Differentially Expressed Genes in Matched Normal, Cancer, and Lymph Node Metastases Predict Clinical Outcomes in Patients With Breast Cancer

Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS

Our study aims to address the methodological challenges frequently encountered in RNA-Seq data analysis within cancer studies. Specifically, it enhances the identification of key genes involved in axillary lymph node metastasis (ALNM) in breast cancer. We employ Generalized Linear Models with Quasi-Likelihood (GLMQLs) to manage the inherently discrete and overdispersed nature of RNA-Seq data, marking a significant improvement over conventional methods such as the t-test, which assumes a normal distribution and equal variances across samples. We utilize the Trimmed Mean of M-values (TMMs) method for normalization to address library-specific compositional differences effectively. Our study focuses on a distinct cohort of 104 untreated patients from the TCGA Breast Invasive Carcinoma (BRCA) dataset to maintain an untainted genetic profile, thereby providing more accurate insights into the genetic underpinnings of lymph node metastasis. This strategic selection paves the way for developing early intervention strategies and targeted therapies. Our analysis is exclusively dedicated to protein-coding genes, enriched by the Magnitude Altitude Scoring (MAS) system, which rigorously identifies key genes that could serve as predictors in developing an ALNM predictive model. Our novel approach has pinpointed several genes significantly linked to ALNM in breast cancer, offering vital insights into the molecular dynamics of cancer development and metastasis. These genes, including ERBB2, CCNA1, FOXC2, LEFTY2, VTN, ACKR3, and PTGS2, are involved in key processes like apoptosis, epithelial-mesenchymal transition, angiogenesis, response to hypoxia, and KRAS signaling pathways, which are crucial for tumor virulence and the spread of metastases. Moreover, the approach has also emphasized the importance of the small proline-rich protein family (SPRR), including SPRR2B, SPRR2E, and SPRR2D, recognized for their significant involvement in cancer-related pathways and their potential as therapeutic targets. Important transcripts such as H3C10, H1-2, PADI4, and others have been highlighted as critical in modulating the chromatin structure and gene expression, fundamental for the progression and spread of cancer.

9S1R nullomer peptide induces mitochondrial pathology, metabolic suppression, and enhanced immune cell infiltration, in triple-negative breast cancer mouse model

Nullomers are the shortest strings of absent amino acid (aa) sequences in a species or group of species. Primes are those nullomers that have not been detected in the genome of any species. 9S1R is a 5-aa peptide prime sequence attached to 5-arginine aa, used to treat triple negative breast cancer (TNBC) in an in vivo mouse model. This unique peptide, administered with a trehalose carrier (9S1R-NulloPT), offers enhanced solubility and exhibits distinct anti-cancer effects against TNBC. In our study, we investigated the effect of 9S1R-NulloPT on tumor growth, metabolism, metastatic burden, tumor immune-microenvironment (TME), and transcriptome of aggressive mouse TNBC tumors. Notably, treated mice had smaller tumors in the initial phase of the treatment, as compared to untreated control, and diminished in vivo and ex vivo bioluminescence at later-stages - indicative of metabolically quiescent, dying tumors. The treatment also caused changes in TME with increased infiltration of immune cells and altered tumor transcriptome, with 365 upregulated genes and 710 downregulated genes. Consistent with in vitro data, downregulated genes were enriched in cellular metabolic processes (179), specifically mitochondrial TCA cycle/oxidative phosphorylation (44), and translation machinery/ribosome biogenesis (45). The upregulated genes were associated with the developmental (13), ECM organization (12) and focal adhesion pathways (7). In conclusion, our study demonstrates that 9S1R-NulloPT effectively reduced tumor growth during its initial phase, altering the TME and tumor transcriptome. The treatment induced mitochondrial pathology which led to a metabolic deceleration in tumors, aligning with in vitro observations.

Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches

Cancer metastasis accounts for approximately 90% of cancer deaths, and elucidating markers in metastasis is the first step in its prevention. To characterize metastasis marker genes (MGs) of breast cancer, XGBoost models that classify metastasis status were trained with gene expression profiles from TCGA. Then, a metastasis score (MS) was assigned to each gene by calculating the inner product between the feature importance and the AUC performance of the models. As a result, 54, 202, and 357 genes with the highest MS were characterized as MGs by empirical p-value cutoffs of 0.001, 0.005, and 0.01, respectively. The three sets of MGs were compared with those from existing metastasis marker databases, which provided significant results in most comparisons (p-value < 0.05). They were also significantly enriched in biological processes associated with breast cancer metastasis. The three MGs, SPPL2C, KRT23, and RGS7, showed highly significant results (p-value < 0.01) in the survival analysis. The MGs that could not be identified by statistical analysis (e.g., GOLM1, ELAVL1, UBP1, and AZGP1), as well as the MGs with the highest MS (e.g., ZNF676, FAM163B, LDOC2, IRF1, and STK40), were verified via the literature. Additionally, we checked how close the MGs were to each other in the protein-protein interaction networks. We expect that the characterized markers will help understand and prevent breast cancer metastasis.

Whole-Exome Sequencing Reveals High Mutational Concordance between Primary and Matched Recurrent Triple-Negative Breast Cancers

PURPOSE

Triple-negative breast cancer (TNBC) is a molecularly complex and heterogeneous breast cancer subtype with distinct biological features and clinical behavior. Although TNBC is associated with an increased risk of metastasis and recurrence, the molecular mechanisms underlying TNBC metastasis remain unclear. We performed whole-exome sequencing (WES) analysis of primary TNBC and paired recurrent tumors to investigate the genetic profile of TNBC.

METHODS

Genomic DNA extracted from 35 formalin-fixed paraffin-embedded tissue samples from 26 TNBC patients was subjected to WES. Of these, 15 were primary tumors that did not have recurrence, and 11 were primary tumors that had recurrence (nine paired primary and recurrent tumors). Tumors were analyzed for single-nucleotide variants and insertions/deletions.

RESULTS

The tumor mutational burden (TMB) was 7.6 variants/megabase in primary tumors that recurred (n = 9); 8.2 variants/megabase in corresponding recurrent tumors (n = 9); and 7.3 variants/megabase in primary tumors that did not recur (n = 15). MUC3A was the most frequently mutated gene in all groups. Mutations in MAP3K1 and MUC16 were more common in our dataset. No alterations in PI3KCA were detected in our dataset.

CONCLUSIONS

We found similar mutational profiles between primary and paired recurrent tumors, suggesting that genomic features may be retained during local recurrence.

Nullomer peptide increases immune cell infiltration and reduces tumor metabolism in triple negative breast cancer mouse model

Background

Nullomers are the shortest strings of absent amino acid (aa) sequences in a species or group of species. Primes are those nullomers that have not been detected in the genome of any species. 9S1R is a 5-aa peptide derived from a prime sequence that is tagged with 5 arginine aa, used to treat triple negative breast cancer (TNBC) in an in vivo TNBC mouse model. 9S1R is administered in trehalose (9S1R-NulloPT), which enhances solubility and exhibits some independent effects against tumor growth and is thus an important component in the drug preparation.

Method

We examined the effect of 9S1R-NulloPT on tumor growth, metabolism, metastatic burden, necrosis, tumor immune microenvironment, and the transcriptome of aggressive mouse TNBC tumors.

Results

The peptide-treated mice had smaller tumors in the initial phase of the treatment, as compared to the untreated control, and reduced in vivo bioluminescence at later stages, which is indicative of metabolically inactive tumors. A decrease in ex vivo bioluminescence was also observed in the excised tumors of treated mice, but not in the secondary metastasis in the lungs. The treatment also caused changes in tumor immune microenvironment with increased infiltration of immune cells and margin inflammation. The treatment upregulated 365 genes and downregulated 710 genes in tumors compared to the untreated group. Consistent with in vitro findings in breast cancer cell lines, downregulated genes in the treated TNBC tumors include Cellular Metabolic Process Related genes (179), specifically mitochondrial genes associated with TCA cycle/oxidative phosphorylation (44), and translation machinery/ribosome biogenesis genes (45). Among upregulated genes, the Developmental Pathway (13), ECM Organization (12) and Focal Adhesion Related Pathways (7) were noteworthy. We also present data from a pilot study using a bilateral BC mouse model, which supports our findings.

