Infiltration In Breast Cancer Research Articles

PTPRO-related CD8+ T-cell signatures predict prognosis and immunotherapy response in patients with breast cancer

BackgroundPoor immunogenicity and extensive immunosuppressive T-cell infiltration in the tumor immune microenvironment (TIME) have been identified as potential barriers to immunotherapy success in “immune-cold” breast cancers. Thus, it is crucial to identify biomarkers that can predict immunotherapy efficacy. Protein tyrosine phosphatase receptor type O (PTPRO) regulates multiple kinases and pathways and has been implied to play a regulatory role in immune cell infiltration in various cancers.MethodsESTIMATE and single-sample gene set enrichment analysis (ssGSEA) were performed to uncover the TIME landscape. The correlation analysis of PTPRO and immune infiltration was performed to characterize the immune features of PTPRO. Univariate and multivariate Cox analyses were applied to determine the prognostic value of various variables and construct the PTPRO-related CD8+ T-cell signatures (PTSs). The Kaplan–Meier curve and the receiver operating characteristic (ROC) curve were used to estimate the performance of PTS in assessing prognosis and immunotherapy response in multiple validation datasets.ResultsHigh PTPRO expression was related to high infiltration levels of CD8+ T cells, as well as macrophages, activated dendritic cells (aDCs), tumor-infiltrating lymphocytes (TILs), and Th1 cells. Given the critical role of CD8+ T cells in the TIME, we focused on the impact of PTPRO expression on CD8+ T-cell infiltration. The prognostic PTS was then constructed using the TCGA training dataset. Further analysis showed that the PTS exhibited favorable prognostic performance in multiple validation datasets. Of note, the PTS could accurately predict the response to immune checkpoint inhibitors (ICIs).ConclusionPTPRO significantly impacts CD8+ T-cell infiltration in breast cancer, suggesting a potential role of immunomodulation. PTPRO-based PTS provides a new immune cell paradigm for prognosis, which is valuable for immunotherapy decisions in cancer patients.

Prognostic value of 12 m7G methylation-related miRNA markers and their correlation with immune infiltration in breast cancer.

RNA guanine-7 methyltransferase (RNMT), in complex with FAM103A1, plays an important role in tumorigenesis and development. The aim of this study was to establish a prognostic model of RNMT and FAM103A1-based upstream microRNAs and explore its correlation with immune cell infiltration in breast cancer (BC) while investigating its potential prognostic value and verify the model by quantitative real-time polymerase chain reaction (qRT-PCR). The miRNA expression data upstream of the m7G methyltransferase complex RNMT/FAM103A1 in BC was obtained from The Cancer Genome Atlas and TargetScan databases. We performed univariate Cox regression, LASSO regression, Kaplan-Meier survival, and principal component analyses, along with risk prognostic modelling. Based on multivariate Cox regression analysis, a total of 12 m7G methyltransferase-related miRNAs were found. The model showed good accuracy for predicting the 1-, 3-,5-, and 10-year survival rates, and the areas under the curve were almost >0.7. To characterize the risk-level model constructed from 12 miRNAs, 12 differentially expressed mRNAs related to prognosis and immune infiltration were obtained. The prognosis of BC patients is well predicted by the risk model we constructed. This model is also closely related to immune infiltration, and new immunotherapy targets can be explored from this field.

Bioinformatics Prediction and in vivo Verification Identify SLC7A5 as Immune Infiltration Related Biomarker in Breast Cancer.

PurposeSolute carrier family 7, member 5 (SLC7A5) is reportedly a key gene in various tumors. However, the relationship between SLC7A5 and immune cell infiltration in breast cancer remains elusive. In this study, we investigated the expression levels and clinical value of SLC7A5 using the R package in breast cancer through the TCGA database.MethodsPublic transcript data and clinical information of patients were downloaded from the TCGA database. Prognosis analysis was performed to explore the clinical value of SLC7A5 as well as drug sensitivity prediction and Gene Set Enrichment Analysis (GSEA). We also explored the correlations between SLC7A5 and immune infiltration as well as immune markers.ResultsWe observed that high SLC7A5 expression levels were related to poor survival. Our GSEA demonstrated that G protein-coupled receptor-ligand binding, interleukin signaling, neutrophil degranulation, amino acid and derivative metabolism, and Rho GTPase signaling were differentially enriched in the SLC7A5 high-expression phenotype. Patients in the low SLC7A5 expression group showed sensitivity to paclitaxel and doxorubicin.ConclusionSLC7A5 could serve as a biomarker for breast cancer prognosis.

