of genes related to paclitaxel's anti-breast cancer effect through immunogenic cell death

Research indicated that Paclitaxel (PTX) can induce immunogenic cell death (ICD) through immunogenic modulation. However, the combination of PTX and ICD has not been extensively studied in breast cancer (BRCA). The TCGA-BRCA and GSE20685 datasets were enrolled in this study. Samples from the TCGA-BRCA dataset were consistently clustered based on selected immunogenic cell death-related genes (ICD-RGs). Next, candidate genes were obtained by overlapping differentially expressed genes (DEGs) between BRCA and normal groups, intersecting genes common to DEGs between cluster1 and cluster2 and hub module genes, and target genes of PTX from five databases. The univariate Cox algorithm and the least absolute shrinkage and selection operator (LASSO) were performed to obtain biomarkers and build a risk model. Following observing the immune microenvironment in differential risk subgroups, single-gene gene set enrichment analysis (GSEA) was carried out in all biomarkers. Finally, the expression of biomarkers was analyzed. Enrichment analysis showed that 626 intersecting genes were linked with inflammatory response. Further five biomarkers (CHI3L1, IL18, PAPLN, SH2D2A, and UBE2L6) were identified and a risk model was built. The model's performance was validated using GSE20685 dataset. Furthermore, the biomarkers were enriched with adaptive immune response. Lastly, the experimental results indicated that the alterations in IL18, SH2D2A, and CHI3L1 expression after treatment matched those in the public database. In this study, Five PTX-ICD-related biomarkers (CHI3L1, IL18, PAPLN, SH2D2A, and UBE2L6) were identified to aid in predicting BRCA treatment outcomes.

Single-cell and bulk RNA sequencing analysis of B cell marker genes in TNBC TME landscape and immunotherapy

Objective

This study amied to investigate the prognostic characteristics of triple negative breast cancer (TNBC) patients by analyzing B cell marker genes based on single-cell and bulk RNA sequencing.

Methods

Utilizing single-cell sequencing data from TNBC patients, we examined tumor-associated B cell marker genes. Transcriptomic data from The Cancer Genome Atlas (TCGA) database were used as the foundation for predictive modeling. Independent validation set was conducted using the GSE58812 dataset. Immune cell infiltration into the tumor was assessed through various, including XCELL, TIMER, QUANTISEQ, CIBERSORT, CIBERSORT-ABS, and ssGSEA. The TIDE score was utilized to predict immunotherapy outcomes. Additional investigations were conducted on the immune checkpoint blockade gene, tumor mutational load, and the GSEA enrichment analysis.

Results

Our analysis encompassed 22,106 cells and 20,556 genes in cancerous tissue samples from four TNBC patients, resulting in the identification of 116 B cell marker genes. A B cell marker gene score (BCMG score) involving nine B cell marker genes (ZBP1, SEL1L3, CCND2, TNFRSF13C, HSPA6, PLPP5, CXCR4, GZMB, and CCDC50) was developed using TCGA transcriptomic data, revealing statistically significant differences in survival analysis (P<0.05). Functional analysis demonstrated that marker genes were predominantly associated with immune-related pathways. Notably, substantial differences between the higher and lower- BCMG score groups were observed in terms of immune cell infiltration, immune cell activity, tumor mutational burden, TIDE score, and the expression of immune checkpoint blockade genes.

Conclusion

This study has established a robust model based on B-cell marker genes in TNBC, which holds significant potential for predicting prognosis and response to immunotherapy in TNBC patients.

Bridging Pyroptosis and Immunity: A Comprehensive Study of the Pyroptosis-Related Long Non-Coding RNA Signature in Breast Cancer

Breast cancer continuously poses serious clinical challenges to human health due to its intrinsic heterogenicity and evolving drug resistance. Recently, increasing evidence has shown that pyroptosis, known as a programmed and inflammatory form of cell death, participates in tumorigenesis, progression, and remodeling of the tumor immune microenvironment (TIME). However, a comprehensive insight into pyroptosis-related signatures for breast cancer remains elusive. The current study established a pyroptosis-related lncRNA signature using transcriptome data and corresponding clinical information from The Cancer Genome Atlas (TCGA). Pyroptosis-related gene clusters, the associated differential expression in breast cancer patients' subtypes, and the potential mechanisms were all discussed. This integrative analysis revealed a unique signature underpinning the dichotomy of breast cancer progression and survival outcomes. Interestingly, the pyroptosis-related lncRNA signature was revealed as closely intertwined with the TIME. A correlation was established between the pyroptosis-related LncRNA signature and the TIME, underlying the mutual effect between pyroptosis and the immune responses implicated in breast cancer. The findings in this work underline the critical role exerted by pyroptosis in breast cancer, providing new insights into disease progression, prognosis, and therapeutic potential. This work has been poised to provide new avenues for personalized, immune-based cancer therapeutics by enhancing our understanding of pyroptosis in breast cancer.

Inflammasomes in breast cancer: the ignition spark of progression and resistance?

Inflammation and immune evasion are major key players in breast cancer (BC) progression. Recently, the FDA approved the use of anti-programmed death-ligand 1 antibody (anti-PD-L1) and phosphoinositide 3-kinase (PI3K) inhibitors against aggressive BC. Despite the paradigm shift in BC treatments, patients still suffer from resistance, recurrence and serious immune-related adverse events. These obstacles require unravelling of the hidden molecular contributors for such therapy failure hence yielding therapeutics that are at least as efficient yet safer. Inflammasome pathway is activated when the pattern recognition receptor senses danger signals (danger-associated molecular patterns) from damagedRdying cells or pathogen-associated molecular patterns found in microbes, leading to secretion of the active pro-inflammatory cytokines interleukin-1β (IL-1β) and interleukin-18 (IL-18). It has been shown throughout numerous studies that inflammasome pathway enhanced invasion, metastasis, provoked BC progression and therapy resistance. Additionally, inflammasomes upregulated the proliferative index ki67 and enhanced PD-L1 expression leading to immunotherapy resistance. IL-1β contributed to significant decrease in oestrogen receptor levels and promoted BC chemo-resistance. High levels of IL-18 in sera of BC patients were associated with worst prognosis. Stimulation of purinergic receptors and modulation of adipokines in obese subjects activated inflammasomes that evoked radiotherapy resistance and BC progression. The micro RNA miR-223-3p attenuated the inflammasome over-expression leading to lowered tumour volume and lessened angiogenesis in BC. This review sheds the light on the molecular pathways of inflammasomes and their impacts in distinct BC subtypes. In addition, it highlights novel strategies in treatment and prevention of BC.