Conclusion

In conclusion, although 9S1R-NulloPT was moderate at reducing the tumor volume, it altered the tumor immune microenvironment as well as the tumor transcriptome, rendering tumors metabolically less active by downregulating the mitochondrial function and ribosome biogenesis. This corroborates previously published in vitro findings.

Functional Proteomic Profiling Analysis in Four Major Types of Gastrointestinal Cancers

Gastrointestinal (GI) cancer accounts for one in four cancer cases and one in three cancer-related deaths globally. A deeper understanding of cancer development mechanisms can be applied to cancer medicine. Comprehensive sequencing applications have revealed the genomic landscapes of the common types of human cancer, and proteomics technology has identified protein targets and signalling pathways related to cancer growth and progression. This study aimed to explore the functional proteomic profiles of four major types of GI tract cancer based on The Cancer Proteome Atlas (TCPA). We provided an overview of functional proteomic heterogeneity by performing several approaches, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), t-stochastic neighbour embedding (t-SNE) analysis, and hierarchical clustering analysis in oesophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ) tumours, to gain a system-wide understanding of the four types of GI cancer. The feature selection approach, mutual information feature selection (MIFS) method, was conducted to screen candidate protein signature subsets to better distinguish different cancer types. The potential clinical implications of candidate proteins in terms of tumour progression and prognosis were also evaluated based on TCPA and The Cancer Genome Atlas (TCGA) databases. The results suggested that functional proteomic profiling can identify different patterns among the four types of GI cancers and provide candidate proteins for clinical diagnosis and prognosis evaluation. We also highlighted the application of feature selection approaches in high-dimensional biological data analysis. Overall, this study could improve the understanding of the complexity of cancer phenotypes and genotypes and thus be applied to cancer medicine.

Demystifying the Role of Prognostic Biomarkers in Breast Cancer through Integrated Transcriptome and Pathway Enrichment Analyses

Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of death in women. Researchers have discovered an increasing number of molecular targets for BC prognosis and therapy. However, it is still urgent to identify new biomarkers. Therefore, we evaluated biomarkers that may contribute to the diagnosis and treatment of BC. We searched TCGA datasets and identified differentially expressed genes (DEGs) by comparing tumor (100 samples) and non-tumor (100 samples) tissues using the Deseq2 package. Pathway and functional enrichment analysis of the DEGs was performed using the DAVID (Database for Annotation, Visualization, and Integrated Discovery) database. The protein–protein interaction (PPI) network was identified using the STRING database and visualized through Cytoscape software. Hub gene analysis of the PPI network was completed using cytohubba plugins. The associations between the identified genes and overall survival (OS) were analyzed using a Kaplan–Meier plot. Finally, we have identified hub genes at the transcriptome level. A total of 824 DEGs were identified, which were mostly enriched in cell proliferation, signal transduction, and cell division. The PPI network comprised 822 nodes and 12,145 edges. Elevated expression of the five hub genes AURKA, BUB1B, CCNA2, CCNB2, and PBK are related to poor OS in breast cancer patients. A promoter methylation study showed these genes to be hypomethylated. Validation through genetic alteration and missense mutations resulted in chromosomal instability, leading to improper chromosome segregation causing aneuploidy. The enriched functions and pathways included the cell cycle, oocyte meiosis, and the p53 signaling pathway. The identified five hub genes in breast cancer have the potential to become useful targets for the diagnosis and treatment of breast cancer.