Hub genes associated with immune cell infiltration in breast cancer, identified through bioinformatic analyses of multiple datasets

Objective:The aim of this study was to identify hub genes associated with immune cell infiltration in breast cancer through bioinformatic analyses of multiple datasets.Methods:Nonparametric (NOISeq) and robust rank aggregation-ranked parametric (EdgeR) methods were used to assess robust differentially expressed genes across multiple datasets. Protein-protein interaction network, GO, KEGG enrichment, and sub-network analyses were performed to identify immune-associated hub genes in breast cancer. Immune cell infiltration was evaluated with the CIBERSORT, XCELL, and TIMER methods. The association between the hub gene-based risk signature and survival was determined through Kaplan–Meier survival analysis, multivariate Cox analysis, and a nomogram with external verification.Results:We identified 163 robust differentially expressed genes in breast cancer through applying both nonparametric and parametric methods to multiple GEO (n = 2,212) and TCGA (n = 1,045) datasets. Integrated bioinformatic analyses further identified 10 hub genes: CXCL10, CXCL9, CXCL11, SPP1, POSTN, MMP9, DPT, COL1A1, ADAMDEC1, and RGS1. The 10 hub-gene-based risk signature significantly correlated with the prognosis of patients with breast cancer. Moreover, these hub genes were strongly associated with the extent of infiltration of CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and myeloid dendritic cells into breast tumors.Conclusions:Integrated analyses of multiple databases led to the discovery of 10 robust hub genes that together may serve as a risk factor characteristic of the immune microenvironment in breast cancer.

Identification of UCP1 and UCP2 as Potential Prognostic Markers in Breast Cancer: A Study Based on Immunohistochemical Analysis and Bioinformatics.

Background: Uncoupling protein 1 (UCP1) and UCP2 are associated with tumor metabolism and immunity. However, the prognostic value and molecular mechanisms underlying their action in breast cancer (BC) remain unclear. Materials and methods: In TCGA-BRCA cohort, we investigated the expression characteristics of UCP mRNAs, analyzed their prognostic value by Kaplan-Meier survival analysis, their potential molecular functions by gene set enrichment analysis, and their relationship with immune infiltrating cell types using TIMER and CIBERSORT, along with the assessment of their association with mutational profiles. Kaplan-Meier survival analysis was performed for UCPs in our cohort and their association with BC thermogenesis was assessed by thermal tomography. Results: High expression of UCP1 and UCP2 were positive prognostic markers for BC. UCP1 was associated with the impaired glucose metabolism, while UCP2 with enhanced anti-tumor immunity. High expressions of UCP1 and UCP2 were associated with CDH1 mutations. High UCP1 expression was associated with a high rate of thermogenesis in BC. Conclusions: These results implied a key role of UCP1 and UCP2 in prognosis, metabolism, and immune infiltration in BC. Further investigation of the relevant molecular mechanisms may provide new strategies for BC treatment.

Total glucosides of paeony inhibit breast cancer growth by inhibiting TAMs infiltration through NF-κB/CCL2 signaling

