The Role of CXCL13 and CXCL9 in Early Breast Cancer

Senomorphic activity of a combination of niacinamide and hyaluronic acid: correlation with clinical improvement of skin aging

Intrinsic and extrinsic factors, including lifestyle and sun exposure, can contribute to cell senescence, which impairs skin homeostasis, that may in turn lead to skin aging. Senescent cells have a specific secretome, called the senescence-associated secretory phenotype (SASP) that includes MMPs, CXCLs and S100A8/9. Reducing the SASP with senotherapeutics is a promising strategy to reduce skin aging. Here we evaluated the effect of a formula containing niacinamide and hyaluronic acid, which are known to limit senescence and skin aging. We conducted three different studies. (1) Ex vivo explants treated with the formula had more collagen and glycosaminoglycan. (2) In a clinical trial with forty-four women, two months of treatment improved fine lines, wrinkles, luminosity, smoothness, homogeneity, and plumpness. (3) In a third study on thirty women, we treated one arm for two months and took skin biopsies to study gene expression. 101 mRNAs and 13 miRNAs were differentially expressed. We observed a likely senomorphic effect, as there was a decrease in many SASP genes including MMP12 and CXCL9 and a significant downregulation of autocrine signaling genes: S100A8 and S100A9. These pharmaco-clinical results are the first to demonstrate the senomorphic properties of an effective anti-aging formula in skin.

Quantifying spatial CXCL9 distribution with image analysis predicts improved prognosis of triple-negative breast cancer

Background: The C-X-C motif chemokine ligand 9 (CXCL9) plays a pivotal role in tumor immunity by recruiting and activating immune cells. However, the relationship between CXCL9 expression and prognosis in triple-negative breast cancer (TNBC) is unclear. Methods: We investigated CXCL9 mRNA expression, clinicopathological features, and prognosis in TNBC patients. We also used computational image analysis to quantify and assess the distribution of CXCL9 protein in the tumor core (TC) and invasive margin (IM). Results: CXCL9 mRNA expression was significantly higher in TNBC tumors compared to normal tissue (p < 0.001) and was associated with smaller tumors (p = 0.022) and earlier stages (p = 0.033). High CXCL9 mRNA expression was correlated with improved overall survival (OS) in three independent cohorts (all p < 0.05). In a separate analysis, low CXCL9 protein expression was associated with increased lymph node metastasis (p = 0.018 and p = 0.036). High CXCL9 protein expression in the TC, IM, or both was associated with prolonged OS (all p < 0.001). Conclusion: High CXCL9 expression, at both the mRNA and protein levels, is associated with improved prognosis in TNBC patients. CXCL9 expression in the TC and/or IM may be an independent prognostic factor.

Advances in the study of tertiary lymphoid structures in the immunotherapy of breast cancer

Breast cancer, as one of the most common malignancies in women, exhibits complex and heterogeneous pathological characteristics across different subtypes. Triple-negative breast cancer (TNBC) and HER2-positive breast cancer are two common and highly invasive subtypes within breast cancer. The stability of the breast microbiota is closely intertwined with the immune environment, and immunotherapy is a common approach for treating breast cancer.Tertiary lymphoid structures (TLSs), recently discovered immune cell aggregates surrounding breast cancer, resemble secondary lymphoid organs (SLOs) and are associated with the prognosis and survival of some breast cancer patients, offering new avenues for immunotherapy. Machine learning, as a form of artificial intelligence, has increasingly been used for detecting biomarkers and constructing tumor prognosis models. This article systematically reviews the latest research progress on TLSs in breast cancer and the application of machine learning in the detection of TLSs and the study of breast cancer prognosis. The insights provided contribute valuable perspectives for further exploring the biological differences among different subtypes of breast cancer and formulating personalized treatment strategies.

Transcriptome analysis reveals the clinical significance of CXCL13 in Pan-Gyn tumors

BACKGROUND

Gynecologic and breast tumors (Pan-Gyn) exhibit similar characteristics, and the role of CXCL13 in anti-tumor immunity and it's potential as a biomarker for immune checkpoint blockade (ICB) therapy have been gradually revealed. However, the precise role of CXCL13 in Pan-Gyn remains unclear, lacking a systematic analysis.

METHODS

We analyzed 2497 Pan-Gyn samples from the TCGA database, categorizing them into high and low CXCL13 expression groups. Validation was conducted using tumor expression datasets sourced from the GEO database. Correlation between CXCL13 and tumor immune microenvironment (TIME) was evaluated using multiple algorithms. Finally, we established nomograms for 3-year and 5-year mortality.

RESULTS

High expression of CXCL13 in Pan-Gyn correlates with a favorable clinical prognosis, increased immune cell infiltration, and reduced intra-tumor heterogeneity. Model was assessed using the C-index [BRCA: 0.763 (0.732-0.794), UCEC: 0.821 (0.793-0.849), CESC: 0.736 (0.684-0.788), and OV: 0.728 (0.707-0.749)], showing decent prediction of discrimination and calibration.

CONCLUSION

Overall, this study provides comprehensive insights into the commonalities and differences of CXCL13 in Pan-Gyn, potentially opening new avenues for personalized treatment.

