Prognostic correlation and immune characteristics of a TAM cluster- related 8-gene risk signature in triple-negative breast cancer

doi:10.21203/rs.3.rs-3213759/v1

Download PDF

Research Article

Prognostic correlation and immune characteristics of a TAM cluster- related 8-gene risk signature in triple-negative breast cancer

https://doi.org/10.21203/rs.3.rs-3213759/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Tumor-associated macrophages (TAMs) play a critical role in the progression of and immune response to triple-negative breast cancer (TNBC). This study aimed to explore the features of TAMs in TNBCs, construct a risk signature associated with TAM clusters, and verify their relationship with prognosis and immune-related characteristics.

Primarily, we selected four TAM clusters and determined the prognosis-related clusters in TNBC based on single-cell RNA sequencing data. Subsequently, TAM-related prognostic genes were identified by univariate Cox regression analysis and an 8-genes risk signature was constructed by LASSO regression. The analysis of immune characteristics showed a significant association between the gene signature and stromal and immune scores as well as immune cells. Multivariate analysis revealed that the risk signature was an independent prognostic factor for TNBC, and confirmed its predictive value for immunotherapeutic outcomes. The newly constructed nomogram integrating stage and TAM-based risk signatures exhibited favorable predictability and reliability for TNBC prognosis prediction. Finally, the increased expression of GPR34, one of the eight hub genes, was explored in TNBC using reverse-transcriptase polymerase chain reaction, western blot, and immunohistochemistry. Our study may allow discovering new independent prognostic factors, updating immunotherapeutic methods, and identifying effective therapeutic targets for TNBC.

tumor-associated macrophage

triple-negative breast cancer

GPR34

immunotherapy

prognosis

risk signature

Breast cancer is the most prevalent malignancy and leading cause of cancer-related deaths among women worldwide. In 2020, there were approximately 2.3 million new cases and over 685,000 deaths attributable to breast cancer.¹[1] Among cancer subtypes, triple-negative breast cancer (TNBC) represents approximately 20% of the cases and is known for its aggressiveness, high recurrency, and mortality.[2, 3] The absence of estrogen and progesterone receptors and human epidermal growth factor receptor 2 (HER2) amplification preclude TNBC patients from benefiting from targeted approaches such as endocrine and anti-HER2 therapies.[1, 3] Moreover, only 5–10% of patients with advanced TNBC respond to standard chemotherapy.[4] Thus, effective therapeutic targets should be developed for TNBC treatment.

Recent cancer immunotherapy advances have revolutionized treatment paradigms.[5] Owing to its higher immunogenicity when compared with other breast cancer subtypes, immunotherapy is a viable strategy for treating TNBC.[6] In contrast to other cancer types, the effectiveness of immunotherapy in breast cancer, including TNBC, is relatively limited.[7] Although immune checkpoint inhibitors are possible treatment options, the prognosis of advanced TNBC remains poor for patients expressing PD-L1, with a median overall survival under two years.[6, 7] Therefore, it is necessary to identify new therapeutic strategies for TNBC.

Tumor-associated macrophages (TAMs) are crucial drivers of the immunosuppressive microenvironment and mediate tumor progression and resistance to immunotherapy.[8, 9] Modifying the properties and functions of TAMs in malignancies can enhance tumor immune surveillance and suppress immunological evasion.[8] Moreover, in TNBC, TAMs have can promote cancer progression [10] and release of tumor-derived CSF-1 and macrophage-derived EGF.[9] However, the specific features of TAMs and their relationship with TNBC prognosis and immune-related characteristics remain elusive.

Furthermore, the emergence of single-cell technologies has allowed dissecting of a myriad of immune cell states in the tumor microenvironment (TME).[5]

Here, we identified four TAM clusters based on single-cell RNA sequencing (scRNA-seq) data and selected the clusters associated with TNBC prognosis. We established an 8-gene risk signature based on the differentially expressed genes (DEGs) associated with the prognosis-related TAM clusters, analyzed the prognostic value and immune characteristics of the risk signature, and identified potentially effective breast cancer immunotherapy drugs based on such signature. Our study provides new insights for updating prognostic assessment methods and immunotherapy approaches for breast cancer.