Integrated spatial analysis of gene mutation and gene expression for understanding tumor diversity in formalin-fixed paraffin-embedded lung adenocarcinoma

Introduction

A deeper understanding of intratumoral heterogeneity is essential for prognosis prediction or accurate treatment plan decisions in clinical practice. However, due to the cross-links and degradation of biomolecules within formalin-fixed paraffin-embedded (FFPE) specimens, it is challenging to analyze them. In this study, we aimed to optimize the simultaneous extraction of mRNA and DNA from microdissected FFPE tissues (φ = 100 µm) and apply the method to analyze tumor diversity in lung adenocarcinoma before and after erlotinib administration.

Method

Two magnetic beads were used for the simultaneous extraction of mRNA and DNA. The decross-linking conditions were evaluated for gene mutation and gene expression analyses of microdissected FFPE tissues. Lung lymph nodes before treatment and lung adenocarcinoma after erlotinib administration were collected from the same patient and were preserved as FFPE specimens for 4 years. Gene expression and gene mutations between histologically classified regions of lung adenocarcinoma (pre-treatment tumor in lung lymph node biopsies and post-treatment tumor, normal lung, tumor stroma, and remission stroma, in resected lung tissue) were compared in a microdissection-based approach.

Results

Using the optimized simultaneous extraction of DNA and mRNA and whole-genome amplification, we detected approximately 4,000-10,000 expressed genes and the epidermal growth factor receptor (EGFR) driver gene mutations from microdissected FFPE tissues. We found the differences in the highly expressed cancer-associated genes and the positive rate of EGFR exon 19 deletions among the tumor before and after treatment and tumor stroma, even though they were collected from tumors of the same patient or close regions of the same specimen.

Conclusion

Our integrated spatial analysis method would be applied to various FFPE pathology specimens providing area-specific gene expression and gene mutation information.

Screening of Specific and Common Pathways in Breast Cancer Cell Lines MCF-7 and MDA-MB-231 Treated with Chlorophyllides Composites

Our previous findings have shown that the chlorophyllides composites have anticancer activities to breast cancer cell lines (MCF-7 and MDA-MB-231). In the present study, microarray gene expression profiling was utilized to investigate the chlorophyllides anticancer mechanism on the breast cancer cells lines. Results showed that chlorophyllides composites induced upregulation of 43 and 56 differentially expressed genes (DEG) in MCF-7 and MDA-MB-231 cells, respectively. In both cell lines, chlorophyllides composites modulated the expression of annexin A4 (ANXA4), chemokine C-C motif receptor 1 (CCR1), stromal interaction molecule 2 (STIM2), ethanolamine kinase 1 (ETNK1) and member of RAS oncogene family (RAP2B). Further, the KEGG annotation revealed that chlorophyllides composites modulated DEGs that are associated with the endocrine system in MCF-7 cells and with the nervous system in MDA-MB-231 cells, respectively. The expression levels of 9 genes were validated by quantitative reverse transcription PCR (RT-qPCR). The expression of CCR1, STIM2, ETNK1, MAGl1 and TOP2A were upregulated in both chlorophyllides composites treated-MCF-7 and MDA-MB-231 cells. The different expression of NLRC5, SLC7A7 and PKN1 provided valuable information for future investigation and development of novel cancer therapy.

Polypeptide-GalNAc-Transferase-13 Shows Prognostic Impact in Breast Cancer

Breast cancer is a public health concern and is currently the fifth cause of mortality worldwide. Identification of different biological subtypes is essential for clinical management; therefore, the role of pathologists is essential and useful tools for immunohistochemistry diagnosis are needed. Polypeptide-GalNAc-transferases are emerging novel biomarkers related to cancer behavior and GalNAc-T13, correlated with aggressiveness in some tumors, is an interesting candidate. Few monoclonal antibodies reacting with native proteins, and not affected by fixation and paraffin embedding, have been reported. The aim of this work was to develop a useful monoclonal antibody anti-GalNAc-T13 and to assess its potential significance in breast cancer diagnosis. We evaluated 6 human breast cancer cell lines, 338 primary breast tumors and 48 metastatic lymph nodes and looked for clinical significance correlating GalNAc-T13 expression with patients' clinical features and survival. We found high GalNAc-T13 expression in 43.8% of the cases and observed a significant higher expression in metastatic lymph nodes, correlating with worse overall survival. We hypothesized several possible molecular mechanisms and their implications. We conclude that GalNAc-T13 may be a novel biomarker in breast cancer, useful for routine pathological diagnosis. Elucidation of molecular mechanisms related to aggressiveness should contribute to understand the role of GalNAc-T13 in breast cancer biology.