PurposeThe high density of tumor-associated macrophages (TAMs) and inflammatory factors are crucial elements leading to tumor immune tolerance. Previously, we found that total glucosides of paeony (TGP) have strong inhibitory effects on the release of various inflammatory factors; however, it is unclear whether the inhibitory effects can improve the inflammatory microenvironment of tumors. Therefore, in the present study, we investigated the mechanism via which TGP depresses tumor growth and metastasis via modulation of TAM infiltration in breast cancer. MethodsWe assessed the effects of TGP on various mouse models of tumor. Lung metastasis was detected using hematoxylin and eosin staining. T cell (CD3+CD4+ and CD3+CD8+) effector and memory subsets, and TAM (CD45+CD11b+F4/80+) populations in the tumor microenvironment (TME) were examined using flow cytometry. Lipopolysaccharide (LPS)-stimulated macrophage experiments were used to investigate the TGP anti-inflammatory effects in vitro. Furthermore, conditional medium (CM) was added to detect 4T1 breast cancer cell growth using a Real-Time Cell Analyzer (RTCA) xCELLigence system. Inflammatory cytokine and chemokine levels were measured using cytometric bead array (CBA) kits and quantitative polymerase chain reaction (qPCR). NF-κB expression in the nucleus was examined by immunofluorescence and Western blot analysis. ResultsTGP suppressed tumor growth and lung metastasis, decreased CD45+CD11b+F4/80+ (TAMs) population obviously, and increased CD44LowCD62LHi (T memory stem cells) and CD44HiCD62LHi (central memory cells) populations in the tumor-infiltrating CD4+ and CD8+ T cells. In addition, TGP reduced inflammatory factor levels in tumors, thus inhibiting the infiltration of TAMs to improve the inflammation immunosuppressive microenvironment. In the in vitro experiment, TGP inhibited IL-10 and C-C Motif Chemokine Ligand 2 (CCL2) secretion and mRNA expression in LPS-stimulated macrophages to inhibit 4T1 cell growth and restrain macrophages M2 polarization. In addition, TGP can directly inhibit 4T1 cell proliferation by restraining autocrine CCL2 and IL-10. Further mechanistic studies reavealed that TGP inhibited CCL2 secretion by inhibiting NF-κB accumulation in the nucleus in macrophages. ConclusionTGP reduced TAM recruitment mainly through the NF-κB/CCL2 signaling pathway, thereby promoting T cell infiltration in the TME. TGP has a unique advantage in balancing the inflammatory response. Furthermore, our results present novel insights on the mechanisms underlying TAM infiltration that were inhibited by TGP, with potential application in development of novel therapies targeting CCL2 pathways.

CLEC10A can serve as a potential therapeutic target and its level correlates with immune infiltration in breast cancer.

Breast cancer (BC) is one of the most common malignant cancers in females worldwide and greatly threatens women's health. The C-type lectin domain family 10 member A (CLEC10A) is a member of the C-type lectin receptor family that has been previously reported to promote the antitumor activity of immune cells. In the present study, the potential prognostic value of CLEC10A expression in BC was assessed using data from The Cancer Genome Atlas online database. Differences in the mRNA expression levels of CLEC10A between BC and normal tissues were then analyzed using the Tumor Immune Estimation Resource (TIMER) platform and the University of Alabama at Birmingham Cancer data analysis portal. Reverse transcription-quantitative PCR was performed to validate the results of this analysis. The Kaplan-Meier plotter database was used to evaluate the association between the mRNA expression levels of CLEC10A and clinical prognosis of BC. Based on the association between the mRNA expression levels of CLEC10A and the tumor immune microenvironment, the TIMER platform and the Tumor and Immune System Interaction Database website were utilized to assess the correlation between CLEC10A expression and the degree of tumor immune cell infiltration. The present study revealed that CLEC10A expression was significantly lower in BC tissues compared with that in normal tissues, which was in turn associated with poorer clinical outcomes. This suggested that lower CLEC10A expression levels were associated with unfavorable prognosis in BC. In addition, the expression level of CLEC10A was found to be positively associated with the level of different tumor-infiltrating immune cells in BC, including CD8 T cells, B cells, macrophages and NK cells which, was in turn closely correlated with some gene markers such as CD19, CD8A, KIR2DS4 and PTGS2. These results suggest that the relationship between lower CLEC10A expression level and poor prognosis in BC may be due to the role of CLEC10A in the tumor immune microenvironment. In conclusion, CLEC10A may be a potential biomarker that can be used to efficiently predict prognosis in patients with BC.

Identification of pyroptosis related subtypes and tumor microenvironment infiltration characteristics in breast cancer

Understanding the association of pyroptosis with tumor progression, prognosis and effect on immunotherapeutic response in breast cancer (BC) is limited. This study analysed forty pyroptosis-related genes to construct the pyroptosis score. Association of the pyroptosis score with the overall survival, clinical features, tumor mutation load, immune cell infiltration, and treatment sensitivity of patients with BC was analysed. Out of 983 BC samples, 304 (30.93%) had genetic alterations with the highest TP53 frequency. We identified three separate subtypes associated with pyroptosis action. These subtypes correlate with the clinicopathological characteristics, TME immune cell infiltration, and disease prognosis. Based on the expression levels of the pyroptosis genes, we divided the pyroptosis score into a high group and a low group. The immune-activated pyroptosis subtype had a higher score with a better prognosis. We also observed that the pyroptosis score correlates with the tumor mutation burden. The pyroptosis score and disease prognosis were directly proportional. A higher pyroptosis score indicated a better prognosis. Results suggest that the pyroptosis-related gene prognosis model is closely related to the immune cell infiltration of BC. The three pyroptosis subtypes associated with BC assist in accurately identifying the tumor subtype, the prognosis of immunotherapy drugs and the patient’s therapeutic response.