Treatment-prognostication-adjustment a new therapeutic idea by analyzing T cell immune checkpoint in tumor microenvironment by algorithm: A bibliometric analysis

To evaluate the temporal and spatial distribution of the knowledge network about tumor microenvironment and prognoses and explore new research hot spots and trends. Articles and reviews on tumor microenvironment and prognoses in the Web of Science journal from January 1999 to April 2022 were included. We used the CiteSpace and VOSviewer software to analyze the knowledge network composed of journals, institutions, countries, authors, and keywords. Frontiers in Immunology, Cancers, and Frontiers in Oncology have published more than 10% of articles in this field. China and the United States have contributed the most articles. Fudan University and Sun Yat-Sen University are the most active institutions. The authors in this field work closely; Zhang Wei and Douglas have made outstanding contributions. The three main research areas of tumor microenvironment and prognoses are microenvironment, prognosis, and immunotherapy. Until 2020, the main keywords were endothelial growth factor and adhesion. In the past three years, survival analysis, immune cell infiltration, and prediction model have been used. It can be seen that the focus in this field has shifted from tumor cell behavior and directly related molecules to prognosis prediction and non-tumor cells in the microenvironment. The future research trend may be to study the changes in the tumor microenvironment to predict the prognosis and guide the treatment. VOSviewer, CiteSpace, and Microsoft Excel 2019 were used to conduct a comprehensive visual analysis of the research on tumor environment and prognoses and provide valuable reference materials for researchers.

Gene expression alterations predict the pathological complete response in triple-negative breast cancer exploratory analysis of the NACATRINE trial

This exploratory analysis of the Neoadjuvant Carboplatin in Triple Negative Breast Cancer (NACATRINE) study aimed to identify the biomarkers of pathological complete response (pCR) in patients with triple-negative breast cancer (TNBC) treated with neoadjuvant chemotherapy (NAC) within the context of a clinical trial. The NACATRINE trial is a phase II, single-center, randomized, open-label clinical trial that investigated the addition of carboplatin to sequential anthracycline- and taxane-based NAC for TNBC. We evaluated the gene expression in untreated samples to investigate its association with pCR, overall survival (OS), and disease-free survival (DFS). RNA was extracted from the tissue biopsy, and the nCounter Breast Cancer panel was used to analyze gene expression. Of the 66 patients included in the gene expression profiling analysis, 24 (36.4%) achieved pCR and 42 (63.6%) had residual disease. In unsupervised hierarchical clustering analyses, differentially expressed genes between patients with and without pCR were identified irrespective of the treatment (24 genes), carboplatin (37 genes), and non-carboplatin (27 genes) arms. In receiver operating characteristic (ROC) curve analysis, 10 genes in the carboplatin arm (area under the ROC curve [AUC], 0.936) and three genes in the non-carboplatin arm (AUC, 0.939) were considered to be potential pCR-associated biomarkers. We identified genes that were associated with improvements in OS and DFS in addition to being related to pCR. We successfully identified gene expression signatures associated with pCR in pretreatment samples of patients with TNBC treated with NAC. Further investigation of these biomarkers is warranted.

C-X-C Motif Chemokine Ligand 9 Correlates with Favorable Prognosis in Triple-Negative Breast Cancer by Promoting Immune Cell Infiltration

C-X-C motif chemokine ligand 9 (CXCL9) plays an important role in antitumor immunity through the recruitment, proliferation, and activation of immune cells (IC). Here, we evaluated the expression patterns of CXCL9 and programmed death-ligand 1 (PD-L1) in a cohort of 268 patients with triple-negative breast cancer (TNBC) by tissue microarray (TMA). The correlations between CXCL9 expression in ICs or tumor cells (TC) and clinicopathologic parameters, PD-L1 expression, tumor-infiltrating lymphocytes (TIL) and survival were analyzed in this cohort (n = 268). In addition, we analyzed a TNBC dataset (n = 138) from The Cancer Genome Atlas (TCGA) to identify correlation between CXCL9 expression and other immune gene expression, immune infiltration, and prognosis. The results of the TMA cohort (n = 268) showed that CXCL9 was expressed in 80.6% cases, with elevated expression levels in ICs relative to in TCs (median: 1% vs. 0%). CXCL9 expressed in ≥1% of ICs was categorized as the CXCL9-IC-positive group. CXCL9-IC expression was strongly and positively correlated with the PD-L1 expression, CD3+ TILs, CD4+ TILs, CD8+ TILs, and CD19+ TILs (all P < 0.0001). Survival analyses showed that the CXCL9-IC-positive group demonstrated prolonged disease-free survival (P = 0.038) and overall survival (P = 0.023) compared with the negative group. The analyses from TCGA cohort (n = 138) showed that elevated CXCL9 expression correlated with increased infiltration of B cells, macrophages, natural killer cells, monocytes and increased expression of immune checkpoint molecules and other CXCL family members, including CXCL10 and CXCL11. These findings confirm the regulatory role of CXCL9 in antitumor immunity and suggest a potential role in treatments involving immune checkpoint blockade.

A novel model associated with tumor microenvironment on predicting prognosis and immunotherapy in triple negative breast cancer

Triple negative breast cancer (TNBC) is the most aggressive and malignant subtype in breast cancer. Immunotherapy is a currently promising and effective treatment for TNBC, while not all patients are responsive. Therefore, it is necessary to explore novel biomarkers to screen sensitive populations for immunotherapy. All mRNA expression profiles of TNBC from The Cancer Genome Atlas (TCGA) database were clustered into two subgroups by analyzing tumor immune microenvironment (TIME) with single sample gene set enrichment analysis (ssGSEA). A risk score model was constructed based on differently expressed genes (DEGs) identified from two subgroups using Cox and Least Absolute Shrinkage and Selector Operation (LASSO) regression model. And it was validated by Kaplan-Meier analysis and Receiver Operating Characteristic (ROC) analysis in Gene Expression Omnibus (GEO) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Multiplex immunofluorescence (mIF) and Immunohistochemical (IHC) staining were performed on clinical TNBC tissue samples. The relationship between risk score and immune checkpoint blockades (ICB) related signatures was further investigated, as well as the biological processes were performed by gene set enrichment analysis (GSEA). We obtained three DEGs positively related to prognosis and infiltrating immune cells in TNBC. Our risk score model could be an independent prognostic factor and the low risk group exhibited a prolonged overall survival (OS). Patients in low risk group were more likely to present a higher immune infiltration and stronger response to immunotherapy. GSEA revealed the model was associated with immune-related pathways. We constructed and validated a novel model based on three prognostic genes related to TIME in TNBC. The model contributed a robust signature that could predict the prognosis in TNBC, especially for the efficacy of immunotherapy.

