Bioinformatics analysis of prognostic significance of COL10A1 in breast cancer

Upregulation of collagen type X alpha 1 promotes the progress of triple-negative breast cancer via Wnt/β-catenin signaling

Triple-negative breast cancer (TNBC) is a malignant tumor with high degree of malignancy and lack of effective target treatment. The research aims to explore the role and mechanism of X collagen alpha-1 chain protein (COL10A1 gene) in TNBC. UALCAN and Kaplan-Meier were used to detect the expression of COL10A1 and its role in the prognosis of breast cancer patients. The cells with stably expressing high levels of COL10A1 were obtained by recombinant lentivirus infection. The expression of COL10A1 in cells was temporarily downregulated by siRNA interference fragments. Real-time quantitative polymerase chain reaction and western blot analysis were utilized to detect the changes of COL10A1 mRNA and protein expression. The biological functions of the cells were evaluated by colony formation, cell counting kit-8, cell invasion and wound healing experiments. In addition, the effect of COL10A1 on angiogenesis was investigated by tube formation assay. Xenograft tumor model was used to confirm the effect of COL10A1 on tumorigenicity in vivo and multiplex fluorescent immunohistochemistry to detect multiple proteins simultaneously. The possible molecular mechanism of the function of COL10A1 was speculated through the detection of proteins in functionally related pathways. COL10A1 is highly expressed and is significantly associated with worse overall survival (OS) and recurrence-free survival (RFS) in TNBC. Overexpression of COL10A1 increased the clone formation rate and cell migration capacity of TNBC cells. In the COL10A1 overexpression group, the clone formation rates of MD-MB-231 and BT-549 cells (21.5 ± 0.62, 27.83 ± 3.72)% were significantly higher than those in the control group(15.23 ± 2.79, 19.4 ± 1.47)%, and the relative migration ratio (47.40 ± 3.09, 41.26 ± 4.33)% were higher than those in the control group (34.48 ± 2.03, 21.80 ± 1.03)%. When the expression of COL10A1 was downregulated, the ability of clone formation and wound-healing migration capacity in TNBC cells was weakened. Upregulated COL10A1 in TNBC cells generated more junctions and longer total segments between vascular endothelial cells, and promoted angiogenesis of the cells, and thus enhanced the tumorigenesis. In TNBC, it was found that COL10A1 might affect epithelial-mesenchymal transition (EMT) of the cells through Wnt/β-catenin signaling pathway by the detection of the related pathway proteins. COL10A1 is highly expressed in TNBC, and its high expression leads to poor OS and RFS. COL10A1 may enhance TNBC cell proliferation, migration and tumor-related angiogenesis, and promote tumorigenesis in vivo via Wnt/β-catenin signaling.

Unveiling Collagen's Role in Breast Cancer: Insights into Expression Patterns, Functions and Clinical Implications

Collagen, the predominant protein constituent of the mammalian extracellular matrix (ECM), comprises a diverse family of 28 members (I-XXVIII). Beyond its structural significance, collagen is implicated in various diseases or cancers, notably breast cancer, where it influences crucial cellular processes including proliferation, metastasis, apoptosis, and drug resistance, intricately shaping cancer progression and prognosis. In breast cancer, distinct collagens exhibit differential expression profiles, with some showing heightened or diminished levels in cancerous tissues or cells compared to normal counterparts, suggesting specific and pivotal biological functions. In this review, we meticulously analyze the expression of individual collagen members in breast cancer, utilizing Transcripts Per Million (TPM) data sourced from the GEPIA2 database. Through this analysis, we identify collagens that deviate from normal expression patterns in breast cancer, providing a comprehensive overview of their expression dynamics, functional roles, and underlying mechanisms. Our findings shed light on recent advancements in understanding the intricate interplay between these aberrantly expressed collagens and breast cancer. This exploration aims to offer valuable insights for the identification of potential biomarkers and therapeutic targets, thereby advancing the prospects of more effective interventions in breast cancer treatment.

COL10A1 as a Prognostic Biomarker in Association with Immune Infiltration in Prostate Cancer

BACKGROUND

The collagen type X alpha 1 (COL10A1) has recently been found to play an important role in the development and progression of cancer. However, the link between COL10A1 and the tumor immune microenvironment remains understood scantily.

METHODS

In the current study, the pan-cancer data of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were used to investigate the expression mode, the clinical prognostic and diagnostic value of COL10A1 in different tumors. We used TCGA data to assess the correlations between COL10A1 and clinical symptoms of prostate cancer. The R packages "edgR" and "clusterProfiler" were used for differential expression gene and enrichment analysis of COL10A1. Immunohistochemistry was further employed to corroborate the expression of COL10A1 gene in prostate cancer. After that, we used TIMER to evaluate the pertinence of COL10A1 expression to immune infiltration level in prostate cancer.