NTN4 as a prognostic marker and a hallmark for immune infiltration in breast cancer

Netrin-4 (NTN4), a member of neurite guidance factor family, can promote neurite growth and elongation. This study aims to investigate if NTN4 correlates with prognosis and immune infiltration in breast cancer. The prognostic landscape of NTN4 and its relationship with immune infiltration in breast cancer were deciphered with public databases and immunohistochemistry (IHC) in tissue samples. The expression profiling and prognostic value of NTN4 were explored using UALCAN, TIMER, Kaplan–Meier Plotter and Prognoscan databases. Based on TIMER, relationships of NTN4 expression with tumor immune invasion and immune cell surface markers were evaluated. Transcription and survival analyses of NTN4 in breast cancer were investigated with cBioPortal database. The STRING database was explored to identify molecular functions and signaling pathways downstream of NTN4. NTN4 expression was significantly lower in invasive breast carcinoma compared with adjacent non-malignant tissues. Promoter methylation of NTN4 exhibited different patterns in breast cancer. Low expression of NTN4 was associated with poorer survival. NTN4 was significantly positively related to infiltration of CD8+ T cells, macrophages and neutrophils, whereas significantly negatively related to B cells and tumor purity. Association patterns varied with different subtypes. Various associations between NTN4 levels and immune cell surface markers were revealed. Different subtypes of breast cancer carried different genetic alterations. Mechanistically, NTN4 was involved in mediating multiple biological processes including morphogenesis and migration.

Abstract 456: Jointly leveraging spatial transcriptomics and deep learning models for image annotation achieves better-than-pathologist performance in cell type identification in tumors

Abstract For over 100 years, the traditional tools of pathology, such as tissue-marking dyes (e.g. the H&E stain) have been used to study the disorganization and dysfunction of cells within tissues. This has represented a principal diagnostic and prognostic tool in cancer. However, in the last 5 years, new technologies have promised to revolutionize histopathology, with Spatial Transcriptomics technologies allowing us to measure gene expression directly in pathology-stained tissue sections. In parallel with these developments, Artificial Intelligence (AI) applied to histopathology tissue images now approaches pathologist level performance in cell type identification. However, these new technologies still have severe limitations, with Spatial Transcriptomics suffering difficulties distinguishing transcriptionally similar cell types, and AI-based pathology tools often performing poorly on real world out-of-batch test datasets. Thus, century-old techniques still represent standard-of-care in most areas of clinical cancer diagnostics and prognostics. Here, we present a new frontier in digital pathology: describing a conceptually novel computational methodology, based on Bayesian probabilistic modelling, that allows Spatial Transcriptomics data to be leveraged together with the output of deep learning-based AI used to computationally annotate H&E-stained sections of the same tumor. By leveraging cell-type annotations from multiple independent pathologists, we show that this integrated methodology achieves better performance than any given pathologist’s manual tissue annotation in the task of identifying regions of immune cell infiltration in breast cancer, and easily outperforms either technology alone. We also show that on a subset of histopathology slides examined, the methodology can identify regions of clinically relevant immune cell infiltration that were missed entirely by an initial pathologist’s manual annotation. While this use case has clear diagnostic and prognostic value in cancer (e.g. predicting response to immunotherapy), our methodology is generalizable to any type of pathology images and also has broad applications in spatial transcriptomics data analytics, where most applications (such as identifying cell-cell interactions) rely on correct cell type annotations having been established a priori. We anticipate that this work will spur many follow-up studies, including new computational innovations building on the approach. The work sets the stage for better-than-pathologist performance in other cell-type annotation tasks, with relevant applications in diagnostics and prognostics across almost all cancers. Citation Format: Asif Zubair, Rich Chapple, Sivaraman Natarajan, William C. Wright, Min Pan, Hyeong-Min Lee, Heather Tillman, John Easton, Paul Geeleher. Jointly leveraging spatial transcriptomics and deep learning models for image annotation achieves better-than-pathologist performance in cell type identification in tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 456.