Data acquisition and processing

We obtained scRNA-seq data (GSE206638) from the GEO database comprising four tumor and three juxta-tumor samples of TNBC.[11] Single cells were selected based on gene expression in a minimum of three cells, with each cell expressing at least 250 genes. Bulk RNA-seq datasets including survival data were collected from the TCGA-BRCA and GSE58812 databases. Additionally, the PD-L1-treated dataset, including clinical traits, was downloaded from the IMvigor210 cohort.[12]

Definition of TAM

The "Seurat" package [13] was used to conduct a comprehensive analysis of TNBC scRNA-seq data and delve into the TAM signature. Data preprocessing involved the exclusion of cells with gene expression counts < 250 or > 6000. Subsequently, log normalization was performed on the remaining genes. The “FindIntegrationAnchors” function was employed, and data dimensionality was reduced using the t-distributed Stochastic Neighbor Embedding method (dim = 30, resolution = 0.1) using a nonlinear approach. To classify single cells into subgroups, the “FindNeighbors” and “FindClusters” functions were used with parameters dim = 30 and resolution = 0.1. Additionally, we employed the “RuntSNE” function for tSNE dimensional reduction. TAMs were identified based on four marker genes (CD68, C1QA, APOC1, and CD163) and subsequently re-clustered using “FindClusters” and “FindNeighbors”. We defined marker genes for each TAM cluster by employing the “FindAllMarkers” function, which compared different clusters using specific criteria including minpct = 0.35, logFC = 0.5, and adjusted P-value < 0.05. To analyze the copy number variation (CNV) characteristics of the TAM clusters and differentiate them from tumor and normal cells, we utilized the “CopyKAT” package. Finally, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of marker genes using the "clusterProfiler" package.[14]

TAM hub genes identification

We utilized the "limma" package to identify DEGs between tumor and normal cells.[15] DEGs were selected based on a false discovery rate < 0.05 and |log2(Fold Change)|>1. Subsequently, we examined the correlations between DEGs and TAM clusters and identified key TAM-related genes with P < 0.001 and cor > 0.4. The prognosis-related genes were determined using univariate Cox regression analysis using the Kaplan–Meier “survival” package, considering P < 0.05 statistically significant. To reduce the number of genes, LASSO Cox regression analysis was performed, followed by multivariate Cox regression analysis using a stepwise regression method. Finally, the predictive performance of the risk signature was evaluated by a receiver operating characteristic curve (ROC) analysis using the "timeROC" package.

Immune landscape analysis

In the TCGA cohort, we evaluated the proportions of 22 immune cell subtypes and calculated immune and stromal scores using the CIBERSORT and ESTIMATE algorithms, respectively.[16] To validate the differences in immune status between subtypes, we subsequently performed an ESTIMATION analysis and single-sample gene set enrichment analysis (ssGSEA).[17, 18]

Construction of a risk signature and nomogram

To develop a nomogram model for clinical application, we conducted univariate and multivariate Cox regression analyses of the clinicopathological factors and risk signatures. The significant variables from the multivariate Cox model (those with P < 0.05) were used to construct the nomogram using the "rms" package.[19] Calibration curves were used to assess the predictive accuracy of the model, and a decision curve analysis (DCA) evaluated its reliability.

RNA extraction and reverse-transcriptase polymerase chain reaction

Total RNA was extracted from cancer cell lines (MDA-MB-231 and MCF-10A) using an RNA-Quick Purification Kit (EScience, RN001). Complementary DNA was synthesized using HiScript III RT SuperMix for qPCR and 1 mg of total RNA. Reverse-transcriptase polymerase chain reaction (RT-PCR) was performed using the ChamQ Universal SYBR qPCR Master Mix (Vazyme, Q711) on a LightCycler96 (Roche), according to the manufacturer’s instructions. The primer pairs used are listed in Table S1. The housekeeping gene ACTB was used as an internal control.