RESULTS

On the whole, COL10A1 was expressed at significantly higher levels in a variety of tumor tissues than in the corresponding normal tissues. Besides, significant correlations with tumor prognosis and relative exactitude in predicting tumors show that COL10A1 may be a probable prognostic and diagnostic biomarker of prostate cancer. In addition, the evidence indicates a significant correlation between COL10A1 and clinical symptoms of prostate cancer. Furthermore, the main molecular functions of COL10A1 included humoral immune response, complement activation, immunoglobulin, regulation of complement activation, and regulation of humoral immune response. Finally, we found that COL10A1 expression is positively correlated with enhanced macrophage and M2 macrophage infiltration in prostate cancer.

CONCLUSION

The study indicates that COL10A1 might participate in M2 macrophage polarization in prostate cancer. COL10A1 might be an innovative biomarker to evaluate tumor microenvironment immune cell infiltration and prognosis in prostate cancer.

COL10A1 promotes tumorigenesis by modulating CD276 in pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PAAD) is a lethal malignant tumour. Further study is needed to determine the molecular mechanism and identify novel biomarkers of PAAD.

METHODS

Gene expression data from the GSE62165 microarray were analysed with the online software Morpheus to identify differentially expressed genes (DEGs). The STRING database was used to generate a protein‒protein interaction (PPI) network for these DEGs. Hub genes were identified with Cytoscape. COL10A1 expression in PAAD was analysed via the GEPIA database. COL10A1 expression in pancreatic cancer cell lines was measured by using qRT‒PCR. The LinkedOmics database was utilized to perform survival analysis of pancreatic adenocarcinoma patients grouped based on COL10A1 expression level. CCK-8, wound healing, and Transwell assays were used to study the role of COL10A1 in pancreatic cancer cell viability, migration, and invasion. Differentially expressed genes that were related to COL10A1 in PAAD were analysed via the LinkedOmics portal. After COL10A1 was knocked down, CD276 expression was assessed by western blotting.

RESULTS

COL10A1 was identified as one of the hub genes in PAAD by bioinformatics analysis of the GSE62165 microarray with Morpheus, the STRING database and Cytoscape. GEPIA revealed elevated expression of COL10A1 in PAAD samples vs. normal samples. COL10A1 expression was also increased in pancreatic cancer cells vs. control cells. Survival analysis of PAAD patients via LinkedOmics revealed that high expression of COL10A1 was associated with a poorer prognosis. Knockdown of COL10A1 inhibited the proliferation, migration, and invasion of cells in functional assays. Furthermore, mechanistic studies indicated that CD276 was a target of COL10A1 and that knockdown of COL10A1 decreased CD276 expression. Overexpression of CD276 in cells reversed COL10A1 knockdown-induced repression of proliferation and migration.

CONCLUSIONS

Our research suggests that COL10A1 promotes pancreatic adenocarcinoma tumorigenesis by regulating CD276. This study provides new insight into biomarkers and possible targets for pancreatic cancer treatment.

Recent progress on the effect of extracellular matrix on occurrence and progression of breast cancer

Breast cancer (BC) metastasis is an enormous challenge targeting BC therapy. The extracellular matrix (ECM), the principal component of the BC metastasis niche, is the pivotal driver of breast tumor development, whose biochemical and biophysical characteristics have attracted widespread attention. Here, we review the biological effects of ECM constituents and the influence of ECM stiffness on BC metastasis and drug resistance. We provide an overview of the relative signal transduction mechanisms, existing metastasis models, and targeted drug strategies centered around ECM stiffness. It will shed light on exploring more underlying targets and developing specific drugs aimed at ECM utilizing biomimetic platforms, which are promising for breast cancer treatment.

BrcaDx: precise identification of breast cancer from expression data using a minimal set of features

Background: Breast cancer is the foremost cancer in worldwide incidence, surpassing lung cancer notwithstanding the gender bias. One in four cancer cases among women are attributable to cancers of the breast, which are also the leading cause of death in women. Reliable options for early detection of breast cancer are needed. Methods: Using public-domain datasets, we screened transcriptomic profiles of breast cancer samples, and identified progression-significant linear and ordinal model genes using stage-informed models. We then applied a sequence of machine learning techniques, namely, feature selection, principal components analysis, and k-means clustering, to train a learner to discriminate "cancer" from "normal" based on expression levels of identified biomarkers. Results: Our computational pipeline yielded an optimal set of nine biomarker features for training the learner, namely, NEK2, PKMYT1, MMP11, CPA1, COL10A1, HSD17B13, CA4, MYOC, and LYVE1. Validation of the learned model on an independent test dataset yielded a performance of 99.5% accuracy. Blind validation on an out-of-domain external dataset yielded a balanced accuracy of 95.5%, demonstrating that the model has effectively reduced the dimensionality of the problem, and learnt the solution. The model was rebuilt using the full dataset, and then deployed as a web app for non-profit purposes at: https://apalania.shinyapps.io/brcadx/. To our knowledge, this is the best-performing freely available tool for the high-confidence diagnosis of breast cancer, and represents a promising aid to medical diagnosis.