Downregulation of ATP binding cassette subfamily a member 10 acts as a prognostic factor associated with immune infiltration in breast cancer.

e13012 Background: The human ATP binding cassette (ABC) family of transporter proteins plays an important role in the maintenance of homeostasis in vivo. The aim of this study is to evaluate the potential diagnostic, prognostic, and therapeutic value of the ABCA10 gene in BRCA. Methods: In this study, a multi-step process was used to construct an immune-related genes (IRGs) based signature leveraging the expression profiles and clinical characteristics of BRCA from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. An integrated immunogenomic analysis was performed to determine the association between IRGs with prognostic significance and cancer genotypes in the tumor immune microenvironment (TIME). Moreover, we performed a comprehensive in silico therapeutics screening to identify agents with subclass-specific efficacy. Results: We found that ABCA10 expression was downregulated in different subgroups of breast cancer and strongly correlated with pathological stage in BRCA patients. Low expression of ABCA10 was associated with BRCA patients showing shorter overall survival (OS). ABCA10 expression may be regulated by promoter methylation, copy number variation (CNV) and kinase, and is associated with immune infiltration. Our study also demonstrated the potential role of ABCA10 modifications in tumor microenvironment (TME) cellular infiltration. Nevertheless, the regulatory mechanism remains unknown and immunotherapy is marginal in BRCA. We demonstrate the expression of different ABCA10 modulators in breast cancer associated with genetic variants, deletions, tumor mutation burden (TMB) and TME. Mutations in ABCA10 are positively associated with different immune cells in six different immune databases. Mutations in ABCA10 and immune cell infiltration play an important role in breast cancer. Conclusions: Overall, this study provides evidence that ABCA10 could become the potential targets for precision treatment and new biomarkers in the prognosis of breast cancer.

CTHRC1 is a Potential Prognostic Biomarker and Correlated with Macrophage Infiltration in Breast Cancer.

BackgroundTumor immune cell infiltration is closely associated with the occurrence and development of tumors. Collagen triple helix repeats containing 1 (CTHRC1), a regulator of collagen expression and cell migration, is involved in the metastasis and invasion of tumors. However, the role of CTHRC1 in breast cancer remains unclear. This study aimed to investigate the prognostic value of CTHRC1, and further explore its association with immune infiltration in breast cancer.MethodsCTHRC1 expression pattern and prognostic value were analyzed using ONCOMINE, PrognoScan, GEPIA, and Kaplan–Meier Plotter databases. We then detected CTHRC1 mRNA levels in breast cancer tissues and paired normal breast tissues by Q-PCR. Subsequently, the University of California Santa Cruz (UCSC) database was used to determine the methylation status of CTHRC1. Furthermore, CTHRC1 mutations were investigated using the Catalogue of Somatic mutations in Cancer (COSMIC) and cBioPortal databases. We also assessed the correlation between CTHRC1 expression and immune cell infiltration using TIMER. In addition, The relationship of CTHRC1 expression with the immune marker sets of various immune cells was evaluated using GEPIA and TIMER.ResultsCTHRC1 was highly expressed in a variety of tumors, including breast cancer. Elevated CTHRC1 expression was related to a poor prognosis. Notably, CTHRC1 expression was significantly associated with macrophage infiltration, especially the immune infiltration gene marker set of M2. Copy number variations, DNA mutations and methylation states might be potential mechanisms for regulating CTHRC1 expression. Protein digestion and absorption, human papillomavirus infection, ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathways were identified as the potential CTHRC1-driven signaling pathways.ConclusionThese findings suggest that CTHRC1 could be a promising immune-related biomarker for the treatment of breast cancer patients.

Identification of Angiogenesis-Related Prognostic Biomarkers Associated With Immune Cell Infiltration in Breast Cancer.