The elusive Luminal B breast cancer and the mysterious chemokines

PURPOSE

Invasive ductal breast cancer (IDC) is heterogeneous. Staging and immunohistochemistry (IH) allow for effective therapy but are not yet ideal. Women with Luminal B tumors show an erratic response to treatment. This prospective study with 81 women with breast cancer aims to improve the prognostic stratification of Luminal B patients.

METHODS

This is a prospective translational study with 81 women with infiltrating ductal carcinoma, grouped by TNM staging and immunohistochemistry, for survival analysis, and their correlations with the chemokines. Serum measurements of 13 chemokines were performed, including 7 CC chemokines [CCL2(MCP1), CCL3(MIP1α), CCL4(MIP1β), CCL5(Rantes), CCL11(Eotaxin), CCL17(TARC), CCL20(MIP3α)], 6 CXC chemokines [CXCL1(GroAlpha), CXCL5(ENA78), CCXCL8(IL-8), CXCL9(MIG), CXCL10(IP10), CXCL11(ITAC)].

RESULTS

Overall survival was significantly dependent on tumor staging and subtypes by immunohistochemistry, with a median follow-up time the 32.87 months (3.67-65.63 months). There were age correlations with IP10/CXCL10 chemokines (r =  0.4360; p = 0.0079) and TARC/CCL17 (Spearman + 0.2648; p = 0.0360). An inverse correlation was found between body weight and the chemokines Rantes/CCL5 (r = - 0.3098; p = 0.0169) and Eotaxin/CCL11 (r = - 0.2575; p = 0.0470). Smokers had a higher concentration of MIP3α/CCL20 (Spearman + 0.3344; p = 0.0267). Luminal B subtype patients who expressed lower concentrations of ENA78/CXCL5 (≤ 254.83 pg/ml) (Log-Rank p = 0.016) and higher expression of MIP1β/CCL4 (> 34.84 pg/ml) (Log-Rank p = 0.014) had a higher risk of metastases.

CONCLUSION

Patients with Luminal B breast tumors can be better stratified by serum chemokine expression, suggesting that prognosis is dependent on biomarkers other than TNM and IH.

A Retrospective Exploratory Analysis for Serum Extracellular Vesicles Reveals APRIL (TNFSF13), CXCL13, and VEGF-A as Prognostic Biomarkers for Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

Neoadjuvant chemotherapy (NAC) is widely used as a standard treatment for early-stage triple-negative breast cancer (TNBC). While patients who achieve pathologic complete response (pCR) have a highly favorable outcome, patients who do not achieve pCR have variable prognoses. It is important to identify patients who are most likely to have poor survival outcomes to identify candidates for more aggressive therapeutic approaches after NAC. Many studies have demonstrated that cytokines and growth factors packaged into extracellular vesicles (EVs) have an essential role in tumor progression and drug resistance. In this study, we examined the role of serum-derived EV-associated cytokines as prognostic biomarkers for long-term outcomes in patients who underwent anthracycline-taxane-based NAC. We isolated extracellular vesicles from the serum of 190 TNBC patients who underwent NAC between 2015 and 2018 at Samsung Medical Center. EV-associated cytokine concentrations were measured with ProcartaPlex Immune Monitoring 65-plex panels. The prognostic value of EV-associated cytokines was studied. We found that patients with high EV_APRIL, EV_CXCL13, and EV_VEGF-A levels had shorter overall survival (OS). We further evaluated the role of these selected biomarkers as prognostic factors in patients with residual disease (RD) after NAC. Even in patients with RD, high levels of EV_APRIL, EV_CXCL13, and EV_VEGF-A were correlated with poor OS. In all subgroup analyses, EV_CXCL13 overexpression was significantly associated with poor overall survival. Moreover, multivariate analysis indicated that a high level of EV_CXCL13 was an independent predictor of poor OS. Correlation analysis between biomarker levels in EVs and serum showed that EV_VEGF-A positively correlated with soluble VEGF-A but not CXCL13. An elevated level of soluble VEGF-A was also associated with poor OS. These findings suggest that EV_APRIL, EV_CXCL13, and EV_VEGF-A may be useful in identifying TNBC patients at risk of poor survival outcomes after NAC.