Tumor-restrictive type III collagen in the breast cancer microenvironment: prognostic and therapeutic implications

Collagen plays a critical role in regulating breast cancer progression and therapeutic resistance. An improved understanding of both the features and drivers of tumor-permissive and -restrictive collagen matrices are critical to improve prognostication and develop more effective therapeutic strategies. In this study, using a combination of in vitro, in vivo and in silico experiments, we show that type III collagen (Col3) plays a tumor-restrictive role in human breast cancer. We demonstrate that Col3-deficient, human fibroblasts produce tumor-permissive collagen matrices that drive cell proliferation and suppress apoptosis in noninvasive and invasive breast cancer cell lines. In human TNBC biopsy samples, we demonstrate elevated deposition of Col3 relative to type I collagen (Col1) in noninvasive compared to invasive regions. Similarly, in silico analyses of over 1000 breast cancer patient biopsies from The Cancer Genome Atlas BRCA cohort revealed that patients with higher Col3:Col1 bulk tumor expression had improved overall, disease-free and progression-free survival relative to those with higher Col1:Col3 expression. Using an established 3D culture model, we show that Col3 increases spheroid formation and induces formation of lumen-like structures that resemble non-neoplastic mammary acini. Finally, our in vivo study shows co-injection of murine breast cancer cells (4T1) with rhCol3-supplemented hydrogels limits tumor growth and decreases pulmonary metastatic burden compared to controls. Taken together, these data collectively support a tumor-suppressive role for Col3 in human breast cancer and suggest that strategies that increase Col3 may provide a safe and effective modality to limit recurrence in breast cancer patients.

Identification of molecularly unique tumor-associated mesenchymal stromal cells in breast cancer patients

The tumor microenvironment is a complex mixture of cell types that bi-directionally interact and influence tumor initiation, progression, recurrence, and patient survival. Mesenchymal stromal cells (MSCs) of the tumor microenvironment engage in crosstalk with cancer cells to mediate epigenetic control of gene expression. We identified CD90+ MSCs residing in the tumor microenvironment of patients with invasive breast cancer that exhibit a unique gene expression signature. Single-cell transcriptional analysis of these MSCs in tumor-associated stroma identified a distinct subpopulation characterized by increased expression of genes functionally related to extracellular matrix signaling. Blocking the TGFβ pathway reveals that these cells directly contribute to cancer cell proliferation. Our findings provide novel insight into communication between breast cancer cells and MSCs that are consistent with an epithelial to mesenchymal transition and acquisition of competency for compromised control of proliferation, mobility, motility, and phenotype.

Prognostic value of COL10A1 and its correlation with tumor-infiltrating immune cells in urothelial bladder cancer: A comprehensive study based on bioinformatics and clinical analysis validation

Introduction

Bladder cancer (BLCA) is one of the most lethal diseases. COL10A1 is secreted small-chain collagen in the extracellular matrix associated with various tumors, including gastric, colon, breast, and lung cancer. However, the role of COL10A1 in BLCA remains unclear. This is the first research focusing on the prognostic value of COL10A1 in BLCA. In this research, we aimed to uncover the association between COL10A1 and the prognosis, as well as other clinicopathological parameters in BLCA.

Methods

We obtained gene expression profiles of BLCA and normal tissues from the TCGA, GEO, and ArrayExpress databases. Immunohistochemistry staining was performed to investigate the protein expression and prognostic value of COL10A1 in BLCA patients. GO and KEGG enrichment along with GSEA analyses were performed to reveal the biological functions and potential regulatory mechanisms of COL10A1 based on the gene co-expression network. We used the “maftools” R package to display the mutation profiles between the high and low COL10A1 groups. GIPIA2, TIMER, and CIBERSORT algorithms were utilized to explore the effect of COL10A1 on the tumor immune microenvironment.

Results

We found that COL10A1 was upregulated in the BLCA samples, and increased COL10A1 expression was related to poor overall survival. Functional annotation of 200 co-expressed genes positively correlated with COL10A1 expression, including GO, KEGG, and GSEA enrichment analyses, indicated that COL10A1 was basically involved in the extracellular matrix, protein modification, molecular binding, ECM-receptor interaction, protein digestion and absorption, focal adhesion, and PI3K-Akt signaling pathway. The most commonly mutated genes of BLCA were different between high and low COL10A1 groups. Tumor immune infiltrating analyses showed that COL10A1 might have an essential role in recruiting infiltrating immune cells and regulating immunity in BLCA, thus affecting prognosis. Finally, external datasets and biospecimens were used, and the results further validated the aberrant expression of COL10A1 in BLCA samples.

Conclusions

In conclusion, our study demonstrates that COL10A1 is an underlying prognostic and predictive biomarker in BLCA.

Unusual Suspects: Bone and Cartilage ECM Proteins as Carcinoma Facilitators

The extracellular matrix (ECM) is the complex three-dimensional network of fibrous proteins and proteoglycans that constitutes an essential part of every tissue to provide support for normal tissue homeostasis. Tissue specificity of the ECM in its topology and structure supports unique biochemical and mechanical properties of each organ. Cancers, like normal tissues, require the ECM to maintain multiple processes governing tumor development, progression and spread. A large body of experimental and clinical evidence has now accumulated to demonstrate essential roles of numerous ECM components in all cancer types. Latest findings also suggest that multiple tumor types express, and use to their advantage, atypical ECM components that are not found in the cancer tissue of origin. However, the understanding of cancer-specific expression patterns of these ECM proteins and their exact roles in selected tumor types is still sketchy. In this review, we summarize the latest data on the aberrant expression of bone and cartilage ECM proteins in epithelial cancers and their specific functions in the pathogenesis of carcinomas and discuss future directions in exploring the utility of this selective group of ECM components as future drug targets.