Background: This study aimed to explore the prognostic value of angiogenesis-related genes (ARGs) and their association with immune cell infiltration (ICI) in breast cancer (BC). Methods: Transcriptome data of BC were obtained from the TCGA and GEO databases. Differentially expressed ARGs were identified by the limma package. The identification of key genes and construction of the risk score model were performed by univariate and multivariate Cox regression algorithms. The prognostic value of the risk score was assessed by ROC curves and nomogram. GO, KEGG pathway, and GSEA were used to investigate the biological functions of differentially expressed genes (DEGs), and CIBERSORT, ssGSEA, and xCell algorithms were performed to estimate the ICI in high-risk and low-risk groups. The correlations between prognostic biomarkers and differentially distributed immune cells were assessed. Moreover, a ceRNA regulatory network based on prognostic biomarkers was constructed and visualized by Cytoscape software. Results: A total of 18 differentially expressed ARGs were identified between tumor and adjacent normal tissue samples. TNFSF12, SCG2, COL4A3, and TNNI3 were identified as key prognostic genes by univariate and multivariate Cox regression analyses. The risk score model was further constructed based on the four-gene signature and validated in GSE7390 and GSE88770 datasets. ROC curves and nomogram indicated that the risk score had good accuracy for determining BC patient survival. Biological function analysis showed that DEGs in high- and low-risk groups had a high enrichment in immune-related biological processes and signaling pathways. Moreover, significantly different ICIs were found between high- and low-risk groups, such as memory B cells, CD8+ T cells, resting memory CD4+ T cells, follicular helper T cells, regulatory T cells, monocytes, M2 macrophages, and neutrophils, and each prognostic biomarker was significantly correlated with one or more immune cell types. Conclusion: The current study identified novel prognostic ARGs and developed a prognostic model for predicting survival in patients with BC. Furthermore, this study indicated that ICI may act as a bond between angiogenesis and BC. These findings enhance our understanding of angiogenesis in BC and provide novel guidance on developing therapeutic targets for BC patients.

PCMT1 Is a Potential Prognostic Biomarker and Is Correlated with Immune Infiltrates in Breast Cancer.

Background Protein-L-isoaspartate (D-aspartate) O-methyltransferase (PCMT1) is involved in the occurrence and development of a variety of malignant tumors. However, the prognostic value of PCMT1 in breast cancer remains unclear. Methods Based on the Cancer Genome Atlas database, we assessed the correlation between the expression of PCMT1 and prognosis, immune invasion, and tumor mutation burden in a variety of cancers. The expression level, mutation, immune correlation, and coexpression of PCMT1 in breast cancer were studied using the following databases: UALCAN database, Human Protein Atlas database, cBioPortal database, TIMER database, and LinkedOmics database. Kaplan–Meier Plotter was used for survival analysis. Receiver operating characteristic (ROC) curves and nomograms were drawn using the R software package. P < 0.05 was considered statistically significant. Results Pancancer analysis showed that PCMT1 is highly expressed in a variety of cancers and is significantly related to the prognosis of a variety of cancers. PCMT1 is significantly related to the tumor mutation burden of a variety of cancers. PCMT1 is significantly high in breast cancer, and it is significantly related to the abundance of immune infiltration. Survival analysis revealed that high PCMT1 expression is significantly associated with shorter overall survival (OS), relapse-free survival (RFS), and postprogression survival (PPS) in breast cancer patients. ROC curves and nomograms verify the effectiveness of PCMT1 as a prognostic biomarker for breast cancer. Conclusions PCMT1 can be used as a potential prognostic biomarker of breast cancer, and it is significantly related to the abundance of breast cancer immune infiltration.

Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer

BackgroundBreast cancer (BC) is the most common malignant tumor worldwide. Apoptosis and hypoxia are involved in the progression of BC, but reliable biomarkers for these have not been developed. We hope to explore a gene signature that combined apoptosis and hypoxia‐related genes (AHGs) to predict BC prognosis and immune infiltration.MethodsWe collected the mRNA expression profiles and clinical data information of BC patients from The Cancer Genome Atlas database. The gene signature based on AHGs was constructed using the univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analysis. The associations between risk scores, immune infiltration, and immune checkpoint gene expression were studied using single‐sample gene set enrichment analysis. Besides, gene signature and independent clinicopathological characteristics were combined to establish a nomogram. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on the potential functions of AHGs.ResultsWe identified a 16‐AHG signature (AGPAT1, BTBD6, EIF4EBP1, ERRFI1, FAM114A1, GRIP1, IRF2, JAK1, MAP2K6, MCTS1, NFKBIA, NFKBIZ, NUP43, PGK1, RCL1, and SGCE) that could independently predict BC prognosis. The median score of the risk model divided the patients into two subgroups. By contrast, patients in the high‐risk group had poorer prognosis, less abundance of immune cell infiltration, and expression of immune checkpoint genes. The gene signature and nomogram had good predictive effects on the overall survival of BC patients. GO and KEGG analyses revealed that the differential expression of AHGs may be closely related to tumor immunity.ConclusionWe established and verified a 16‐AHG BC signature which may help predict prognosis, assess potential immunotherapy benefits, and provide inspiration for future research on the functions and mechanisms of AHGs in BC.

