Reversal of tamoxifen resistance by artemisinin in ER+ breast cancer: bioinformatics analysis and experimental validation.

Breast cancer is the leading cause of cancer-related deaths in women worldwide, with Hormone Receptor (HR)+ being the predominant subtype. Tamoxifen (TAM) serves as the primary treatment for HR+ breast cancer. However, drug resistance often leads to recurrence, underscoring the need to develop new therapies to enhance patient quality of life and reduce recurrence rates. Artemisinin (ART) has demonstrated efficacy in inhibiting the growth of drug-resistant cells, positioning art as a viable option for counteracting endocrine resistance. This study explored the interaction between artemisinin and tamoxifen through a combined approach of bioinformatics analysis and experimental validation. Five characterized genes (ar, cdkn1a, erbb2, esr1, hsp90aa1) and seven drug-disease crossover genes (cyp2e1, rorc, mapk10, glp1r, egfr, pgr, mgll) were identified using WGCNA crossover analysis. Subsequent functional enrichment analyses were conducted. Our findings confirm a significant correlation between key cluster gene expression and immune cell infiltration in tamoxifen-resistant and -sensitized patients. scRNA-seq analysis revealed high expression of key cluster genes in epithelial cells, suggesting artemisinin's specific impact on tumor cells in estrogen receptor (ER)-positive BC tissues. Molecular target docking and in vitro experiments with artemisinin on LCC9 cells demonstrated a reversal effect in reducing migratory and drug resistance of drug-resistant cells by modulating relevant drug resistance genes. These results indicate that artemisinin could potentially reverse tamoxifen resistance in ER-positive breast cancer.

An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer.

Background: Ovarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting the discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, are promising new markers. Previous studies only focused on exosome-related genes and employed the Lasso algorithm to construct prediction models, which are not robust. Methods: 420 OC patients from the TCGA datasets were divided into training and validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with exosome-related genes were selected using Pearson analysis. Univariate COX regression analysis was used to filter prognosis-related lncRNAs. The overlapping lncRNAs were identified as candidate lncRNAs for machine learning. Based on 10 machine learning algorithms and 117 algorithm combinations, the optimal predictor combinations were selected according to the C index. The exosome-related LncRNA Signature (ERLS) model was constructed using multivariate COX regression. Based on the median risk score of the training datasets, the patients were divided into high- and low-risk groups. Kaplan-Meier survival analysis, the time-dependent ROC, immune cell infiltration, immunotherapy response, and immune checkpoints were analyzed. Results: 64 lncRNAs were subjected to a machine-learning process. Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model. Kaplan-Meier survival analysis showed that the high-risk group had a lower survival rate. The area under the curve (AUC) in predicting OS at 1, 3, and 5 years were 0.758, 0.816, and 0.827 in the entire TCGA cohort. xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration, which may contribute to the activation of cytolytic activity, inflammation promotion, and T-cell co-stimulation pathways. The low-risk group had higher expression levels of PDL1, CTLA4, and higher TMB. The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy. Patients with low expression of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly better survival prospects, whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes. Conclusion: Our study constructed an ERLS model that can predict prognostic risk and immunotherapy response, optimizing clinical management for OC patients.

CRP, IL-1α, IL-1ß, and IL-6 levels and the risk of breast cancer: a two-sample Mendelian randomization study.

Epidemiological studies have reported a positive association between chronic inflammation and cancer risk. However, the causal association between chronic inflammation and breast cancer (BC) risk remains unclear. Here, we performed a Mendelian randomization study to investigate the etiological role of chronic inflammation in BC risk. We acquired data regarding C-reactive protein (CRP), interleukin (IL)-1a, IL-1b, and IL-6 expression and BC related to single nucleotide polymorphisms (SNPs) from two larger consortia (the genome-wide association studies and the Breast Cancer Association Consortium). Next, we conducted the two-sample Mendelian randomization study to investigate the relationship of the abovementioned inflammatory factors with the incidence of BC. We found that genetically predicted CRP, IL-6, and IL-1a levels did not increase BC incidence (odds ratio (OR)CRP 1.06, 95% confidence interval (CI) 0.98-1.12, P = 0.2059, ORIL-6 1.05, 95% CI 0.95-1.16, P = 0.3297 and ORIL-1a 1.01, 95% CI 0.99-1.03, P = 0.2167). However, in subgroup analysis, genetically predicted IL-1b levels increased ER + BC incidence (OR 1.15, 95% CI 1.03-1.27, P = 0.0088). Our study suggested that genetically predicted IL-1b levels were found to increase ER + BC susceptibility. However, due to the support of only one SNP, heterogeneity and pleiotropy tests cannot be performed, which deserves further research.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels the heterogeneity of cancer-associated fibroblasts in TNBC.