Western blot

Protein samples were extracted from MDA-MB-231 and MCF-10A cell lines with RIPA buffer (NCM Biotech, WB3100) containing protease inhibitors (Epizyme Biomedical Technology, GRF101). Protein samples were separated on 10% gels (Epizyme Biomedical Technology) and transferred onto polyvinyl difluoride membranes. After blocking with 5% nonfat dry milk, the membranes were incubated at 4°C with a primary antibody against GPR34 (Abnova, PAB26304) overnight followed by incubation in an HRP-conjugated secondary antibody (Beyotime, A0208) for 1 h at room temperature. The bands were visualized using ECL reagent (NCM Biotech, P10300) and imaged using a Bio-Rad imaging system. The signal intensity was quantified using the ImageJ software (National Institutes of Health).

Immunohistochemistry

Tissue microarray sections (15 TNBC and 15 noncancerous breast tissues) were deparaffinized and rehydrated by washing with xylene two times (5 min each); sequential washing with 100%, 95%, and 70% alcohol (1 min each); and then rinsing in distilled water. This was followed by antigen retrieval (#S1699, Agilent Dako). After blocking, the sections were incubated with the primary antibody, GPR34 (Abnova, PAB26304; 1:200), overnight at 4℃. The secondary antibodies were incubated at room temperature for 30 min. The target protein was visualized using diaminobenzidine.

Statistical analysis

Statistical analyses were performed using the R software (v4.1.2). Pearson or Spearman correlation was employed for correlation matrices, whereas the Wilcoxon test was used to compare the groups. Survival differences were assessed using Kaplan–Meier curves and the log-rank test, considering P-values < 0.05 statistically significant.

TAM screening in scRNA-seq samples

A flowchart of the study is shown in Fig. 1. This study included 11,535 cells from the scRNA-seq data and identified 24 subpopulations using log normalization and dimensionality reduction. Subsequently, TAM populations were identified using four marker genes (CD68, C1QA, APOC1, and CD163; Figs. 2A-B). Further clustering and dimensionality reduction of the TAM populations resulted in the identification of four clusters (Figs. 2C-D). The UMAP plot in Fig. 3A shows the distribution of seven samples; ultimately, four TAM clusters were generated for subsequent analysis (Fig. 3B). Figure 3C shows the expression of the top five DEGs representative of the TAM clusters. The four clusters proportions of each sample are shown in Fig. 3D. KEGG analysis (Fig. 3E) revealed DEGs enrichment in various pathways, such as oxidative phosphorylation, Parkinson's disease, and prion disease. Additionally, the four TAM clusters encompassed 1,533 tumor and normal cells based on their CNVs (Fig. 3F).

Cancer-related pathways expression in TAMs

To investigate the association between TAM clusters and tumor progression, we examined 10 tumor-related pathways within those clusters. Figure 4A shows the pathways GSVA scores across the different TAM clusters. Notably, the TAM-1 cluster exhibited a significantly higher proportion of malignant cells than the other clusters (Fig. 4B). Moreover, each TAM cluster showed different GSVA scores of the tumor-related pathways for malignant and non-malignant cells (Fig. 4C). Prognosis-associated ssGSEA scores were calculated for the marker genes in each TAM cluster using the TCGA cohort; the TAM-1 and TAM-3 clusters had higher scores in tumor samples, whereas the other clusters showed opposite trend (Fig. 4D). When dividing the TNBC samples into high- and low-TAM-score groups, the high-TAM-score group exhibited a better prognosis in the TAM-0 cluster (Fig. 4E), suggesting its potential significance in TNBC progression.