Clinical Impact of 11q13.3 Amplification on Immune Cell Infiltration and Prognosis in Breast Cancer.

IntroductionAmplification of the 11q13.3 locus has been observed in various tumors. This study sought to determine the correlation of gene amplification at the 11q13.3 locus with the immune status and survival of breast cancer.MethodsAmplification of the 11q13.3 locus was characterized by analyzing a publicly available database from the cBioPortal platform (TCGA). The correlation of amplified genes with immune cell infiltration in breast cancer was further analyzed using the TIMER2.0 platform. Immunohistochemical staining was used to determine the expression levels of Cyclin D1 (CCND1), Fas-associated death domain (FADD) and P53 in 156 clinical breast cancer samples.ResultsThis study revealed that amplification of the 11q13.3 amplicon in breast cancer is likely more frequently detected in luminal B breast cancer. Moreover, high expression or amplification of CCND1, fibroblast growth factor 3 (FGF3), fibroblast growth factor 4 (FGF4), fibroblast growth factor 19 (FGF19) and FADD was inversely correlated with the abundance of CD4+ T cells and dendritic cell infiltration in breast cancer (P < 0.05). Data analysis also demonstrated that high expression of CCND1, FGF4 and FADD mRNA levels was closely correlated with shorter recurrence-free survival (RFS) in patients with breast cancer (P < 0.05). The results of immunohistochemical staining from clinical samples further confirmed that high expression of CCND1 and FADD was frequently detected in luminal B and high-grade breast cancer with shorter metastasis-free survival times (P < 0.05).ConclusionThis study demonstrated that coamplification of genes located on the 11q13.3 amplicon is frequently detected in luminal B subtype breast cancer and is closely associated with worse survival in patients with breast cancer. Moreover, coamplification of the CCND1-FGF locus might decrease antitumor immune activity in breast cancer, indicating that coamplification of the 11q13.3 amplicon is likely to be a key determinant of therapeutic resistance and accelerate the aggressive evolution of breast cancer.

Integrative Analysis of the Expression Levels and Prognostic Values for NEK Family Members in Breast Cancer.

Background: In the latest rankings, breast cancer ranks first in incidence and fifth in mortality among female malignancies worldwide. Early diagnosis and treatment can improve the prognosis and prolong the survival of breast cancer (BC) patients. The NIMA-related kinase (NEK), a group of serine/threonine kinase, is a large and conserved gene family that includes NEK1–NEK11. The NEK plays a pivotal role in the cell cycle and microtubule formation. However, an integrative analysis of the effect and prognosis value of NEK family members on BC patients is still lacking. Methods: In this study, the expression profiles of NEK family members in BC and its subgroups were analyzed using UALCAN, GEPIA2, and Human Protein Atlas datasets. The prognostic values of NEK family members in BC were evaluated using the Kaplan–Meier plotter. Co-expression profiles and genetic alterations of NEK family members were analyzed using the cBioPortal database. The function and pathway enrichment analysis of the NEK family were performed using the WebGestalt database. The correlation analysis of the NEK family and immune cell infiltration in BC was conducted using the TIMER 2.0 database. Results: In this study, we compared and analyzed the prognosis values of the NEKs. We found that NEK9 was highly expressed in normal breast tissues than BC, and NEK2, NEK6, and NEK11 were significantly highly expressed in BC than adjacent normal tissues. Interestingly, the expression levels of NEK2, NEK6, and NEK10 were not only remarkably correlated with the tumor stage but also with the molecular subtype. Through multilevel research, we found that high expression levels of NEK1, NEK3, NEK8, NEK9, NEK10, and NEK11 suggested a better prognosis value in BC, while high expression levels of NEK2 and NEK6 suggested a poor prognosis value in BC. Conclusion: Our studies show the prognosis values of the NEKs in BC. Thus, we suggest that NEKs may be regarded as novel biomarkers for predicting potential prognosis values and potential therapeutic targets of BC patients.