BACKGROUND: The treatment of triple-negative breast cancer (TNBC) is one of the main focuses and key difficulties because of its heterogeneity, and the source of this heterogeneity is unclear. METHODS: Single-cell RNA (scRNA) and transcriptomics data of TNBC and normal breast samples were retrieved from Gene Expression Omnibus (GEO) database and TCGA-BRCA database. These cells were clustered using the t-SNE and UMAP method, and the marker genes for each cluster were found. We annotated the clusters using the published literature, CellMarker database and "SingleR" R package. RESULTS: A total of 1535 cells and 21785 genes from 6 TNBC patients and 2068 cells and 15868 genes from 3 normal breast tissues were used for downstream analyses. The scRNA data were divided into 14 clusters labeled into 8 cell types, including epithelial cells, immunocytes, CAFs/fibroblasts and etc. In the TNBC samples, CAFs were divided into three clusters and labelled as prCAFs, myCAFs and emCAFs, and the marker genes were DCN, FAP and RGS5, respectively. The prCAF subgroup is functionally characterized by promoting proliferation and multi drug resistance; myCAF subgroup is involved in constituting the extracellular matrix and collagen production, matrix composition and collagen production, and the emCAF functionally characterized by energy metabolism. CONCLUSIONS: TNBC has inter- and intra-tumor heterogeneity, and CAF is one of the sources of this heterogeneity. CD74, SASH3, CD2, TAGAP and CCR7 served as significant marker genes with prognostic and therapeutic value.

Severe mental illness and the risk of breast cancer: A two-sample, two-step multivariable Mendelian randomization study.

BACKGROUND: Based on epidemiological reports, severe mental illness (SMI) and breast cancer (BC) risk are linked positively. However, it is susceptible to clinical confounding factors, such as smoking, alcohol consumption, etc. Here, we performed a two-sample, two-step multivariable Mendelian randomization (MR) research to explore how the SMI etiologically influences BC risk and to quantify mediating effects of known modifiable risk factors. METHODS: Data concerning the single nucleotide polymorphism (SNP)-associated with schizophrenia, bipolar disorder (BD), major depressive disorder (MDD), and BC were obtained from two large consortia: the Breast Cancer Association Consortium (BCAC) and the Psychiatric Genomics Consortium (PGC). Then, the correlations of the previous SMI with the BC prevalence and the potential impact of mediators were explored through the two-sample and two-step MR analyses. RESULTS: In two-sample MR, schizophrenia increased BC incidence (odds ratio (OR) 1.06, 95% confidence interval (CI) 1.02-1.10, P = 0.001). In subgroup analysis, schizophrenia increased ER+ BC (OR 1.06, 95% CI 1.03-1.10, P = 0.0009) and ER-BC (OR 1.06, 95% CI 1.01-1.11, P = 0.0123) incidences. Neither MDD nor BD elevated the BC risk. In two-step MR, smoking explained 11.29% of the schizophrenia-all BC risk association. CONCLUSIONS: Our study indicates that schizophrenia increases susceptibility to breast cancer, with smoking playing a certain mediating role. Therefore, BC screening and smoking should be incorporated into the health management of individuals with schizophrenia.

Macrophages Phenotype Regulated by IL-6 Are Associated with the Prognosis of Platinum-Resistant Serous Ovarian Cancer: Integrated Analysis of Clinical Trial and Omics.