Cancer-Associated Fibroblasts Hinder Lung Squamous Cell Carcinoma Oxidative Stress-Induced Apoptosis via METTL3 Mediated m6A Methylation of COL10A1

Background. Cancer-associated fibroblasts (CAFs) within the tumor microenvironment are key players in tumorigenesis and tumor development. Nevertheless, the regulatory mechanisms of CAFs on lung squamous cell carcinoma- (LUSC-) associated remain poorly elucidated. Methods. The microarray dataset GSE22874, containing 30 specimens of primary culture of normal fibroblasts (NFs) and 8 specimens of cancer-associated fibroblasts (CAFs) samples derived from LUSC, was retrieved from the Gene Expression Omnibus (GEO) database and then calculated by using the R language (limma package) to identify differentially expressed genes (DEGs). CAF-conditioned medium (CAF-CM) was collected and used to culture LUSC cells, followed by assessment of cell proliferation, apoptosis, and oxidative stress levels by using CCK-8, annexin V-FITC/PI double staining and ELISA assays. Subsequently, COL10A1 was knocked down in CAFs to assess the role of COL10A1 in CAF regulation of LUSC behavior. Bioinformatics online analysis and MeRIP were applied to predict and test the m6A modification of COL10A1 mRNA and the regulatory relationship with METTL3. Rescue experiments were next performed to explore the effects of METTL3 and COL10A1 in CAFs on LUSC cell proliferation, apoptosis, and oxidative stress. LUSC tumor cells with or without (COL10A1-silenced) CAFs were subcutaneously inoculated in nude mice to evaluate the effect of COL10A1 in CAFs on LUSC tumor growth. Results. Elevated expression of COL10A1 was found in LUSC-derived CAFs by GSE22874 dataset analysis. We discovered that COL10A1 and METTL3 was expressed in both LUSC cells and matched CAFs, while COL10A1 expression was prominently higher in CAFs than in LUSC cells. CAF-CM memorably encouraged LUSC cell proliferation and suppressed apoptosis-induced oxidative stress, which was reversed by interfering with COL10A1 expression in CAFs, suggesting that COL10A1 might be secreted by CAFs into the culture medium to exert its effects inside LUSC cells. Global m6A modification was decreased in METTL3 knocked down CAFs. M6A modification, expression levels, and stability of COL10A1 mRNA were impaired upon METTL3 knockdown in CAFs. Overexpression of COL10A1 in CAFs partially reversed the effect of METTL3 knockdown on the malignant behavior of LUSC cells. In vivo studies confirmed that CAFs accelerated LUSC tumor growth, and this effect was counteracted by COL10A1 silencing. Conclusions. COL10A1 secreted by CAFs could facilitate LUSC cell proliferation and repress apoptosis-induced oxidative stress, and the mechanism was due to elevated expression mediated by METTL3 promoting its mRNA m6A modification, thereby accelerating tumor growth.

Bioinformatics-Based Analysis: Noncoding RNA-Mediated COL10A1 Is Associated with Poor Prognosis and Immune Cell Infiltration in Pancreatic Cancer

Background. Collagen type X alpha 1 (COL10A1) is a structural component of the extracellular matrix that is aberrantly expressed in a variety of cancer tissues. However, its role in pancreatic cancer progression is not well understood. Methods. The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Gene Expression Profiling Interaction Analysis (GEPIA) data were employed to explore the expression of COL10A1 in normal and tumor tissues and its prognostic value in pancreatic adenocarcinoma. The clinical data of pancreatic cancer in TCGA were used to explore the relationship between COL10A1 and clinical features. Genes coexpressed with COL10A1 were explored using multiple databases and analyzed for functional enrichment. In addition, the lncRNA/miRNA/COL10A1 axis that may be involved in COL10A1 regulation in pancreatic cancer was explored by constructing a competitive endogenous RNA (ceRNA) regulatory axis. Finally, COL10A1 was analyzed for correlation with immune cell infiltration and various immune checkpoint molecules in pancreatic cancer. Results. It was found that the expression of COL10A1 was significantly increased in pancreatic cancer tissues. High expression of COL10A1 was related to the clinicopathological characteristics and the worse prognosis of pancreatic cancer patients. The TUG1/miR-144-3p/COL10A1 axis was identified as the most likely upstream noncoding RNA pathway for COL10A1 in pancreatic cancer. Besides, in pancreatic adenocarcinoma, the expression level of COL10A1 showed a significant positive correlation with tumor immune cell infiltration, biomarkers of immune cells, and expression of immune checkpoint molecules. Conclusion. COL10A1 is an early diagnostic marker, and its high expression correlates with immune infiltration in pancreatic cancer. The TUG1/miR-144-3p/COL10A1 axis was identified as the most likely upstream noncoding RNA pathway for COL10A1 in pancreatic cancer.