Prostaglandin D2 synthase/prostaglandin D2/TWIST2 signaling inhibits breast cancer proliferation

Though the past few years have witnessed exciting achievements in targeted and immunotherapeutic treatments of all breast cancer subtypes, yet the decline in breast cancer mortality has been slowed, urging the need for further expanding options of high-quality treatments. Prostaglandin D2 synthase (PTGDS)/prostaglandin D2 (PGD2) play important roles in a variety of cancer types and show tissue-specificity, however, there are limited relevant reports in breast cancer. Therefore, the aims of the present study were to investigate the effects of PTGDS/PGD2 in breast cancer by large-scale bioinformatic analysis and in vitro experiments conducted on human breast cancer cell lines. Results of our study indicated that patients with high levels of PTGDS expression showed a reduced potential of tumor proliferation. PGD2 treatment significantly inhibited the proliferation and migration of breast cancer cells, which was mediated by the reduced expression of TWIST2. Overexpression of TWIST2 reversed the inhibitory effects of PGD2 on breast cancer cell proliferation. These results provided the novel evidence that PTGDS may play a significant role in modulating breast cancer growth, with implications for its potential use in treating breast cancer.

An immune-related signature that to improve prognosis prediction of breast cancer

Although the classic molecular subtype of breast cancer (BRCA) has been widely used in clinical diagnosis, as a highly heterogeneous malignant tumor, the classic scheme is not enough to accurately predict the prognosis of breast cancer patients. Immune cells in the tumor microenvironment (TME) are thought to play a paramount role in tumor development and driving poor prognosis. In this study, we aimed to develop a TME-associated, immune-related signature to improve prognosis prediction of BRCA. BRCA_OURS enriched transcriptomic RNA sequencing (RNA-seq) of tumor tissue was acquired from 43 breast cancer patients before any treatment. On the immune gene profiles of 43 patients from BRCA_OURS and 932 BRCA patients from The Cancer Genome Atlas (TCGA), we identified a robust immune-related signature including one positive coefficients gene (IL-10) and other 9 genes (C14orf79, C1orf168, C1orf226, CELSR2, FABP7, FGFBP1, KLRB1, PLEKHO1, and RAC2), of which the negative coefficients suggesting higher expression were correlated with better prognosis. Based on the expression of these genes, patients were grouped into the high- and low-risk group with significant overall survival (OS) (P<0.0001). The high-risk group was likely to have inferior outcomes related to several important cancer-associated pathways, including mobilizing more Golgi vesicle-mediated transport and intensive DNA double-strand breaking, which are closely related to the infiltration of immune cells and holds the key for further growing and metastasizing. Collectively, our results highlight that the immunological value within BRCA is an essential determinant of prognostic factor. Our signature may provide an effective risk stratification tool for clinical prognosis assessment of patients with BRCA.

Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers

The complexity of cancer has always been a huge issue in understanding the source of this disease. However, by appreciating its complexity, we can shed some light on crucial gene associations across and in specific cancer types. In this study, we develop a general framework to infer relevant gene biomarkers and their gene-to-gene associations using multiple gene co-expression networks for each cancer type. Specifically, we infer computationally and biologically interesting communities of genes from kidney renal clear cell carcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma data sets of The Cancer Genome Atlas (TCGA) database. The gene communities are extracted through a data-driven pipeline and then evaluated through both functional analyses and literature findings. Furthermore, we provide a computational validation of their relevance for each cancer type by comparing the performance of normal/cancer classification for our identified gene sets and other gene signatures, including the typically-used differentially expressed genes. The hallmark of this study is its approach based on gene co-expression networks from different similarity measures: using a combination of multiple gene networks and then fusing normal and cancer networks for each cancer type, we can have better insights on the overall structure of the cancer-type-specific network.