Non-coding regions of nuclear-DNA-encoded mitochondrial genes and intergenic sequences are targeted by autoantibodies in breast cancer

Autoantibodies against mitochondrial-derived antigens play a key role in chronic tissue inflammation in autoimmune disorders and cancers. Here, we identify autoreactive nuclear genomic DNA (nDNA)-encoded mitochondrial gene products (GAPDH, PKM2, GSTP1, SPATA5, MFF, TSPOAP1, PHB2, COA4, and HAGH) recognized by breast cancer (BC) patients’ sera as nonself, supporting a direct relationship of mitochondrial autoimmunity to breast carcinogenesis. Autoreactivity of multiple nDNA-encoded mitochondrial gene products was mapped to protein-coding regions, 3’ untranslated regions (UTRs), as well as introns. In addition, autoantibodies in BC sera targeted intergenic sequences that may be parts of long non-coding RNA (lncRNA) genes, including LINC02381 and other putative lncRNA neighbors of the protein-coding genes ERCC4, CXCL13, SOX3, PCDH1, EDDM3B, and GRB2. Increasing evidence indicates that lncRNAs play a key role in carcinogenesis. Consistent with this, our findings suggest that lncRNAs, as well as mRNAs of nDNA-encoded mitochondrial genes, mechanistically contribute to BC progression. This work supports a new paradigm of breast carcinogenesis based on a globally dysfunctional genome with altered function of multiple mitochondrial and non-mitochondrial oncogenic pathways caused by the effects of autoreactivity-induced dysregulation of multiple genes and their products. This autoimmunity-based model of carcinogenesis will open novel avenues for BC treatment.

Prognostic Significance of the CXCLs and Its Impact on the Immune Microenvironment in Ovarian Cancer

The chemokine (C-X-C motif) ligand (CXCL) family in tumor tissue is closely related to tumor growth, metastasis, and survival. However, the differential expression profile and prognostic value of the CXCLs in ovarian cancer (OC) have not been elucidated. Therefore, we studied the expression levels and mutations of CXCLs in OC patient in TCGA and various public databases. The expression differences of CXCLs in OC cancer tissues and normal tissues were compared through the Gene Expression Profiling Interactive Analysis (GEPIA) database. The effect of CXCLs on OC prognosis was analyzed using the Kaplan-Meier curves in GEPIA database. The impact of CXCLs on immune infiltration and clinicopathological outcomes in OC was assessed using the TIMER algorithm. Compared with normal tissues, we found that eight CXCLs were significantly differentially expressed in OC. The expression levels of CXCL9 (P = 0.0201), CXCL11 (P = 0.0385), and CXCL13 (P = 0.0288) were significantly associated with tumor stage. CXCL13 was the only gene that significantly affected both disease-free survival (DFS) and overall survival (OS) in OC, and higher CXCL13 transcript levels implied longer DFS and OS. Although there was no significant impact on DFS, CXCL10 (P = 0.0079) and CXCL11 (P = 0.0011) expression levels had a significant effect on OS in OC. At the same time, CXCLs were significantly associated with several immune-infiltrating cells in OC tissues. The CXCLs were significantly associated with one or more immune-infiltrating cells in OC tissue. CXCL13 was differentially expressed in OC and significantly affected the prognosis of patients and was a potential marker of OC prognosis.

Using publicly available datasets to identify population-based transcriptomic landscape contributing to the aggressiveness of breast cancer in young women

Introduction: Although the risk of breast cancer increases with advancing age, some regions have larger number of young breast cancer patients (≤45 years-old), such as the Middle East, Eastern Asia, and North Africa, with more aggressive and poorly differentiated tumors. We aimed to conduct an in-silico analysis in an attempt to understand the aggressive nature of early-onset breast cancer, and to identify potential drivers of early-onset breast cancer using gene expression profiling datasets in a population-dependent manner. Methods: Functional genomics experiments data were acquired from cBioPortal database for cancer genomics, followed by the stratification of patients based on the age at representation of breast cancer and race. Differential gene expression analysis and gene amplification status analysis were carried out, followed by hub gene, transcription factor, and signalling pathway identification. Results: PAM50 subtype analysis revealed that young patients (≤45 years-old) had four-fold more basal tumors and worst progression-free survival (median of 101 months), compared with the 45-65 years group (median of 168 months). Fourteen genes were amplified in more than 14% of patients with an early-onset breast cancer. Interestingly, FREM2, LINC00332, and LINC00366 were exclusively amplified in younger patients. Gene expression data from three different populations (Asian, White, and African) revealed a unique transcriptomic profile of young patients, which was also reflected on the PAM50 subtype analysis. Our data indicates a higher tendency of young African patients to develop basal tumors, while young Asian patients are more prone to developing Luminal A tumors. Most genes that were found to be upregulated in younger patients are involved in important signaling pathways that promote cancer progression and metastasis, such as MAPK pathway, Reelin pathway and the PI3K/Akt pathway. Conclusion: This study provides strong evidence that the molecular profile of tumors derived from young breast cancer patients of different populations is unique and may explain the aggressiveness of these tumors, stressing the need to conduct population- based multi-omic analyses to identify the potential drivers for tumorigenesis and molecular profiles of young breast cancer patients.

Molecular differences between younger versus older ER-positive and HER2-negative breast cancers

The RxPONDER and TAILORx trials demonstrated benefit from adjuvant chemotherapy in patients age ≤ 50 with node-positive breast cancer and Recurrence Score (RS) 0-26, and in node-negative disease with RS 16-25, respectively, but no benefit in older women with the same clinical features. We analyzed transcriptomic and genomic data of ER+/HER2- breast cancers with in silico RS < 26 from TCGA (n = 530), two microarray cohorts (A: n = 865; B: n = 609), the METABRIC (n = 867), and the SCAN-B (n = 1636) datasets. There was no difference in proliferation-related gene expression between age groups. Older patients had higher mutation burden and more frequent ESR1 copy number gain, but lower frequency of GATA3 mutations. Younger patients had higher rate of ESR1 copy number loss. In all datasets, younger patients had significantly lower mRNA expression of ESR1 and ER-associated genes, and higher expression of immune-related genes. The ER- and immune-related gene signatures showed negative correlation and defined three subpopulations in younger women: immune-high/ER-low, immune-intermediate/ER-intermediate, and immune-low/ER-intermediate. We hypothesize that in immune-high cancers, the cytotoxic effect of chemotherapy may drive the benefit, whereas in immune-low/ER-intermediate cancers chemotherapy induced ovarian suppression may play important role.