High COL10A1 expression potentially contributes to poor outcomes in gastric cancer with the help of LEF1 and Wnt2

Background

COL10A1 is a secreted, short‐chain collagen found in several types of cancer. Studies have shown that COL10A1 aberrant expression is considered an oncogenic factor. However, its underlying mechanisms and regulation of gastric cancer remain undefined.

Methods

The data on the expression of COL10A1, clinicopathological characteristics, and outcome of patients with GC were obtained from The Cancer Genome Atlas. The ALGGEN‐PROMO database defined the related transcription factors. Quantitative real‐time reverse transcription‐polymerase chain reaction and western blotting analysis were used to identify the differential expression levels of COL10A1 and related transcription factors.

Results

We found that high COL10A1 expression is an independent risk factor for gastric cancer. Upregulation of LEF1 and Wnt2 was also observed in gastric cancer, suggesting a potential correlation between LEF1/COL10A1 regulation in the Wnt2 signaling pathway.

Conclusion

High COL10A1 expression may contribute to poor outcomes via upregulation of LEF1 and Wnt2 in gastric cancer.

Burden, trends, and risk factors for breast cancer in China from 1990 to 2019 and its predictions until 2034: an up-to-date overview and comparison with those in Japan and South Korea

BACKGROUND

The difference in epidemiological characteristics of breast cancer (BC) across countries is valuable for BC management and prevention. The study evaluated the up-to-date burden, trends, and risk factors of BC in China, Japan and South Korea during 1990-2019 and predicted the BC burden until 2034.

METHODS

Data on incident cases, deaths, disability-adjusted life-years (DALYs) and age-standardized rate (ASR) of BC were extracted from the Global Burden of Disease Study 2019. Trend analysis and prediction until 2034 were conducted by estimated annual percentage change and a Bayesian age-period-cohort model, respectively. Besides, the attributable burden to BC risk factors was also estimated.

RESULTS

In 2019, the number of BC incident cases, deaths and DALYs in China were 375,484, 96,306 and 2,957,453, respectively. The ASR of incidence increased, while that of death and DALYs decreased for Chinese females and Japanese and South Korean males during 1990-2019. High body-mass-index (BMI) was the largest contributor to Chinese female BC deaths and DALYs, while alcohol use was the greatest risk factor for Japanese and South Korean as well as Chinese males. The incident cases and deaths were expected to continue increase during 2020-2034 (except for Japanese female incident cases).

CONCLUSIONS

China had the greatest burden of BC among the three countries. Incident cases and deaths of BC were projected to increase over the next 15 years in China, particularly among Chinese males. Effective prevention and management strategies are urgently necessary for BC control in China.

Role of germline variants in the metastasis of breast carcinomas

Most cancer-related deaths in breast cancer patients are associated with metastasis, a multistep, intricate process that requires the cooperation of tumour cells, tumour microenvironment and metastasis target tissues. It is accepted that metastasis does not depend on the tumour characteristics but the host’s genetic makeup. However, there has been limited success in determining the germline genetic variants that influence metastasis development, mainly because of the limitations of traditional genome-wide association studies to detect the relevant genetic polymorphisms underlying complex phenotypes. In this work, we leveraged the extreme discordant phenotypes approach and the epistasis networks to analyse the genotypes of 97 breast cancer patients. We found that the host’s genetic makeup facilitates metastases by the dysregulation of gene expression that can promote the dispersion of metastatic seeds and help establish the metastatic niche—providing a congenial soil for the metastatic seeds.

Identification of Key Genes and Key Pathways in Breast Cancer Based on Machine Learning

BACKGROUND Breast cancer is one of the most common malignant tumors among women worldwide. This study aimed to screen key genes and pathways for breast cancer diagnosis and treatment. MATERIAL AND METHODS We obtained public data from the NCBI GEO database. The data were divided into a control group (normal breast tissue) and a treatment group (breast cancer tissue). We screened 32 differentially expressed genes (DEGs) between normal breast and cancerous tissues and used GO analysis and GSEA to identify the key pathways. We then combined LASSO and SVM-RFE analyses to screen key genes, and used CIBERSORT to obtain the proportion of 22 types of immune cells. The relationships between key genes and immune-infiltrating cells were further explored. RESULTS We screened 32 DEGs from the 2 groups, including 27 downregulated genes and 5 upregulated genes. GO analysis indicated that the DEGs were mainly correlated with collagen-containing extracellular matrix (ECM), Wnt signaling pathway, and glycosaminoglycan binding. GSEA indicated that the treatment group was correlated with chromosome segregation and cell cycle while the control group was correlated with cornification, intermediate filament, and nuclear transcription. Through machine learning, SYNM, TGFBR3, and COL10A1 were screened as key genes. Numbers of CD8 T cells, gamma delta T cells, and M1 macrophages were significantly higher, while monocytes and follicular helper-T cells were significantly lower in the treatment group. The downregulated genes, SYNM and TGFBR3, were positively correlated with CD8 T cells and monocytes, but were negatively correlated with gamma delta T cells and M1 macrophages. The upregulated gene, COL10A1, was positively correlated with gamma delta T cells and M1 macrophages, and was negatively correlated with CD8 T cells, monocytes, and follicular helper-T cells. CONCLUSIONS SYNM, TGFBR3, and COL10A1 are diagnostic genes of breast cancer. They affect breast cancer cells by modulating immune-infiltrating cells.