Squalene epoxidase expression is associated with breast tumor progression and with a poor prognosis in breast cancer

Differentially expressed genes (DEGs) have been previously identified using massive parallel RNA sequencing in matched normal, breast cancer (BC) and nodal metastatic tissues. Squalene epoxidase (SQLE), one of these DEGs, is a key enzyme in cholesterol synthesis. The aim of the present study was to investigate the potential involvement of SQLE in the tumorigenic process of BC and to determine its association with the clinical outcome of BC. SQLE mRNA expression was measured using reverse transcription-quantitative PCR in 10 pairs of ductal carcinoma in situ (DCIS) and BC tissues and their adjacent normal tissues. Immunohistochemical staining of SQLE on tissue microarray was performed in 26 normal breast, 79 DCIS and 198 BC samples. The role of SQLE as a prognostic biomarker in patients with BC has been verified using BreastMark. SQLE mRNA expression was significantly increased in DCIS and BC tissues compared with that in their adjacent normal tissues. High SQLE expression was detected in 0, 48.1 and 40.4% of normal breast, DCIS and BC tissues, respectively. SQLE expression in DCIS and BC tissues was significantly higher than that in normal breast tissues. High SQLE expression was observed in DCIS with higher nuclear grade, comedo-type necrosis and HER2 positivity. High SQLE expression in BC was associated with larger tumor size, nodal metastases, higher stage, HER2 subtype and distant metastatic relapse. High SQLE expression was associated with poor disease-free and overall survival, and independently predicted poor disease-free survival in patients with BC. Following BreastMark analysis, high SQLE mRNA expression in BC was significantly associated with a poor prognosis in the ‘all’, lymph node negative, lymph node positive, luminal A subtype and luminal B subtype groups. Therefore, SQLE expression may be upregulated during the tumorigenic process of BC, and high SQLE expression may be a useful biomarker for predicting a poor prognosis in patients with BC.

Decreased PTGDS Expression Predicting Poor Survival of Endometrial Cancer by Integrating Weighted Gene Co-Expression Network Analysis and Immunohistochemical Validation

Purpose

To identify key pathogenic genes and reveal the potential molecular mechanisms of endometrial cancer (EC) using bioinformatics analysis and immunohistochemistry validation.

Materials and Methods

Through weighted gene co-expression network analysis (WGCNA), a co-expression network was constructed based on the top 25% variant genes in the GSE50830 dataset downloaded from gene expression omnibus (GEO). GO and KEGG pathway enrichment analyses were performed using the DAVID online tool. Candidate genes were selected using the cytoHubba plug-in of Cytoscape, mRNA expression levels and prognostic values in EC were analyzed by Oncomine, GEPIA, and Kaplan–Meier Plotter database to determine hub genes. One hub gene was validated by immunohistochemical (IHC) staining of 116 paraffin-embedded endometrial tissues and TCGA-UCEC cohort. Genes co-expressed with this hub gene were identified by LinkedOmics. Finally, its correlation with immune infiltration was evaluated by TIMER.

Results

Three co-expression modules and five candidate genes in each module were obtained by WGCNA; four hub genes were identified (LGR5, SST, ZNF558, and PTGDS). The mRNA levels of LGR5 and SST were significantly upregulated in EC, whereas those of ZNF558 and PTGDS were significantly downregulated; the expression of all four genes was associated with EC prognosis. Further validation demonstrated that PTGDS was significantly downregulated in the EC group compared with the atypical hyperplasia and normal endometrial groups, and its low expression was an independent risk factor for worse prognosis of EC. Biological function analysis indicated that PTGDS might be involved in the adaptive immune response, leukocyte migration, as well as in the regulation of cell adhesion molecules and chemokine signaling. Additionally, PTGDS expression was positively correlated with immune infiltration status of B cells, CD4⁺ T cells and macrophages.

Conclusion

LGR5, SST, ZNF558, and PTGDS may participate in the development, progression, and prognosis of EC, in which PTGDS may be a novel biomarker and therapeutic target for EC.