A comprehensive bioinformatics analysis to identify potential prognostic biomarkers among CC and CXC chemokines in breast cancer

Breast cancer (BC) is a major human health problem due to its increasing incidence and mortality rate. CC and CXC chemokines are associated with tumorigenesis and the progression of many cancers. Since the prognostic values of CC and CXC families' expression in various types of cancers are becoming increasingly evident, we aimed to conduct a comprehensive bioinformatics analysis elucidating the prognostic values of the CC and CXC families in BC. Therefore, TCGA, UALCAN, Kaplan–Meier plotter, bc-GenExMiner, cBioPortal, STRING, Enrichr, and TIMER were utilized for analysis. We found that high levels of CCL4/5/14/19/21/22 were associated with better OS and RFS, while elevated expression of CCL24 was correlated with shorter OS in BC patients. Also, high levels of CXCL9/13 indicated longer OS, and enhanced expression of CXCL12/14 was linked with better OS and RFS in BC patients. Meanwhile, increased transcription levels of CXCL8 were associated with worse OS and RFS in BC patients. In addition, our results showed that CCL5, CCL8, CCL14, CCL20, CCL27, CXCL4, and CXCL14 were notably correlated with the clinical outcomes of BC patients. Our findings provide a new point of view that may help the clinical application of CC and CXC chemokines as prognostic biomarkers in BC.

Comprehensive analysis of prognostic value and immune infiltration of CXC chemokines in pancreatic cancer

BACKGROUND

The prognosis of pancreatic cancer is poor, with a 5-year survival rate of less than 10%. Studies have shown that chemokines in the tumour microenvironment are often altered, which is associated with immune infiltration and the prognosis and survival of pancreatic cancer patients.

METHODS

Multiomics and bioinformatics tools were used to clarify CXC chemokine expression and its role in the pancreatic ductal adenocarcinoma (PDAC) immune microenvironment.

RESULTS

Most CXC chemokines were upregulated in pancreatic cancer and correlated with patient prognosis. CXC chemokines can activate cancer-related signalling pathways and affect immune infiltration. Furthermore, most CXC chemokines were significantly correlated with the abundance of macrophages, neutrophils and dendritic cells. CXCL5 was selected as a hub gene, and a variety of immune checkpoints, including PD-1/PD-L1 and CTLA-4, were identified.

CONCLUSION

Our study provides novel insights into CXC chemokine expression and its role in the PDAC immune microenvironment. These results can provide more data about prognostic biomarkers and therapeutic targets of PDAC.

Identification of prognostic biomarkers for breast cancer brain metastases based on the bioinformatics analysis

Purpose

The prognosis of breast cancer (BC) patients who develop into brain metastases (BMs) is very poor. Thus, it is of great significance to explore the etiology of BMs in BC and identify the key genes involved in this process to improve the survival of BC patients with BMs.

Patients and methods

The gene expression data and the clinical information of BC patients were downloaded from TCGA and GEO database. Differentially expressed genes (DEGs) in TCGA-BRCA and GSE12276 were overlapped to find differentially expressed metastatic genes (DEMGs). The protein-protein interaction (PPI) network of DEMGs was constructed via STRING database. ClusterProfiler R package was applied to perform the gene ontology (GO) enrichment analysis of DEMGs. The univariate Cox regression analysis and the Kaplan-Meier (K-M) curves were plotted to screen DEMGs associated with the overall survival and the metastatic recurrence survival, which were identified as the key genes associated with the BMs in BC. The immune infiltration and the expressions of immune checkpoints for BC patients with brain relapses and BC patients with other relapses were analyzed respectively. The correlations among the expressions of key genes and the differently infiltrated immune cells or the differentially expressed immune checkpoints were calculated. The gene set enrichment analysis (GSEA) of each key gene was conducted to investigate the potential mechanisms of key genes involved in BC patients with BMs. Moreover, CTD database was used to predict the drug-gene interaction network of key genes.

Results

A total of 154 DEGs were identified in BC patients at M0 and M1 in TCGA database. A total of 667 DEGs were identified in BC patients with brain relapses and with other relapses. By overlapping these DEGs, 17 DEMGs were identified, which were enriched in the cell proliferation related biological processes and the immune related molecular functions. The univariate Cox regression analysis and the Kaplan-Meier curves revealed that CXCL9 and GPR171 were closely associated with the overall survival and the metastatic recurrence survival and were identified as key genes associated with BMs in BC. The analyses of immune infiltration and immune checkpoint expressions showed that there was a significant difference of the immune microenvironment between brain relapses and other relapses in BC. GSEA indicated that CXCL9 and GPR171 may regulate BMs in BC via the immune-related pathways.

Conclusion

Our study identified the key genes associated with BMs in BC patients and explore the underlying mechanisms involved in the etiology of BMs in BC. These findings may provide a promising approach for the treatments of BC patients with BMs.

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CXCL9 and GPR171, as the key genes, were closely associated with the prognosis of brain metastases in breast cancer.

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There was a significant difference of the immune microenvironment between brain and other metastases in breast cancer.

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We revealed candidate drugs which associated with the key genes of breast cancer patients with brain metastases.

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A series of bioinformatic analysis methods were used in this article.

