High plasma levels of COL10A1 are associated with advanced tumor stage in gastric cancer patients

MFAP2 enhances cisplatin resistance in gastric cancer cells by regulating autophagy

Background

Cisplatin (CDDP) is of importance in cancer treatment and widely used in advanced gastric cancer (GC). However, its clinical usage is limited due to its resistance, and the regulatory mechanism of CDDP resistance in GC has not yet been fully elucidated. In this study, we first conducted a comprehensive study to investigate the role of MFAP2 through bioinformatics analysis.

Methods

The Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were applied to downloadgene expression data and clinicopathologic data, and the differentially expressed genes (DEGs) were further analyzed. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and survival analysis were conducted. Furthermore, according to the clinicopathological characteristics of TCGA, clinical correlation analysis was conducted, and a receiver operating characteristic curve (ROC) was plotted.

Results

We revealed that FAP, INHBA and MFAP2 were good diagnostic factors of GC. However, the mechanism of MFAP2 in GC remains elusive, especially in the aspect of chemotherapy resistance. We developed the CDDP-resistant cell line, and found that MFAP2 was upregulated in CDDP-resistant cells, and MFAP2-knockdown improved CDDP sensitivity. Finally, we found that MFAP2 enhanced CDDP resistance by inducing autophagy in drug-resistant cell lines.

Conclusions

The above results suggested that MFAP2 could affect the chemotherapy resistance by altering the level of autophagy in GC patients as a potential therapeutic target.

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.

The involvement of collagen family genes in tumor enlargement of gastric cancer

Extracellular matrix (ECM) not only serves as a support for tumor cell but also regulates cell-cell or cell-matrix cross-talks. Collagens are the most abundant proteins in ECM. Several studies have found that certain collagen genes were overexpressed in gastric cancer (GC) tissues and might serve as potential biomarkers and therapeutic targets in GC patients. However, the expression patterns of all collagen family genes in GC tissue and their functions are still not clear. With RNA sequencing (RNA-Seq) data, microarray data, and corresponding clinical data obtained from TCGA, GTEx, and GEO databases, bioinformatics analyses were performed to investigate the correlation between the expression patterns of collagen family genes and GC progression. We found that quite many of the collagen family genes were overexpressed in GC tissues. The increase in mRNA expression of most of these overexpressed collagen genes happened between T1 and T2 stage, which indicates the significance of collagens in tumor enlargement of GC. Notably, the mRNA expressions of these differentially expressed collagens genes were highly positively correlated. The elevated expression of a large number of collagen genes in early T stage might greatly change the composition and structure organization of ECM, contributing to ECM remodeling in GC progression.

A detailed understanding of the COL10A1 and SOX9 genes interaction based on potentially damaging mutations in gastric cancer using computational techniques

Gastric cancer (GC) has limited effective treatment options and is followed up with biomarkers that have insufficient sensitivity and specificity. Recent studies on Collagen Type X Alpha 1 Chain (COL10A1) show that the COL10A1 gene may be a diagnostic and/or prognostic biomarker for different cancer types. Moreover, its relationship with the Sex determining Region Y (SRY)-related High-Mobility Group (HMG) box (SOX9) gene which is also a transcription factor, was discovered recently, and co-expression of these two genes are associated with the development of GC. However, to the best of our knowledge, there is no study in the literature on how potential damaging mutations in the SOX9 and COL10A1 genes can affect their interactions. The aim of this study is to investigate the interactions of wild-type and potentially damaging mutated structures of COL10A1 and SOX9 genes. Thus, outputs for drug development and therapeutic strategies for GC can be obtained. For this purpose, structure validation and energy minimization analyses as well as docking and binding affinity calculations were performed. As a result, it was found that all investigated mutations (P563S, I588L, T624A, H165R and N110T) increased the binding affinity between the COL10A1-SOX9 complex, especially the N110T and H165R mutants in SOX9. As a conclusion, the N110T and H165R mutants in SOX9 may contribute to tumor progression. Therefore, it is important to consider these mutations for future therapeutic strategies.Communicated by Ramaswamy H. Sarma.