Downregulation of ATP binding cassette subfamily a member 10 acts as a prognostic factor associated with immune infiltration in breast cancer.

The human ATP binding cassette (ABC) family of transporter proteins plays an important role in the maintenance of homeostasis in vivo. The aim of this study is to evaluate the potential diagnostic, prognostic, and therapeutic value of the ABCA10 gene in BRCA. We found that ABCA10 expression was downregulated in different subgroups of breast cancer and strongly correlated with pathological stage in BRCA patients. Low expression of ABCA10 was associated with BRCA patients showing shorter overall survival (OS). ABCA10 expression may be regulated by promoter methylation, copy number variation (CNV) and kinase, and is associated with immune infiltration. Our study also demonstrated the potential role of ABCA10 modifications in tumor microenvironment (TME) cellular infiltration. Nevertheless, the regulatory mechanism remains unknown and immunotherapy is marginal in BRCA. We demonstrate the expression of different ABCA10 modulators in breast cancer associated with genetic variants, deletions, tumor mutation burden (TMB) and TME. Mutations in ABCA10 are positively associated with different immune cells in six different immune databases and play an important role in immune cell infiltration in breast cancer. Overall, this study provides evidence that ABCA10 could become the potential targets for precision treatment and new biomarkers in the prognosis of breast cancer.

CCDC134 as a Prognostic-Related Biomarker in Breast Cancer Correlating With Immune Infiltrates.

BackgroundThe expression of Coiled-Coil Domain Containing 134(CCDC134) is up-regulated in different pan-cancer species. However, its prognostic value and correlation with immune infiltration in breast cancer are unclear. Therefore, we evaluated the prognostic role of CCDC134 in breast cancer and its correlation with immune invasion.MethodsWe downloaded the transcription profile of CCDC134 between breast cancer and normal tissues from the Cancer Genome Atlas (TCGA). CCDC134 protein expression was assessed by the Clinical Proteomic Cancer Analysis Consortium (CPTAC) and the Human Protein Atlas. Gene set enrichment analysis (GSEA) was also used for pathway analysis. Receiver operating characteristic (ROC) curve was used to differentiate breast cancer from adjacent normal tissues. Kaplan-Meier method was used to evaluate the effect of CCDC134 on survival rate. The protein-protein interaction (PPI) network is built from STRING. Function expansion analysis is performed using the ClusterProfiler package. Through tumor Immune Estimation Resource (TIMER) and tumor Immune System Interaction database (TISIDB) to determine the relationship between CCDC134 expression level and immune infiltration. CTD database is used to predict drugs that inhibit CCDC134 and PubChem database is used to determine the molecular structure of identified drugs.ResultsThe expression of CCDC134 in breast cancer tissues was significantly higher than that of CCDC134 mRNA expression in adjacent normal tissues. ROC curve analysis showed that the AUC value of CCDC134 was 0.663. Kaplan-meier survival analysis showed that patients with high CCDC134 had a lower prognosis (57.27 months vs 36.96 months, P = 2.0E-6). Correlation analysis showed that CCDC134 mRNA expression was associated with tumor purity immune invasion. In addition, CTD database analysis identified abrine, Benzo (A) Pyrene, bisphenol A, Soman, Sunitinib, Tetrachloroethylene, Valproic Acid as seven targeted therapy drugs that may be effective treatments for seven targeted therapeutics. It may be an effective treatment for inhibiting CCDC134.ConclusionIn breast cancer, upregulated CCDC134 is significantly associated with lower survival and immune infiltrates invasion. Our study suggests that CCDC134 can serve as a biomarker of poor prognosis and a potential immunotherapy target in breast cancer. Seven drugs with significant potential to inhibit CCDC134 were identified.

Atlas of immune cell infiltration in breast cancer—high M2 macrophage and low native B cellproportions are associated with poor survival

Background: Knowledge of the tumor immune microenvironment is critical for developing new treatments targeting the immune system in breast cancer. However, no profile of immune cell infiltration in breast cancer has been provided. The aim of this study is to illustrate an atlas of the immune cell microenvironment of breast cancer.