Potential Role of CXCL13/CXCR5 Signaling in Immune Checkpoint Inhibitor Treatment in Cancer

Simple Summary

Immunotherapy is currently the backbone of new drug treatments for many cancer patients. CXC chemokine ligand 13 (CXCL13) is an important factor involved in recruiting immune cells that express CXC chemokine receptor type 5 (CXCR5) in the tumor microenvironment and serves as a key molecular determinant of tertiary lymphoid structure (TLS) formation. An increasing number of studies have identified the influence of CXCL13 on prognosis in patients with cancer, regardless of the use of immunotherapy treatment. However, no comprehensive reviews of the role of CXCL13 in cancer immunotherapy have been published to date. This review aims to provide an overview of the CXCL13/CXCR5 signaling axis to summarize its mechanisms of action in cancer cells and lymphocytes, in addition to effects on immunity and cancer pathobiology, and its potential as a biomarker for the response to cancer immunotherapy.

Abstract

Immune checkpoint inhibitors (ICIs), including antibodies that target programmed cell death protein 1 (PD-1), programmed death-ligand 1 (PD-L1), or cytotoxic T lymphocyte antigen 4 (CTLA4), represent some of the most important breakthroughs in new drug development for oncology therapy from the past decade. CXC chemokine ligand 13 (CXCL13) exclusively binds CXC chemokine receptor type 5 (CXCR5), which plays a critical role in immune cell recruitment and activation and the regulation of the adaptive immune response. CXCL13 is a key molecular determinant of the formation of tertiary lymphoid structures (TLSs), which are organized aggregates of T, B, and dendritic cells that participate in the adaptive antitumor immune response. CXCL13 may also serve as a prognostic and predictive factor, and the role played by CXCL13 in some ICI-responsive tumor types has gained intense interest. This review discusses how CXCL13/CXCR5 signaling modulates cancer and immune cells to promote lymphocyte infiltration, activation by tumor antigens, and differentiation to increase the antitumor immune response. We also summarize recent preclinical and clinical evidence regarding the ICI-therapeutic implications of targeting the CXCL13/CXCR5 axis and discuss the potential role of this signaling pathway in cancer immunotherapy.

Establishment and validation of an individualized macrophage-related gene signature to predict overall survival in patients with triple negative breast cancer

Background

Recently, researchers have classified highly heterogeneous triple negative breast cancer (TNBC) into different subtypes from different perspectives and investigated the characteristics of different subtypes to pursue individualized treatment. With the increase of immunotherapy and its preliminary application in TNBC treatment, the value of immune-related strategies in the treatment of TNBC has been initially reflected. Based thereon, this study plans to classify and further explore TNBC from the perspective of immune cell infiltration.

Method

The fractions of immune cells of TNBC patients were assessed by six immune component analysis methods in The Cancer Genome Atlas (TCGA) database. Hub genes significantly related to poor prognosis were verified by weighted gene co-expression network analysis (WGCNA) analysis, Lasso analysis, and univariate KM analysis. Two cohorts of TNBC patients with complete prognosis information were collected for validation analysis. Finally, the Genomics of Drug Sensitivity in Cancer (GDSC) database was adopted to ascertain the sensitivity differences of different populations based on hub-gene grouping to different chemotherapy drugs.

Results

Five hub genes (CD79A, CXCL13, IGLL5, LHFPL2, and PLEKHF1) of the key co-expression gene module could divide TNBC patients into two groups (Cluster A and Cluster B) based on consistency cluster analysis. The patients with Cluster A were responsible for significantly worse prognosis than the patients with Cluster B (P = 0.023). In addition, another classification method, PCoA, and two other datasets (GSE103091 and GSE76124), were used to obtain consistent results with previous findings, which verified the stability of the classification method and dataset in this study. The grouping criteria based on the previous results were developed and the accuracy of the cut-off values was validated. A prognosis model of TNBC patients was then constructed based on the grouping results of five hub genes and N staging as prognostic factors. The results of ROC and decision curve analyses showed that this model had high prediction accuracy and patients could benefit therefrom. Finally, GDSC database analysis proved that patients in Cluster A were more sensitive to Vinorelbine. Separate analysis of the sensitivity of patients in Cluster A to Gemcitabine and Vinorelbine showed that the patients in Cluster A exhibited higher sensitivity to Vinorelbine. We hypothesized that these five genes were related to gemcitabine resistance and they could serve as biomarkers for clinical drug decision-making after anthracene resistance and taxane resistance in patients with advanced TNBC.

Conclusion

This study found five hub prognostic genes associated with macrophages, and a prognostic model was established to predict the survival of TNBC patients. Finally, these five genes were related to gemcitabine resistance in TNBC patients.

Chemokine signaling in cancer-stroma communications

Cancer is a multi-faceted disease in which spontaneous mutation(s) in a cell leads to the growth and development of a malignant new organ that if left undisturbed will grow in size and lead to eventual death of the organism. During this process, multiple cell types are continuously releasing signaling molecules into the microenvironment, which results in a tangled web of communication that both attracts new cell types into and reshapes the tumor microenvironment as a whole. One prominent class of molecules, chemokines, bind to specific receptors and trigger directional, chemotactic movement in the receiving cell. Chemokines and their receptors have been demonstrated to be expressed by almost all cell types in the tumor microenvironment, including epithelial, immune, mesenchymal, endothelial, and other stromal cells. This results in chemokines playing multifaceted roles in facilitating context-dependent intercellular communications. Recent research has started to shed light on these ligands and receptors in a cancer-specific context, including cell-type specificity and drug targetability. In this review, we summarize the latest research with regards to chemokines in facilitating communication between different cell types in the tumor microenvironment.