High Expression of RAI14 in Triple-Negative Breast Cancer Participates in Immune Recruitment and Implies Poor Prognosis Through Bioinformatics Analyses

Objective: The purpose of current research is to explore the function of retinoic acid-induced protein 14 (RAI14), being a reciprocal protein of carboxypeptidase N1 (CPN1), and as a biomarker for prognosis and immunoregulatory effects in breast cancers.

Methods: Interacting proteins of CPN1 were characterized by co-immunoprecipitation (CO-IP) and mass spectrometry. We evaluated RAI14 expression and related clinical prognosis based on bioinformatics methods. The level of relevance between RAI14 and infiltrating immune cells biomarkers was investigated by using TIMER and certificated by immunohistochemical staining and cytology experiments.

Results: RAI14 is an interacting protein of CPN1. Higher RAI14 expression in TNBC was significantly correlated with poor prognosis in TNBC, especially (RFS: HR = 1.32, p = 0.015; DFS: HR = 1.18, p = 0.035). The estrogen receptor (ER), P53 status, and histological types and triple-negative status were observed and correlated with RAI14 expression. Moreover, the level of RAI14 was positive in relation with the expression of CD163 (M2 macrophages marker, r = 0.393, p = 1.89e-06) and PD-1 (T-cell exhaustion marker, r = 0.626, p = 4.82e-03), indicating RAI14 levels were mainly related to M2 macrophages and T-cell exhaustion infiltration in TNBC. Furthermore, CPN1 overexpression was accompanied by RAI14 and PD-L1 upregulation, and a correlation was found among them.

Conclusions: RAI14 is a potential downstream molecule of CPN1, which may be a potential prognostic biomarker and identification of an immunosuppressive tumor microenvironment in TNBC.

The role of network-forming collagens in cancer progression

Collagens are the main components of extracellular matrix in the tumor microenvironment. Both fibrillar and nonfibrillar collagens are involved in tumor progression. The nonfibrillar network-forming collagens such as type IV and type VIII collagens are frequently overexpressed in various types of human cancers, which promotes tumor cell proliferation, adhesion, invasion, metastasis and angiogenesis. Studies on the roles of these collagens have shed light on the mechanisms underpinning the effects of this protein family. Future research has to explicit the role of network-forming collagens with respect to cancer progression and treatment. Herein, we review the regulation of network-forming collagens expression in cancer; the roles of network-forming collagens in tumor invasion, metastasis and angiogenesis; and the clinical significance of network-forming collagens expression in cancer patients. This article is protected by copyright. All rights reserved.

Collagen Family and Other Matrix Remodeling Proteins Identified by Bioinformatics Analysis as Hub Genes Involved in Gastric Cancer Progression and Prognosis

Gastric cancer has remained in the top five cancers for over ten years, both in terms of incidence and mortality due to the shortage of biomarkers for disease follow-up and effective therapies. Aiming to fill this gap, we performed a bioinformatics assessment on our data and two additional GEO microarray profiles, followed by a deep analysis of the 40 differentially expressed genes identified. PPI network analysis and MCODE plug-in pointed out nine upregulated hub genes coding for proteins from the collagen family (COL12A1, COL5A2, and COL10A1) or involved in the assembly (BGN) or degradation of collagens (CTHRC1), and also associated with cell adhesion (THBS2 and SPP1) and extracellular matrix degradation (FAP, SULF1). Those genes were highly upregulated at the mRNA and protein level, the increase being correlated with pathological T stages. The high expression of BGN (p = 8 × 10⁻¹²), THBS2 (p = 1.2 × 10⁻⁶), CTHRC1 (p = 1.1 × 10⁻⁴), SULF1 (p = 3.8 × 10⁻⁴), COL5A1 (p = 1.3 × 10⁻⁴), COL10A1 (p = 5.7 × 10⁻⁴), COL12A1 (p = 2 × 10⁻³) correlated with poor overall survival and an immune infiltrate based especially on immunosuppressive M2 macrophages (p-value range 4.82 × 10⁻⁷–1.63 × 10⁻¹³). Our results emphasize that these genes could be candidate biomarkers for GC progression and prognosis and new therapeutic targets.

Identification of genes and pathways related to breast cancer metastasis in an integrated cohort

BACKGROUND

Breast cancer is the most common malignant disease in women. Metastasis is the most common cause of death from this cancer. Screening genes related to breast cancer metastasis may help elucidate the mechanisms governing metastasis and identify molecular targets for antimetastatic therapy. The development of advanced algorithms enables us to perform cross-study analysis to improve the robustness of the results.