Tumour-Infiltrating Immune Cell-Based Subtyping and Signature Gene Analysis in Breast Cancer Based on Gene Expression Profiles

Tumour-infiltrating immune cells have been indicated to play an important role in prognosis prediction and therapy sensitivity for breast cancer. In recent years, estimating the abundance of immune cells based on tumour transcriptome data has provided a novel way to analyse the clinical significance of various immune cell subsets. This study integrated breast cancer tissue transcriptome datasets from the Gene Expression Omnibus (GEO), the Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohorts. A novel breast cancer immunotyping and a new prognostic model based on tumour-infiltrating immune cell subsets have been established, aiming to provide new clues regarding prognostic prediction and precision therapy for breast cancer. The key differentially expressed gene between different breast cancer immunotypes has also been identified. We performed unsupervised clustering analysis and construct a novel immunotyping which could classify breast cancer cases into immunotype A (B_cell^high NK^high CD8⁺_T^high CD4⁺_memory_T_activated^high γδT^low Mast_cell_activated^low Neutrophil^low) and immunotype B (B_cell^low NK^low CD8⁺_T^low CD4⁺_memory_T_activated^low γδT^high Mast_cell_activated^high Neutrophil^high) in luminal B, HER2-enriched and basal-like subtypes. The 5-year (85.7% vs. 73.4%) and 10-year OS (75.60% vs. 61.73%) of immunotype A population were significantly higher than those of immunotype B. A novel tumour-infiltrating immune cell-based prognostic model had also been established and the result immunorisk score (IRS) could serve as a new prognostic factor for luminal B, HER2-enriched and basal-like breast cancer. The higher IRS was, the worse prognosis was. We further screened the differentially expressed genes between immunotype A and B and identified a novel breast cancer immune-related gene, prostaglandin D2 synthase (PTGDS) and higher PTGDS mRNA expression level was positively correlated with earlier TNM stage. Immune-related signaling pathways analysis and immune cell subsets correlation analysis revealed that PTGDS expression was related with abundance of B cells, CD4⁺ T cells and CD8⁺ T cells, which was finally validated by immunohistochemical and immunofluorescence staining. We established a novel immunotyping and a tumour-infiltrating immune cell-based prognostic prediction model in luminal B, HER2-enriched and basal-like breast cancer by analyzing the prognostic significance of multiple immune cell subsets. A novel breast cancer immune signature gene PTDGS was discovered, which might serve as a protective prognostic factor and play an important role in breast cancer development and lymphocyte-related immune response.

Update on the human and mouse lipocalin (LCN) gene family, including evidence the mouse Mup cluster is result of an "evolutionary bloom"

Lipocalins (LCNs) are members of a family of evolutionarily conserved genes present in all kingdoms of life. There are 19 LCN-like genes in the human genome, and 45 Lcn-like genes in the mouse genome, which include 22 major urinary protein (Mup) genes. The Mup genes, plus 29 of 30 Mup-ps pseudogenes, are all located together on chromosome (Chr) 4; evidence points to an “evolutionary bloom” that resulted in this Mup cluster in mouse, syntenic to the human Chr 9q32 locus at which a single MUPP pseudogene is located. LCNs play important roles in physiological processes by binding and transporting small hydrophobic molecules —such as steroid hormones, odorants, retinoids, and lipids—in plasma and other body fluids. LCNs are extensively used in clinical practice as biochemical markers. LCN-like proteins (18–40 kDa) have the characteristic eight β-strands creating a barrel structure that houses the binding-site; LCNs are synthesized in the liver as well as various secretory tissues. In rodents, MUPs are involved in communication of information in urine-derived scent marks, serving as signatures of individual identity, or as kairomones (to elicit fear behavior). MUPs also participate in regulation of glucose and lipid metabolism via a mechanism not well understood. Although much has been learned about LCNs and MUPs in recent years, more research is necessary to allow better understanding of their physiological functions, as well as their involvement in clinical disorders.