CXCL13 expression in mouse 4T1 breast cancer microenvironment elicits antitumor immune response by regulating immune cell infiltration

Breast cancer is the most commonly diagnosed cancer type and the leading cause of cancer-related deaths among women worldwide. Previous studies have reported contradictory performance of chemokine CXC motif ligand 13 (CXCL13) in breast cancer. In this study, The Cancer Genome Atlas database analysis revealed that CXCL13 was overexpressed in various human cancers including breast carcinoma, and associated with good clinical prognosis in breast cancer. Flow cytometry detection also found upregulated intracellular CXCL13 expression in human breast cancer cell lines. To explore the possible role of CXCL13 in the breast cancer microenvironment, mouse triple negative breast cancer (TNBC) was lentivirally transfected to stably overexpress mouse CXCL13 (4T1-CXCL13). Both parental 4T1 and 4T1-CXCL13 strains showed no in vitro or in vivo endogenous cell surface CXCR5 expression. In immune-competent BALB/c mice, the in vivo tumor growth of 4T1-CXCL13 was significantly inhibited and even completely eradicated, accompanied with increased infiltrations of CD4+, CD8+ T lymphocytes and CD11b+CD11c+ DCs. Further investigations showed that CXCL13 expression in the 4T1 tumor microenvironment elicited long-term antitumor immune memory, and rejection of distal parental tumor. The antitumor activity of CXCL13 was remarkedly impaired in BALB/cA-nu nude mice, or in BALB/c mice with CD8+ T lymphocyte or NK cell depletion. Our investigation indicated that CXCL13 expression in TNBC triggered effective antitumor immunity by chemoattracting immune cell infiltrations and could be considered as a novel prognostic marker for TNBC.

Identification of CXCL13 as an Immune-Related Biomarker Associated with Tumorigenesis and Prognosis in Cutaneous Melanoma Patients

BACKGROUND Melanoma is one of the most lethal tumors and its treatment is still challenging. It is urgent to detect novel therapy targets in melanoma. MATERIAL AND METHODS The GEO dataset was used to obtain a list of DEGS (differentially-expressed genes). Integrative bioinformatics analyses, including HPRD database, TCGA data, and TIMER, were performed to determine the role of CXCL13 in SKCM (skin cutaneous melanoma) progression and the immune environment. Furthermore, Pearson correlation coefficient analysis was used to measure correlations between CXCL13 and its co-expressed genes. Survival analysis, GO, and KEGG enrichment analysis were performed to investigate the role of CXCL13 in SKCM. RESULTS A total of 41 DEGs were identified in 3 GEO datasets, and 4 out of 41 DEGs are hub genes. Among the 4 hub genes, CXCL13 is involved in the most KEGG terms. CXCL13 is co-expressed with well-known immune checkpoint blockade targets, and it was associated with better overall survival. In addition, CXCL13 levels in infiltrating immune cells (neutrophil and myeloid dendritic cells) affect prognosis and survival in SKCM. Functional enrichment analysis clarified that CXCL13-co-expressed top 30 genes were associated with immune signaling pathways. Network analysis identified CXCL13 as a hub gene that interacts with CXCR5 to participate in immune-related biological process. CONCLUSIONS This study found that CXCL13 is associated with SKCM tumorigenesis and prognosis and immune infiltrations. Our result suggests that CXCL13 has great potential in development of novel immunotherapy targets in melanoma.

Circulating Biomarkers of Inflammation and Ovarian Cancer Risk in the Nurses' Health Studies

BACKGROUND

Chronic inflammation is a well-established mechanism of ovarian carcinogenesis; however, the specific immunogenic processes influencing ovarian tumor development remain unclear. In a case-control study nested within the Nurses' Health Study (NHS) and the NHSII, we examined the association between six inflammatory chemokines and cytokines [B-cell activating factor (BAFF), C-X-C motif chemokine ligand 13 (CXCL13), IL8, soluble(s)IL2-receptor-α(Rα), sIL6Rα] and epithelial ovarian cancer risk.

METHODS

Among 299 epithelial ovarian cancer cases and 334 matched controls, six inflammatory biomarkers were measured in plasma collected 1-24 years before diagnosis or index date using two custom multiplex Luminex panels. ORs and 95% confidence intervals (CI) were estimated for the association between each biomarker and risk using multivariable conditional logistic regression with adjustment for relevant confounders. We additionally assessed heterogeneity in the risk associations by histotype [high-grade serous carcinoma (HGSC) vs. non-HGSC], body mass index, smoking status, menopausal status, and aspirin use.

RESULTS

Women with the highest versus lowest quartile (Q) levels of CXCL13 had a 72% increased ovarian cancer risk (OR = 1.72; 95% CI = 1.04-2.83; P _trend = 0.007). The positive association with CXCL13 was stronger in magnitude for non-HGSC, overweight or obese women, and postmenopausal women, although only menopausal status demonstrated statistically significant heterogeneity (P _interaction = 0.04). The remaining biomarkers were not associated with risk.

CONCLUSIONS

This first evidence that prediagnostic CXCL13, a B-cell chemoattractant, is associated with an increased risk of epithelial ovarian cancer expands current understanding of the role of inflammation in ovarian carcinogenesis.

IMPACT

CXCL13 may represent a novel biomarker for ovarian cancer.