Identification of hub genes associated with TAM

The DEGs between tumor and normal tissues were identified to construct a risk signature. The 2160 DEGs identified included 893 upregulated and 1267 down-regulated genes (Fig. 5A). Among these, 434 exhibited significant correlations with prognosis-related TAM clusters. Subsequently, univariate Cox regression analysis identified 234 genes with prognostic significance (Figs. 5A-B), which were narrowed down to eight candidates (lambda = 0.052) by LASSO Cox regression analysis (Figs. 5C-D). Multivariate Cox regression analysis with stepwise regression classified ADGRF5, C7, EDNRB, GPR34, SAMD1, SDS, STOM, and VSTM4 as a risk signature (Fig. 5E). The formula for the 8-gene signature was determined, and risk scores were calculated for each sample, which were subsequently divided into high- and low-risk groups after z-mean normalization. The final 8-gene signature formula was as follows: Risk Score = -1.700909607*ADGRF5–0.264910514*SAMD1 + 0.532519618*C7 + 0.437584101*EDNRB + 1.570842883*GPR34 + 1.656234352*SDS + 1.773243073*STOM + 1.685475222*VSTM4. Kaplan–Meier survival analyses indicated that high-risk patients exhibited poorer survival outcomes across the three cohorts (Figs. 5F-H). Furthermore, the area under the curve (AUC) values for the model were 0.83–1 in the TCGA cohort, 0.58–0.82 in the GEO cohort, and 0.62–0.84 in the meta cohort, demonstrating its predictive capability (Figs. 5I-K).

Mutation and pathway analysis of the hub genes

The ADGRF5, C7, EDNRB, GPR34, SAMD1, STOM, and VSTM4 genes had SNV in multiple samples (Fig. 6A). We examined the co-occurrence probability between these key genes and the ten most mutated genes. GPR34 showed a significant probability of co-occurrence with TTN, MUC16, and APC mutations (Fig. 6B). Furthermore, we investigated the mutation frequency in ten major oncogenic pathways and detected mutations in several pathways, including the PI3K, TP53, and RTK-RAS pathways (Fig. 6C). Among the eight genes, only a few samples CNV exhibited gain/loss (Fig. 6D). To evaluate the tumor mutation burden (TMB), we calculated the TMB of TCGA patients and analyzed its distribution in the two risk groups. A waterfall plot revealed the top ten mutated genes (Fig. 6E), and the low-risk group had a significantly higher TMB (Fig. 6F). Additionally, we explored the co-occurrence probability of the five most-mutated genes and found a significant co-occurrence probability between MUC16 and TTN (Fig. 6G). Pathway analysis revealed that the eight genes were significantly correlated with 24 pathways, including base excision repair, prostate cancer, and gap junctions (Figs. 6H, I). Moreover, the high-risk group was highly enriched in immune-related pathways (Fig. 6J).

Hub genes and immunity association

GPR34, SDS, VSTM4, ADGRF5, C7, STOM, and EDNRB were significantly and positively correlated with the stromal, immune, and estimated scores (Fig. 7A), whereas SAMD1 exhibited significantly negative correlations with these scores (Fig. 7B). When grouped based on the median gene expression values, we compared the immune scores between the different expression groups. The high expression group (C7, GPR34, SDS, and STOM genes) had significantly higher immune scores (Fig. 7C). Furthermore, the ADGRF5, C7, EDNRB, and VSTM4 expression levels were significantly and positively correlated with resting mast cells, naïve B cells, and memory B cells (Fig. 7D). GPR34 expression was positively correlated with all ten immune cell types (Fig. 7E).

Correlation between the 8-gene signature and immune-related characteristics

Immune and stromal cells were evaluated in the two risk groups, and their scores were combined to obtain the estimated score. The low-risk group had significantly lower scores compared to those of the high-risk group (P < 0.001) (Fig. 8A). Correlation analysis between risk scores and immune, stromal, and estimated scores confirmed these significant associations (Figs. 8B-D). ssGSEA revealed significant differences in immune cell scores between the two risk groups, with higher immune cell scores in the high-risk group, except for type 2 T helper cells, which scored higher in the low-risk group (Fig. 8E). Moreover, the high-risk group expressed higher levels of TRS, CYT, and IFN-γ (Figs. 8F-H). Using the CIBERSORT method, we identified 22 different immune cell infiltrations. The high-risk group displayed higher contents of naive B cells, CD8 T cells, T cells regulatory, monocytes, M1 and M2 macrophages, and resting mast cells, but lower proportions of CD4 + naive T cells and activated mast cells (Fig. 8I). Additionally, we examined the expression of immune checkpoint genes in the two groups and found elevated levels in the high-risk group. PD1, PD-L1, and CTLA4 were significantly more expressed in the high-risk group (Figs. 8J-K). Furthermore, Subclass mapping indicated that patients in the high-risk group tended to respond to immune checkpoint inhibitors (Fig. 8L). Therefore, the high-risk group belongs to the hot tumor subtype and may respond favorably to immunotherapy.

