HIF1α Regulates IL17 Signaling Pathway Influencing Sensitivity of Taxane-Based Chemotherapy for Breast Cancer

Single-cell transcriptome sequencing reveals SPP1-CD44-mediated macrophage-tumor cell interactions drive chemoresistance in TNBC

Triple-negative breast cancer (TNBC) is often considered one of the most aggressive subtypes of breast cancer, characterized by a high recurrence rate and low overall survival (OS). It is notorious for posing challenges related to drug resistance. While there has been progress in TNBC research, the mechanisms underlying chemotherapy resistance in TNBC remain largely elusive. We collect single-cell RNA sequencing (scRNA-seq) data from five TNBC patients susceptible to chemotherapy and five resistant cases. Comprehensive analyses involving copy number variation (CNV), pseudotime trajectory, cell-cell interactions, pseudospace analysis, as well as transcription factor and functional enrichment are conducted specifically on macrophages and malignant cells. Furthermore, we performed validation experiments on clinical samples using multiplex immunofluorescence. We identified a subset of SPP1+ macrophages that secrete SPP1 signals interacting with CD44 on malignant cell surfaces, potentially activating the PDE3B pathway within malignant cells via the integrin pathway, leading to chemotherapy resistance. The abnormally enhanced SPP1 signal between macrophages and malignant cells may serve as a factor promoting chemotherapy resistance in TNBC patients. Therefore, SPP1+ macrophages could potentially serve as a therapeutic target to reduce chemotherapy resistance.

CCAAT enhancer binding protein delta activates vesicle associated membrane protein 3 transcription to enhance chemoresistance and extracellular PD-L1 expression in triple-negative breast cancer

BACKGROUND

Chemoresistance and immunosuppression are two major obstacles in the current anti-cancer treatments. This study investigates the involvements of a CCAAT enhancer binding protein delta (CEBPD)/vesicle associated membrane protein 3 (VAMP3) axis in paclitaxel (PTX) resistance and immune evasion in triple-negative breast cancer (TNBC).

METHODS

PTX resistance-related genes were screened by bioinformatics. CEBPD and VAMP3 expression in clinical TNBC samples was examined by immunohistochemistry. Three PTX-resistant TNBC cell lines (MDA-MB-231/PTX, MDA-MB-468/PTX and MDA-MB-453/PTX) were generated, and their drug resistance was analyzed. Autophagy of cells was analyzed by immunofluorescence staining. Interaction between CEBPD and VAMP3 promoter was identified by immunoprecipitation and luciferase assays. The extracellular expression of programmed cell death-ligand 1 (PD-L1) in TNBC cells was detected. Extracellular vesicles (EVs) from TNBC cells were isolated to examine their effects on CD8+ T cell exhaustion.

RESULTS

CEBPD and VAMP3 were upregulated in chemo-resistant tissue samples and in PTX-resistant TNBC cells. The CEBPD downregulation enhanced PTX sensitivity of cells. However, further upregulation of VAMP3 in cells restored PTX resistance, which was likely due to the activation of autophagy, as the autophagy antagonist chloroquine enhanced PTX sensitivity of cells. CEBPD was found to bind to the VAMP3 promoter to activate its transcription. The CEBPD/VAMP3 axis also increased the PD-L1 expression in the conditioned medium of TNBC cells. The TNBC cell-derived EVs increased the exhaustion of co-cultured CD8+ T cells.

CONCLUSION

This study provides novel evidence that CEBPD plays a key role in enhancing PTX resistance in TNBC cells across various subtypes through VAMP3-mediated autophagy activation. Additionally, the CEBPD/VAMP3 axis also increases extracellular PD-L1 level, delivered by cancer cell-derived EVs, to suppress CD8+ T cell-mediated anti-tumor immune response. These significant observations may provide new insights into the treatment of TNBC, suggesting CEBPD and VAMP3 as promising targets to overcome treatment resistance.

ZC3H12A inhibits tumor growth and metastasis of breast cancer under hypoxic condition via the inactivation of IL-17 signaling pathway

Hypoxia is a major contributor to tumor microenvironment (TME) and metastasis in most solid tumors. We seek to screen hypoxia-related genes affecting metastasis in breast cancer and to reveal relative potential regulatory pathway. Based on gene expression profiling of GSE17188 dataset, differential expressed genes (DEGs) were identified between highly metastatic breast cancer cells under hypoxia and samples under normoxia. The protein-protein interaction (PPI) network was utilized to determine hub genes. The gene expression profiling interactive analysis database (GEPIA2) and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) were employed to quantify hub genes. Moreover, overexpression of zinc finger CCCH-type containing 12A (ZC3H12A) was performed both in breast cancer cells and xenograft mouse model to determine the role of ZC3H12A. We identified 134 DEGs between hypoxic and normoxic samples. Based on PPI analysis, 5 hub genes interleukin (IL)-6, GALN (GAL), CD22 molecule (CD22), ZC3H12A and TNF receptor associated factor 1 (TRAF1) were determined; the expression levels of TRAF1, IL-6, ZC3H12A and GAL were remarkably downregulated while CD22 was upregulated in breast cancer cells. Besides, patients with higher expression of ZC3H12A had favorable prognosis. Overexpression of ZC3H12A could inhibit metastasis and tumor growth of breast cancer; overexpression of ZC3H12A downregulated the expression of IL-17 signaling pathway-related proteins such as IL-17 receptor A (IL-17RA), IL-17A and nuclear factor κB activator 1 (Act1). This study reveals ZC3H12A and IL-17 signaling pathway as potential therapeutic targets for hypoxic breast cancer.