CXCL9 Is a Potential Biomarker of Immune Infiltration Associated With Favorable Prognosis in ER-Negative Breast Cancer

The chemokine CXCL9 (C-X-C motif chemokine ligand 9) has been reported to be required for antitumour immune responses following immune checkpoint blockade. In this study, we sought to investigate the potential value of CXCL9 according to immune responses in patients with breast cancer (BC). A variety of open-source databases and online tools were used to explore the expression features and prognostic significance of CXCL9 in BC and its correlation with immune-related biomarkers followed by subsequent verification with immunohistochemistry experiments. The CXCL9 mRNA level was found to be significantly higher in BC than in normal tissue and was associated with better survival outcomes in patients with ER-negative tumours. Moreover, CXCL9 is significantly correlated with immune cell infiltration and immune-related biomarkers, including CTLA4, GZMB, LAG3, PDCD1 and HAVCR2. Finally, we performed immunohistochemistry with breast cancer tissue samples and observed that CXCL9 is highly expressed in the ER-negative subgroup and positively correlated with the immune-related factors LAG3, PD1, PDL1 and CTLA4 to varying degrees. These findings suggest that CXCL9 is an underlying biomarker for predicting the status of immune infiltration in ER-negative breast cancer.

M1 Polarization Markers Are Upregulated in Basal-Like Breast Cancer Molecular Subtype and Associated With Favorable Patient Outcome

Background

Breast cancer heterogeneity is an essential element that plays a role in the therapy response variability and the patient's outcome. This highlights the need for more precise subtyping methods that focus not only on tumor cells but also investigate the profile of stromal cells as well as immune cells.

Objectives

To mine publicly available transcriptomic breast cancer datasets and reanalyze their transcriptomic profiling using unsupervised clustering in order to identify novel subsets in molecular subtypes of breast cancer, then explore the stromal and immune cells profile in each subset using bioinformatics and systems immunology approaches.

Materials and Methods

Transcriptomic data from 1,084 breast cancer patients obtained from The Cancer Genome Atlas (TCGA) database were extracted and subjected to unsupervised clustering using a recently described, multi-step algorithm called Iterative Clustering and Guide-gene Selection (ICGS). For each cluster, the stromal and immune profile was investigated using ESTIMATE and CIBERSORT analytical tool. Clinical outcomes and differentially expressed genes of the characterized clusters were identified and validated in silico and in vitro in a cohort of 80 breast cancer samples by immunohistochemistry.

Results

Seven unique sub-clusters showed distinct molecular and clinical profiles between the well-known breast cancer subtypes. Those unsupervised clusters identified more homogenous subgroups in each of the classical subtypes with a different prognostic profile. Immune profiling of the identified clusters showed that while the classically activated macrophages (M1) are correlated with the more aggressive basal-like breast cancer subtype, the alternatively activated macrophages (M2) showed a higher level of infiltration in luminal A and luminal B subtypes. Indeed, patients with higher levels of M1 expression showed less advanced disease and better patient outcomes presented as prolonged overall survival. Moreover, the M1 high basal-like breast cancer group showed a higher expression of interferon-gamma induced chemokines and guanylate-binding proteins (GBPs) involved in immunity against microbes.

Conclusion

Adding immune profiling using transcriptomic data can add precision for diagnosis and prognosis and can cluster patients according to the available modalities of therapy in a more personalized approach.

A Quest for New Cancer Diagnosis, Prognosis and Prediction Biomarkers and Their Use in Biosensors Development

Traditional techniques for cancer diagnosis, such as nuclear magnetic resonance, ultrasound and tissue analysis, require sophisticated devices and highly trained personnel, which are characterized by elevated operation costs. The use of biomarkers has emerged as an alternative for cancer diagnosis, prognosis and prediction because their measurement in tissues or fluids, such as blood, urine or saliva, is characterized by shorter processing times. However, the biomarkers used currently, and the techniques used for their measurement, including ELISA, western-blot, polymerase chain reaction (PCR) or immunohistochemistry, possess low sensitivity and specificity. Therefore, the search for new proteomic, genomic or immunological biomarkers and the development of new noninvasive, easier and cheaper techniques that meet the sensitivity and specificity criteria for the diagnosis, prognosis and prediction of this disease has become a relevant topic. The purpose of this review is to provide an overview about the search for new cancer biomarkers, including the strategies that must be followed to identify them, as well as presenting the latest advances in the development of biosensors that possess a high potential for cancer diagnosis, prognosis and prediction, mainly focusing on their relevance in lung, prostate and breast cancers.

Transcriptional Expressions of CXCL9/10/12/13 as Prognosis Factors in Breast Cancer

CXCLs play critical roles in antitumor immunity by activating tumor-specific immune responses and stimulating tumor proliferation, thus affecting patient outcomes. However, the expression and prognostic values of CXCLs in breast cancer have not been well clarified. The aim of this study was to investigate the impact of CXCLs transcriptional expression on breast cancer patients. Oncomine database, GEPIA (Gene Expression Profiling Interactive Analysis), UALCAN, Kaplan–Meier Plotter, TIMER (Tumor Immune Estimation Resource), and DAVID were used in our study. The transcriptional levels of CXCL9/10/11/13 in breast cancer tissues were significantly elevated while the transcriptional levels of CXCL1/2/3/12 were decreased based on intersections of Oncomine database and GEPIA. Among them, breast cancer patients with high transcriptional levels of CXCL2/9/10/12/13 and low transcriptional level of CXCL3 were associated with a better prognosis. We also found that most of CXCLs expressions are significantly correlated with known prognostic factors, such as patient's age, major subclasses, individual cancer stages, and nodal metastasis status. In addition, the expression of CXCL9/10/12/13 was also indicated to be correlated with the infiltration of six types of immune cells (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells). The functions of differentially expressed CXCLs are primarily related to the immune response and cytokine-cytokine receptor interactions. Our results may provide novel evidence of new prognostic or predictive biomarkers for breast cancer patients.