Independent risk factors and potential therapeutic agents identification

To enhance the predictive performance of the risk signature, we conducted univariate and multivariate Cox regression analyses integrating clinicopathological characteristics and risk scores. Multivariate analysis revealed that the risk signature was the most significant independent prognostic factor for TNBC (hazard ratio (HR), 3.683; 95% confidence interval [CI]: 1.662–8.164; P = 0.001), and cancer stage was closely associated with prognosis (HR, 0.024; 95% CI: 0.001–0.769; P = 0.017) (Figs. 9A-B). Consequently, we developed a nomogram by combining stage and risk scores (Fig. 9C). The calibration plot demonstrates the effective predictive capability of the nomogram for actual survival outcomes (Fig. 9D). Moreover, DCA revealed that the nomogram exhibited superior discriminatory ability in identifying high-risk patients compared to the risk score and stage alone (Fig. 9E). The AUCs of the risk score and nomogram surpassed those of the other indicators in TCGA cohort (Fig. 9F). To identify potential agents for high-risk patients with TNBC, we utilized sensitivity data from the Cancer Therapeutics Response Portal (CTRP) and profiled relative inhibition simultaneously in mixtures (PRISM) datasets, encompassing 481 compounds across 835 cancer cell lines (CCLs) and 1448 compounds across 482 CCLs (Yang et al., 2021). We altogether identified 9 CTRP- (RITA, cytochalasin B, NVP − TAE684, GDC − 0941, BRD − K63431240, birinapant, dasatinib, cucurbitacin I, 1S,3R − RSL − 3) and 21 PRISM-derived agents (semaxanib, P276 − 00, AS − 703026, trametinib, AMG − 232, selumetinib, TAK − 733, BVD − 523, AZD8330, idasanutlin, PD − 0325901, PD − 184352, ponatinib, dasatinib, Ro − 4987655, VS − 4718, MEK162, PHA − 848125, cobimetinib, tipifarnib, GDC − 0152) (Figs. 9G–H). The AUCs of these agents were significantly negatively correlated with risk and significantly lower in the high-risk group (Figs. 9I–J).

GPR34 mRNA and protein expression in TNBC

To explore GPR34 as a potential biomarker of TNBC, we investigated its expression in TNBC cells and tissues. GPR34 mRNA expression was increased in MDA-MB-231 cells compared to MCF-10A cells (Fig. 10C). Subsequently, GPR34 protein expression was higher in MDA-MB-231 and TNBC tissue samples (Figs. 10A-B).

The increasingly reported crucial role of the TME in malignancies has shifted the focus of cancer research from the tumor itself to the surrounding microenvironment,[20, 21], which represents a major TME component and mediates tumor progression, poor prognosis, and therapy resistance in various malignancies.[22] TAMs are involved in promoting TNBC aggressiveness.[23] Moreover, TAMs subpopulations change dynamically during tumor development and correlate with immunotherapy efficacy.[22] Here, we comprehensively investigated the characteristics and classification of TAMs in TNBC using scRNA-seq data. We identified four distinct TAM clusters and examined their correlation with prognosis The TAM-0 cluster showed a unique prognostic value, indicating that specific TAM clusters may have potential prognostic prediction and help identify new prognostic subtyping patterns for TNBC.