Role of androgen receptor signaling pathway-related lncRNAs in the prognosis and immune infiltration of breast cancer

Androgen receptor (AR) is strong association with breast cancer (BRCA). We aimed to investigate the effect of the androgen receptor signaling pathway-related long non-coding RNAs (ARSP-related lncRNAs) on the process of subtype classification and the tumor microenvironment (TME) of breast cancer (BRCA). Our study screen ARSP-related lncRNAs for the construction of a risk model. The single-sample gene set enrichment analysis (ssGSEA) method was used to detect the differences between the immune responses generated by the patients belonging to the low- and high-risk groups. The relationship between the ARSP-related lncRNAs and TME was explored following the process of cluster analysis. The univariate Cox analysis and the Lasso regression analysis method was used to screen nine of these lncRNAs to develop a risk model. It was observed that risk score could function as an independent prognostic factor, affecting the prognoses of patients suffering from BRCA. The validity of the model was assessed by analyzing the generated calibration curves and a nomogram. Additionally, the effect of the risk score on the extent of immune cell infiltration realized in TME was explored. M2 macrophages correlated positively, whereas NK cells, CD4+ T cells, and naive B cells correlated negatively with the risk score. Results obtained using the cluster analysis indicated that immune scores correlated with clustered subtypes. Finally, the risk score and cluster subtypes were analyzed to study the sensitivity of the patients toward different drugs to identify the appropriate therapeutic agents. The prognoses of patients suffering from BRCA can be accurately predicted by ARSP-related lncRNAs.

Prognostic significance and identification of basement membrane-associated lncRNA in bladder cancer

Based on the importance of basement membrane (BM) in cancer invasion and metastasis, we constructed a BM-associated lncRNA risk model to group bladder cancer (BCa) patients. Transcriptional and clinical data of BCa patients were downloaded from The Cancer Genome Atlas (TCGA), and the expressed genes of BM-related proteins were obtained from the BM-BASE database. We download the GSE133624 chip data from the GEO database as an external validation dataset. We screened for statistically different BM genes between tumors and adjacent normal tissues. Co-expression analysis of lncRNAs and differentially expressed BM genes was performed to identify BM-related lncRNAs. Then, differentially expressed BM-related lncRNAs (DEBMlncRNAs) between tumor and normal tissues were identified. Univariate/multivariate Cox regression analysis was performed to select lncRNAs for risk assessment. LASSO analysis was performed to build a prognostic model. We constructed a model containing 8 DEBMlncRNAs (AC004034.1, AL662797.1, NR2F1-AS1, SETBP1-DT, AC011503.2, AC093010.2, LINC00649 and LINC02321). The prognostic risk model accurately predicted the prognosis of BCa patients and revealed that tumor aggressiveness and distant metastasis were associated with higher risk scores. In this model, we constructed a nomogram to assist clinical decision-making based on clinicopathological characteristics such as age, T, and N. The model also showed good predictive power for the tumor microenvironment and mutational burden. We validated the expression of eight lncRNAs using the dataset GSE133624 and two human bladder cancer cell lines (5637, BIU-87) and examined the expression and cellular localization of LINC00649 and AC011503.2 using a human bladder cancer tissue chip. We found that knockdown of LINC00649 expression in 5637 cells promoted the proliferation of 5637 cells.Our eight DEBMlncRNA risk models provide new insights into predicting prognosis, tumor invasion, and metastasis in BCa patients.

Identification of immune-related biomarkers for predicting neoadjuvant chemotherapy sensitivity in HER2 negative breast cancer via bioinformatics analysis

BACKGROUND

Neoadjuvant chemotherapy (NAC) is an important treatment for breast cancer (BC) patients. However, due to the lack of specific therapeutic targets, only 1/3 of human epidermal growth factor receptor 2 (HER2)-negative patients reach pathological complete response (pCR). Therefore, there is an urgent need to identify novel biomarkers to distinguish and predict NAC sensitive in BC patients.

METHODS

The GSE163882 dataset, containing 159 BC patients treated with NAC, was downloaded from the Gene Expression Omnibus (GEO) database. Patients with pathological complete response (pCR) and those with residual disease (RD) were compared to obtain the differentially expressed genes (DEGs). Functional enrichment analyses were conducted on these DEGs. Then, we intersect the DEGs and immune-related genes to obtain the hub immune biomarkers, and then use the linear fitting model ("glm" package) to construct a prediction model composed of 9 immune biomarkers. Finally, the single sample gene set enrichment analysis (ssGSEA) algorithm was used to analyze immune cell invasion in BC patients, and the correlation between immune cell content and immune gene expression levels was analyzed.

RESULTS

Nine immune-related biomarkers were obtained in the intersection of DEGs and immune-related genes. Compared with RD patients, CXCL9, CXCL10, CXCL11, CXCL13, GZMB, IDO1, and LYZ were highly expressed in pCR patients, while CXCL14 and ESR1 were lowly expressed in pCR patients. After linear fitting of the multi-gene expression model, the area under the curve (AUC) value of the ROC curve diagnosis of pCR patients was 0.844. Immunoinfiltration analysis showed that compared with RD patients, 15 of the 28 immune cell types examined showed high-infiltration in pCR patients, including activated CD8 T cells, effector memory CD8 T cells, and activated CD4 T cells.

CONCLUSIONS

This investigation ultimately identified 9 immune-related biomarkers as potential tools for assessing the sensitivity of NAC in HER2-negative BC patients. These biomarkers have great potential for predicting pCR BC patients.