Background: The treatment of platinum-resistant recurrent ovarian cancer (PROC) is a clinical challenge and a hot topic. Tumor microenvironment (TME) as a key factor promoting ovarian cancer progression. Macrophage is a component of TME, and it has been reported that macrophage phenotype is related to the development of PROC. However, the mechanism underlying macrophage polarization and whether macrophage phenotype can be used as a prognostic indicator of PROC remains unclear. Methods: We used ESTIMATE to calculate the number of immune and stromal components in high-grade serous ovarian cancer (HGSOC) cases from The Cancer Genome Atlas database. The differential expression genes (DEGs) were analyzed via protein-protein interaction network, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) analysis to reveal major pathways of DEGs. CD80 was selected for survival analysis. IL-6 was selected for gene set enrichment analysis (GSEA). A subsequent cohort study was performed to confirm the correlation of IL-6 expression with macrophage phenotype in peripheral blood and to explore the clinical utility of macrophage phenotype for the prognosis of PROC patients. Results: A total of 993 intersecting genes were identified as candidates for further survival analysis. Further analysis revealed that CD80 expression was positively correlated with the survival of HGSOC patients. The results of GO and KEGG analysis suggested that macrophage polarization could be regulated via chemokine pathway and cytokine-cytokine receptor interaction. GSEA showed that the genes were mainly enriched in IL-6-STAT-3. Correlation analysis for the proportion of tumor infiltration macrophages revealed that M2 was correlated with IL-6. The results of a cohort study demonstrated that the regulation of macrophage phenotype by IL-6 is bidirectional. The high M1% was a protective factor for progression-free survival. Conclusion: Thus, the macrophage phenotype is a prognostic indicator in PROC patients, possibly via a hyperactive IL-6-related pathway, providing an additional clue for the therapeutic intervention of PROC.

Effect of ERCC1 polymorphisms on the response to platinum-based chemotherapy: A systematic review and meta-analysis based on Asian population.

BACKGROUND: Platinum-based chemotherapy is one of the most common treatments for many cancers; however, the effect of chemotherapy varies from individual to individual. Excision repair cross complementation group 1 (ERCC1) is widely recognized as a key gene regulating nucleotide excision repair (NER) and is closely associated with platinum response. Many studies have yielded conflicting results regarding whether ERCC1 polymorphisms can affect the response to platinum and overall survival (OS). Therefore, it is necessary to perform a meta-analysis of patients with specific races and cancer types. METHODS: Eight databases (EMBASE, PubMed, Cochrane Library, Chinese National Knowledge Infrastructure, Scopus, VIP, China Biology Medicine disc and Wanfang databases) were searched. Results were expressed in terms of odds ratios (ORs), hazard ratios (HRs) and 95% CIs. RESULTS: In this study, rs11615, rs2298881 and rs3212986 SNPs were studied. In the comparison between CT and TT on the response to platinum, esophageal cancer [I2 = 0%, OR = 6.18, 95% CI(1.89,20.23), P = 0.003] and ovarian cancer [I2 = 0%, OR = 4.94, 95% CI(2.21,11.04), P<0.001] showed that the rs11615 CT genotype predicted a better response. In the comparison between CC and TT, ovarian cancer [I2 = 48.0%, OR = 6.15, 95% CI (2.56,14.29), P<0.001] indicated that the CC genotype predicted a better response. In the meta-analysis of OS, the CC genotype was related to longer OS than TT in ovarian cancer [TT vs CC: I2 = 57.7%, HR = 1.71, 95% CI (1.18, 2.49), P<0.001]. CONCLUSION: The ERCC1 rs11615 polymorphism was related to the response to platinum and OS, but the correlation is based on specific cancer types in the Asian population.

PTGIS May Be a Predictive Marker for Ovarian Cancer by Regulating Fatty Acid Metabolism.