MATERIALS AND METHODS

Ten data sets meeting our criteria for differential expression analyses were obtained from the Gene Expression Omnibus (GEO) database. Among these data sets, five based on the same platform were formed into a large cohort using the XPN algorithm. Differentially expressed genes (DEGs) associated with breast cancer metastasis were identified using the differential expression via distance synthesis (DEDS) algorithm. A cross-platform method was employed to verify these DEGs in all ten selected data sets. The top 50 validated DEGs are represented with heat maps. Based on the validated DEGs, Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Protein interaction (PPI) networks were constructed to further illustrate the direct and indirect associations among the DEGs. Survival analysis was performed to explore whether these genes can affect breast cancer patient prognosis.

RESULTS

A total of 817 DEGs were identified using the DEDS algorithm. Of these DEGs, 450 genes were validated by the second algorithm. Enriched KEGG pathway terms demonstrated that these 450 DEGs may be involved in the cell cycle and oocyte meiosis in addition to their functions in ECM-receptor interaction and protein digestion and absorption. PPI network analysis for the proteins encoded by the DEGs indicated that these genes may be primarily involved in the cell cycle and extracellular matrix. In particular, several genes played roles in multiple signalling pathways and were related to patient survival. These genes were also observed to be targetable in the CTD2 database.

CONCLUSIONS

Our study analysed multiple cross-platform data sets using two different algorithms, helping elucidate the molecular mechanisms and identify several potential therapeutic targets of metastatic breast cancer. In addition, several genes exhibited promise for applications in targeted therapy against metastasis in future research.

Multi-Omics Marker Analysis Enables Early Prediction of Breast Tumor Progression

Ductal carcinoma in situ (DCIS) is a preinvasive form of breast cancer with a highly variable potential of becoming invasive and affecting mortality of the patients. Due to the lack of accurate markers of disease progression, many women with detected DCIS are currently overtreated. To distinguish those DCIS cases who are likely to require therapy from those who should be left untreated, there is a need for robust and predictive biomarkers extracted from molecular or genetic profiles. We developed a supervised machine learning approach that implements multi-omics feature selection and model regularization for the identification of biomarker combinations that could be used to distinguish low-risk DCIS lesions from those with a higher likelihood of progression. To investigate the genetic heterogeneity of disease progression, we applied this approach to 40 pure DCIS and 259 invasive breast cancer (IBC) samples profiled with genome-wide transcriptomics, DNA methylation, and DNA copy number variation. Feature selection using the multi-omics Lasso-regularized algorithm identified both known genes involved in breast cancer development, as well as novel markers for early detection. Even though the gene expression-based model features led to the highest classification accuracy alone, methylation data provided a complementary source of features and improved especially the sensitivity of correctly classifying DCIS cases. We also identified a number of repeatedly misclassified DCIS cases when using either the expression or methylation markers. A small panel of 10 gene markers was able to distinguish DCIS and IBC cases with high accuracy in nested cross-validation (AU-ROC = 0.99). The marker panel was not specific to any of the established breast cancer subtypes, suggesting that the 10-gene signature may provide a subtype-agnostic and cost-effective approach for breast cancer detection and patient stratification. We further confirmed high accuracy of the 10-gene signature in an external validation cohort (AU-ROC = 0.95), profiled using distinct transcriptomic assay, hence demonstrating robustness of the risk signature.

Collagen Type X Alpha 1 (COL10A1) Contributes to Cell Proliferation, Migration, and Invasion by Targeting Prolyl 4-Hydroxylase Beta Polypeptide (P4HB) in Breast Cancer

Background

Breast cancer, a common malignant tumor, has been considered as the leading cause of cancer-related death in women. Collagen type X alpha 1 (COL10A1) is overexpressed in breast cancer. The current study was designed to determine the functional involvement and regulatory mechanism of COL10A1 on the growth and metastasis of breast cancer.

Material/Methods

COL10A1 and Prolyl 4-hydroxylase beta polypeptide (P4HB) expressions in normal tissues and tumor tissues of breast cancer patients were obtained from the GEPIA dataset. COL10A1 and P4HB levels in breast cancer cell lines were detected by real-time quantitative polymerase chain reaction (RT-qPCR) and western blot analysis. Furthermore, the interaction between COL10A1 and P4HB was confirmed by co-immunoprecipitation (Co-IP) assay. Cell Counting Kit-8 (CCK-8) and colony formation assay were applied to evaluate cell proliferation and clone-forming abilities of breast cancer cells. In addition, wound healing assay and transwell assay were performed to measure cell migration and invasion capabilities, respectively, in breast cancer.

Results

The GEPIA dataset presented overexpressed COL10A1 and P4HB in tumor tissues of breast cancer patients. COL10A1 and P4HB expression levels were greatly upregulated in breast cancer cell lines. In addition, COL10A1 could directly interact with P4HB. Functionally, overexpressed COL10A1 boosted the proliferation and metastasis of breast cancer cells and silenced COL10A1 impeded the progression of breast cancer. More importantly, knockdown of P4HB weakened the promoting effects of overexpressed COL10A1 on cell proliferation, migration, and invasion in breast cancer.

Conclusions

COL10A1 promotes the malignant progression of breast cancer by upregulating P4HB expression, indicating that COL10A1 functions as an oncogene in breast cancer.