Based on the DEGs identified within the TAMs clusters that were significantly associated with TNBC prognosis, we developed a risk signature that comprises six risk genes (C7, EDNRB, GPR34, SDS, STOM, and VSTM4) and two protective genes (ADGRF5 and SAMD1). High C7 expression in breast cancer indicates poor prognosis and decreases sensitivity to taxane-anthracycline,[24] while STOM is involved in breast cancer [25] and GPR34, a member of the G-protein coupled receptor superfamily, participates in cellular physiological functions such as cell growth, differentiation, and motility.[26] GPR34 also promotes malignancy in certain cancers, including cervical cancer, gastric adenocarcinoma, and colorectal cancer,[26, 27] showing a high expression in cell lines of cervical and colorectal cancers.[28, 29] Consistent with this, GPR34 was highly expressed in TNBC tissues and MDA-MB-231 cells in our study. Additionally, our findings demonstrated a stronger immune association between GPR34 and TNBC than that of the other seven genes. The significance of GPR34 in immune responses emphasizes its role in stimulating paracrine signaling in malignant B cells.[27, 30] Therefore, we hypothesized that GPR34 could be involved in the immune-related malignant characteristics of TNBC and serve as a promising immunotherapy target. However, this hypothesis warrants further investigation. Additionally, we examined the association between the risk signature and TNBC prognosis, revealing that the high-risk group had a poorer prognosis. Our findings suggest that our risk signature is a reliable tool for precise prognostic prediction in patients with TNBC. Moreover, almost all genes had SNV mutations, and three oncogenic pathways (PI3K, TP53, and RTK-RAS) were selected. The PI3K pathway is a prominent and extensively studied target for therapeutic intervention.[31] PI3K is a kinase involved in cellular transformation and is associated with polyoma middle T antigens; its activity in mitogenesis and oncogenesis plays an important role in human cancers.[32] PI3K regulates essential cellular functions including protein synthesis, glucose metabolism, apoptosis, and survival, namely through the generation of phosphatidylinositol 3,4,5-trisphosphate, which acts as a potent secondary messenger and recruits specific kinases, such as AKT and PDPK1, to the plasma membrane.[32] Furthermore, cancer genome sequencing has shed light on PI3K mutations in various human cancers, such as breast, colorectal, gastric, and lung tumors.[33] Several drugs targeting the PI3K pathway have gained regular or accelerated approval from the FDA for the treatment of PIK3CA-mutant and estrogen receptor-positive advanced-stage breast cancers.[32] TNBC harbors PI3K mutations that may trigger disease progression and therapy resistance.[34, 35] Activation of the PI3K/AKT pathway has been reported to induce chemotherapeutic resistance in TNBC. Moreover, our low-risk group showed higher TMBs. These findings indicate that our risk signature may play an important role in the development and therapeutic response of TNBC, while enabling exploring new TNBC therapeutic targets. Developments in immunotherapy have emphasized the importance of understanding the immune landscape within the TME.[36] TAMs are intimately involved in immunosuppression and can influence immunotherapy in many cancers; consequently, targeting TAM markers could enhance cancer immunotherapy.[37] Our study mainly analyzed the immune characteristics of the risk signature in TNBC, showing that immune-related pathways were highly enriched in the high-risk group. Furthermore, the high-expression group had significantly higher immune scores. The ssGSEA and CIBERSORT methods revealed significant differences in the immune cell scores and correlations between the two risk groups. Additionally, the high-risk group exhibited higher expression levels of TRS, CYT, and IFN-γ, representing a more immunoreactive microenvironment. Subsequently, the immune checkpoint genes, including PD1, PD-L1, and CTLA4, were more expressed in the high-risk group. Our results highlight the important immunological value of our risk signature. Therefore, we explored potential immunotherapeutic drugs based on this signature and identified 9 CTRP- and 21 PRISM-derived agents. CTRP-derived GDC-0941 is a PI3K inhibitor. PI3K is an important component that is aberrant in signaling pathways related to various cellular biological functions in several malignancies, including breast cancer.[38] Despite previous findings indicating the restricted therapeutic efficacy of GDC-0941 in TNBC, recent in vitro investigations have proposed that its co-administration with third-generation retinoid adapalene could enhance TNBC responsiveness to GDC, suppressing tumor growth, and decreasing treatment resistance.[38, 39] Our risk signature may provide novel strategies for the improvement of existing immunotherapies and enable exploration TNBC immunotherapies.

Our study has some limitations. The risk signature was constructed using mainly retrospective data; prospective studies need to be conducted. Our experiments focused on histological and cellular aspects; animal-level experiments should be conducted in the future. Finally, the mechanism underlying the contribution of the risk signature to prognosis and immunity was not elucidated; this shall be further investigated using both bioinformatics and experiments.

Collectively, this study is an important attempt to propose TAM cluster-related risk signatures in TNBC, providing evidence for identifying independent prognostic factors and effective therapeutic targets and updating immunotherapy methods.

Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Bioethics Committee of the Medical University of Nantong University (approval no. 2020-070)

Consent to participate

Informed consent was obtained from all individual participants included in the study.

Consent to publish

Additional informed consent was obtained from all individual participants for whom identifying information is included in this article.

Competing interests

The authors have no conflicts of interest to declare.

Authors' contributions

QN, SM, CB, designed the research and interpreted the results. SM conducted the experiments. SM, CB, and SW wrote the manuscript. All authors read and approved the final manuscript.

Funding

This article was funded by Nantong Municipal Science and Technology Bureau.

Availability of data and materials

All the data supporting the findings of this study are available within the article and its supplementary information files and from the corresponding authors upon reasonable request.

Nolan E, Lindeman GJ, Visvader JE (2023) Deciphering breast cancer: from biology to 1. Nolan, E., G.J. Lindeman, and J.E. Visvader, Deciphering breast cancer: from biology to the clinic. Cell, 186(8): p. 1708–1728
Leon-Ferre RA, Goetz MP (2023) Advances in systemic therapies for triple negative breast cancer. BMJ 381:e071674
Liedtke C et al (2023) Response to Neoadjuvant Therapy and Long-Term Survival in Patients With Triple-Negative Breast Cancer. J Clin Oncol 41(10):1809–1815
Liu Y et al (2023) Subtyping-based platform guides precision medicine for heavily pretreated metastatic triple-negative breast cancer: The FUTURE phase II umbrella clinical trial. Cell Res 33(5):389–402
Oliveira G, Wu CJ (2023) Dynamics and specificities of T cells in cancer immunotherapy. Nat Rev Cancer 23(5):295–316
Agnello L et al (2023) Aptamer-Based Strategies to Boost Immunotherapy in TNBC. Cancers (Basel), 15(7)
Tarantino P et al (2022) Investigational immunomodulatory drugs for enhancement of triple negative breast cancer (TNBC) immunotherapy: early phase development. Expert Opin Investig Drugs 31(6):499–513
He S et al (2023) Modulation of miR-146b by N6-methyladenosine modification remodels tumor-associated macrophages and enhances anti-PD-1 therapy in colorectal cancer. Cell Oncol (Dordr)
Wu H et al (2023) Model for predicting immunotherapy based on M2 macrophage infiltration in TNBC. Front Immunol 14:1151800
Sami E et al (2020) The Immunosuppressive Microenvironment in BRCA1-IRIS-Overexpressing TNBC Tumors Is Induced by Bidirectional Interaction with Tumor-Associated Macrophages. Cancer Res 80(5):1102–1117
Timperi E et al (2022) Lipid-Associated Macrophages Are Induced by Cancer-Associated Fibroblasts and Mediate Immune Suppression in Breast Cancer. Cancer Res 82(18):3291–3306
Mariathasan S et al (2018) TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554(7693):544–548
Butler A et al (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36(5):411–420
Chi H et al (2022) Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma. Front Endocrinol (Lausanne) 13:1056310
Ritchie ME et al (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43(7):e47
Chen B et al (2018) Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol Biol 1711:243–259
Danilova L et al (2019) Programmed Cell Death Ligand-1 (PD-L1) and CD8 Expression Profiling Identify an Immunologic Subtype of Pancreatic Ductal Adenocarcinomas with Favorable Survival. Cancer Immunol Res 7(6):886–895
Charoentong P et al (2017) Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep 18(1):248–262
Zhang S et al (2019) A novel and validated nomogram to predict overall survival for gastric neuroendocrine neoplasms. J Cancer 10(24):5944–5954
Liu Y et al (2021) Tumor-targeted gene therapy with lipid nanoparticles inhibits tumor-associated adipocytes and remodels the immunosuppressive tumor microenvironment in triple-negative breast cancer. Nanoscale Horiz 6(4):319–329
Sherman M, Beatty G (2022) Tumor Microenvironment in Pancreatic Cancer Pathogenesis and Therapeutic Resistance. Annual review of pathology