Background: Ovarian cancer tends to metastasize to the omentum, which is an organ mainly composed of adipose tissue. Many studies have found that fatty acid metabolism is related to the occurrence and metastasis of cancers. Therefore, it is possible that fatty acid metabolism-related genes (FAMRG) affect the prognosis of ovarian cancer patients. Methods: First, profiles of ovarian cancer and normal ovarian tissue transcriptomes were acquired from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. A LASSO regression predictive model was developed via the "glmnet" R package. The nomogram was created via the "regplot." Gene Set Variation Analysis (GSVA), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) analyses were conducted to determine the FAMRGs' roles. The percentage of immunocyte infiltration was calculated via CIBERSORT. Using "pRRophetic," the sensitivity of eight regularly used medications and immunotherapy was anticipated. Results: 125 genes were determined as different expression genes (DEGs). Based on RXRA, ECI2, PTGIS, and ACACB, a prognostic model is created and the risk score is calculated. Analyses of univariate and multivariate regressions revealed that the risk score was a distinct prognostic factor (univariate: HR: 2.855, 95% CI: 1.756-4.739, P < 0.001; multivariate: HR: 2.943, 95% CI: 1.800-4.812, P < 0.001). The nomogram demonstrated that it properly predicted the 1-year survival rate. The expression of memory B molecular units, follicular helper T molecular units, regulatory T molecular units, and M1 macrophages differed remarkably between the groups at high and low risk (P < 0.05). Adipocytokine signaling pathways, cancer pathways, and degradation of valine, leucine, and isoleucine vary between high- and low-risk populations. The findings of the GO enrichment revealed that the extracellular matrix and cellular structure were the two most enriched pathways. PTGIS, which is an important gene in fatty acid metabolism, was identified as the hub gene. This result was verified in ovarian cancer and ovarian tissues. The connection between the gene and survival was statistically remarkable (P = 0.015). The pRRophetic algorithm revealed that the low-risk group was more adaptable to cisplatin, doxorubicin, 5-fluorouracil, and etoposide (P < 0.001). Conclusion: PTGIS may be an indicator of prognosis and a possible therapeutic target for the therapy of ovarian cancer patients. The fatty acid metabolism of immune cells may be controlled, which has an indirect effect on cancer cell growth.

Construction of a breast cancer prognosis model based on alternative splicing and immune infiltration.

BACKGROUND: Breast cancer (BC) is the most common malignancy among women in the world. Alternative splicing (AS) is an important mechanism for regulating gene expression and producing proteome diversity, which is closely related to tumorigenesis. Understanding the role of AS in BC may be helpful to reveal new therapeutic targets for clinical interventions. METHODS: RNA-seq, clinical and AS data of TCGA-BRCA were downloaded from TCGA and TCGA SpliceSeq databases. AS events associated with prognosis were filtered by univariate Cox regression. The AS risk model of BC was built by Lasso regression, random forest and multivariate Cox regression. The accuracy of the AS risk model and clinicopathological factors were evaluated by time-dependent receiver operating characteristic (ROC) curves. The significant factors were used to construct the nomogram model. Tumor microenvironment analysis, immune infiltration and immune checkpoint analysis were performed to show the differences between the high and low AS risk groups. The expression differences of genes of AS events constituting the risk model in tumor tissues and normal tissues were analyzed, the genes with significant differences were screened, and their relationship with prognosis, tumor microenvironment, immune infiltration and immune checkpoint were analyzed. Finally, Pearson correlation analysis was used to calculate the correlation coefficient between splicing factors (SF) and prognostic AS events in TCGA-BRCA. The results were imported into Cytoscape, and the associated network was constructed. RESULTS: A total of 21,232 genes had 45,421 AS events occurring in TCGA-BRCA, while 1604 AS events were found to be significantly correlated with survival. The BRCA risk model consisted of 5 AS events, (TTC39C|44853|AT*- 2.67) + (HSPBP1|52052|AP*- 4.28) + (MAZ|35942|ES*2.34) + (ANK3|11845|AP*1.18) + (ZC3HAV1|81940|AT*1.59), which were confirmed to be valuable for predicting BRCA prognosis to a certain degree, including ROC curve, survival analysis, tumor microenvironment analysis, immune infiltration and immune checkpoint analysis. Based on this, we constructed a nomogram prediction model composed of clinicopathological features and the AS risk signature. Furthermore, we found that MAZ was a core gene indicating the connection of tumor prognosis and AS events. Ultimately, a network of SF-AS regulation was established to reveal the relationship between them. CONCLUSIONS: We constructed a nomogram model combined with clinicopathological features and AS risk score to predict the prognosis of BC. The detailed analysis of tumor microenvironment and immune infiltration in the AS risk model may further reveal the potential mechanisms of BC recurrence and development.