Screening and bioinformatical analysis of differentially expressed genes in nasopharyngeal carcinoma

Objective: To identify differentially expressed genes via bioinformatical analysis for nasopharyngeal carcinoma (NPC) and explore potential biomarkers for NPC.

Methods: We downloaded the NPC gene expression datasets (GSE40290, GSE53819) and obtained differentially expressed genes (DEGs) via GEO2R. Functional analysis of DEGs was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. In order to explore the interaction of DEGs and screen the core genes, we established protein-protein interaction (PPI) network. Then the expression level, prognostic and diagnostic analysis of the core genes in NPC were performed to reveal their potential effects on NPC. Furthermore, we obtained the transcription factors (TF) and microRNAs of core genes to construct the coregulatory network.

Results: We obtained 124 up-regulated genes and 190 down-regulated genes in total. These genes were found to be related to signal transduction, extracellular matrix organization and cell adhesion based on GO analysis. KEGG analysis revealed that the NF-kappa B (NF-κB) signaling pathway, pathways in cancer were mainly enriched signaling pathways. 25 core genes were obtained by constructing PPI network. Then the high expression of 10 core genes in NPC were verified via GEPIA, Oncomine databases and laboratory experiments. The TF-microRNA coregulatory network of the 10 core genes was built. Survival and diagnostic analysis indicated that SPP1 had negative influence on the prognosis of NPC patients based on two datasets and nine up-regulated core genes (FN1, MMP1, MMP3, PLAU, PLAUR, SERPINE1, SPP1, COL8A1, COL10A1) might be diagnostic markers for NPC.

Conclusions: Core genes of NPC were screened out by bioinformatical analysis in the present study and these genes may serve as prognostic and diagnostic biomarkers for NPC.

Expression and clinical implications of leucine-rich repeat containing 15 (LRRC15) in osteosarcoma

Leucine-rich repeat containing 15 (LRRC15) is a member of the leucine-rich repeat superfamily that is overexpressed in various cancers and associated with higher tumor grade and aggression. Despite its known tumorigenicity, its roles within osteosarcoma are unknown, prompting us to evaluate its expression and clinical significance within this rare yet aggressive cancer. Western blots showed differential expression of LRRC15 in the osteosarcoma cell lines MNNG/HOS, KHOS, 143B, MG63, Saos-2, and U2OS. We additionally validated this positive expression, as well as sublocalization to the cell membrane, with immunofluorescence. A tissue microarray constructed from 69 osteosarcoma patient tissues was immunohistochemically stained for LRRC15 expression, stratified, and used for clinicopathological analysis. Publicly available databases on LRRC15 expression, including RNA sequencing data from the Therapeutically Applicable Research to Generate Effective Treatments on Osteosarcoma (TARGET-OS) and the Gene Expression database of Normal and Tumor tissues 2 (GENT2) were also analyzed. We found 63 of the 69 (91.3%) patient tissues exhibited some degree of LRRC15 immunostaining, including no staining (6 of 69, 8.7%), 1+ staining (12 of 69, 17.4%), 2+ staining (25 of 69, 36.2%), and 3+ staining (26 of 69, 37.7%). The patients with osteosarcomas having elevated LRRC15 expression demonstrated comparatively increased metastasis, chemoresistance, and shorter 5-year survival rates. Our analysis of the TARGET-OS and GENT2 databases also showed increased LRRC15 gene expression in osteosarcoma. Taken together, our study supports LRRC15 as a prognostic biomarker and emerging therapeutic target in osteosarcoma.

Expression Profile and Prognostic Values of Mini-Chromosome Maintenance Families (MCMs) in Breast Cancer

Background

Mini-chromosome maintenance families (MCMs) were considered the key factors for DNA replication initiation. Emerging evidences indicate that MCM2-7 (MCMs) are highly expressed in tissues from various malignant tumors. However, little is known about the clinical values of MCMs in breast cancer.

Material/Methods

In our study, a comprehensive bioinformatics analysis was performed to investigate expression patterns, potential functions, and prognostic values of MCMs in breast cancer, through ONCOMINE, bc-GenExMiner v4.1, Kaplan-Meier Plotter, cBioPortal and GeneMANIA databases.

Results

We found that mRNA levels of MCMs were significantly elevated in breast cancer, especially in fast-growing and spreading tumor subtypes. These over-expressed MCMs predicted worse prognosis for breast cancer patients with shorter relapse-free survival (RFS) and overall survival. Among these six factors, high expression of MCM2/4/5/7 significantly reduced the RFS for patients with Luminal-A or B breast cancer and elevated MCM6/7 indicated shorter RFS for patients with basal-like or HER2-positive breast cancer. We also found that genomic alteration of MCMs was frequently found in breast cancer and the most common alteration was mRNA upregulation and amplification. Furthermore, MCMs were highly correlated with CDC45, CDC7, TIMELESS, ORC6, MCM10, ORC5, ORC4 and ORC3, mainly functioning to control the DNA replication initiation and genome stability.

Conclusions

These results suggest that MCMs are attractive prognostic biomarkers for breast cancer. Our study also provides useful clinical information about the potential of MCMs as therapeutic targets.