Xiang X et al (2021) Targeting tumor-associated macrophages to synergize tumor immunotherapy. Signal Transduct Target Ther 6(1):75
Qiu X et al (2022) Tumor-Associated Macrophages: Key Players in Triple-Negative Breast Cancer. Front Oncol 12:772615
Zhang H et al (2021) High Expression of Complement Component C7 Indicates Poor Prognosis of Breast Cancer and Is Insensitive to Taxane-Anthracycline Chemotherapy. Front Oncol 11:724250
Duan J et al (2022) Targeted core-shell nanoparticles for precise CTCF gene insert in treatment of metastatic breast cancer. Bioact Mater 11:1–14
Cheng Y, Heng X, Feng F (2022) G-protein Coupled Receptor 34 Promotes Gliomagenesis by Inducing Proliferation and Malignant Phenotype via TGF-Beta/Smad Signaling Pathway. Technol Cancer Res Treat 21:15330338221105733
Schöneberg T et al (2018) The G protein-coupled receptor GPR34 - The past 20 years of a grownup. Pharmacol Ther 189:71–88
Wang M et al (2021) MicroRNA-300 Inhibits the Proliferation and Metastasis of Cervical Cancer Cells via Posttranscriptional Suppression of G Protein-Coupled Receptor 34 (GPR34). J Oncol, 2021: p. 2669822
Iida Y et al (2014) Lysophosphatidylserine stimulates chemotactic migration of colorectal cancer cells through GPR34 and PI3K/Akt pathway. Anticancer Res 34(10):5465–5472
Korona B et al (2022) GPR34 activation potentially bridges lymphoepithelial lesions to genesis of salivary gland MALT lymphoma. Blood 139(14):2186–2197
Whitman M et al (1985) Association of phosphatidylinositol kinase activity with polyoma middle-T competent for transformation. Nature 315(6016):239–242
Vasan N, Cantley LC (2022) At a crossroads: how to translate the roles of PI3K in oncogenic and metabolic signalling into improvements in cancer therapy. Nat Rev Clin Oncol 19(7):471–485
Samuels Y et al (2004) High frequency of mutations of the PIK3CA gene in human cancers. Science 304(5670):554
Eustace AJ et al (2022) Aberrant calcium signalling downstream of mutations in TP53 and the PI3K/AKT pathway genes promotes disease progression and therapy resistance in triple negative breast cancer. Cancer Drug Resist 5(3):560–576
Wang W et al (2022) MAPK4 promotes triple negative breast cancer growth and reduces tumor sensitivity to PI3K blockade. Nat Commun 13(1):245
Zheng Y et al (2020) Immune suppressive landscape in the human esophageal squamous cell carcinoma microenvironment. Nat Commun 11(1):6268
Binnewies M et al (2021) Targeting TREM2 on tumor-associated macrophages enhances immunotherapy. Cell Rep 37(3):109844
Mehraj U et al (2022) Adapalene inhibits the growth of triple-negative breast cancer cells by S-phase arrest and potentiates the antitumor efficacy of GDC-0941. Front Pharmacol 13:958443
Mehraj U et al (2022) Expression Pattern and Prognostic Significance of Chemokines in Breast cancer: An Integrated Bioinformatics Analysis. Clin Breast Cancer 22(6):567–578

No competing interests reported.

TABLES1.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Prognostic correlation and immune characteristics of a TAM cluster- related 8-gene risk signature in triple-negative breast cancer

Status:

Version 1

Abstract

Figures

Introduction

Materials and methods

Data acquisition and processing

Definition of TAM

TAM hub genes identification

Immune landscape analysis

Construction of a risk signature and nomogram

RNA extraction and reverse-transcriptase polymerase chain reaction

Western blot

Immunohistochemistry

Statistical analysis

Results

TAM screening in scRNA-seq samples

Cancer-related pathways expression in TAMs

Identification of hub genes associated with TAM

Mutation and pathway analysis of the hub genes

Hub genes and immunity association

Correlation between the 8-gene signature and immune-related characteristics

Independent risk factors and potential therapeutic agents identification

GPR34 mRNA and protein expression in TNBC

Discussion

Declarations

Ethics approval

References

Additional Declarations

Supplementary Files

Status:

Version 1