Identification of key biomarkers for STAD using filter feature selection approaches

Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer death worldwide. Discovery of diagnostic biomarkers prompts the early detection of GC. In this study, we used limma method combined with joint mutual information (JMI), a machine learning algorithm, to identify a signature of 11 genes that performed well in distinguishing tumor and normal samples in a stomach adenocarcinoma cohort. Other two GC datasets were used to validate the classifying performances. Several of the candidate genes were correlated with GC tumor progression and survival. Overall, we highlight the application of feature selection approaches in the analysis of high-dimensional biological data, which will improve study accuracies and reduce workloads for the researchers when identifying potential tumor biomarkers.

High expression COL10A1 promotes breast cancer progression and predicts poor prognosis

Background

As a common malignant disease in females, breast cancer (BCa) causes increasing numbers of cancer-related death. Collagen X alpha 1 chain (COL10A1) plays a critical role in the oncogenesis and progression of malignant tumors. However, a systematic analysis of COL10A1 in BCa has not been conducted.

Methods

The COL10A1 expression level and prognostic value in BCa were defined through the Cancer Genome Atlas (TCGA) as well as the Kaplan-Meier plotter data respectively. The expression pattern of COL10A1 was subsequently confirmed on tissue microarray (TMA) by immunochemistry (IHC) staining. Moreover, cellular functional assays which aimed to evaluate cell proliferation, migration, invasion, and apoptosis, were conducted to investigate the oncogenic activity of COL10A1 in BCa. Then, Tumor Immune Estimation Resource (TIMER) was adopted to determine the association between COL10A1 expression and immune cell infiltration.

Results

Bioinformatics analysis revealed that COL10A1 was significantly overexpressed and had notable prognostic value, especially for distant metastasis-free survival (DMFS) in BCa. Moreover, IHC analysis of 140 BCa tissues on TMA chips exhibited the overexpression of COL10A1 was correlated to advanced clinical stage, poor overall survival (OS), and worse recurrence-free survival (RFS). Besides, knockdown of COL10A1 remarkably suppressed cell proliferation, migration, and invasion in BCa cells, and notably promoted cell apoptosis as well. Furthermore, COL10A1 was positively associated with immune cell infiltration including B cell, CD8⁺ T cell, CD4⁺ T cell, macrophage, neutrophil, and dendritic cell.

Conclusion

The results revealed that COL10A1 is a novel oncogene and could serve as a potential prognostic biomarker in BCa. Besides, the downregulation of COL10A1 could inhibit BCa progression, which could be a potential target for BCa therapy.

Collagen Family as Promising Biomarkers and Therapeutic Targets in Cancer

Despite advances in cancer detection and therapy, it has been estimated that the incidence of cancers will increase, while the mortality rate will continue to remain high, a fact explained by the large number of patients diagnosed in advanced stages when therapy is often useless. Therefore, it is necessary to invest knowledge and resources in the development of new non-invasive biomarkers for the early detection of cancer and new therapeutic targets for better health management. In this review, we provided an overview on the collagen family as promising biomarkers and on how they may be exploited as therapeutic targets in cancer. The collagen family tridimensional structure, organization, and functions are very complex, being in a tight relationship with the extracellular matrix, tumor, and immune microenvironment. Moreover, accumulating evidence underlines the role of collagens in promoting tumor growth and creating a permissive tumor microenvironment for metastatic dissemination. Knowledge of the molecular basis of these interactions may help in cancer diagnosis and prognosis, in overcoming chemoresistance, and in providing new targets for cancer therapies.

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.

Identification and Verification of Potential Biomarkers in Gastric Cancer By Integrated Bioinformatic Analysis

Background: Gastric cancer (GC) is a common cancer with high mortality. This study aimed to identify its differentially expressed genes (DEGs) using bioinformatics methods.

Methods: DEGs were screened from four GEO (Gene Expression Omnibus) gene expression profiles. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. A protein–protein interaction (PPI) network was constructed. Expression and prognosis were assessed. Meta-analysis was conducted to further validate prognosis. The receiver operating characteristic curve (ROC) was analyzed to identify diagnostic markers, and a nomogram was developed. Exploration of drugs and immune cell infiltration analysis were conducted.

Results: Nine up-regulated and three down-regulated hub genes were identified, with close relations to gastric functions, extracellular activities, and structures. Overexpressed Collagen Type VIII Alpha 1 Chain (COL8A1), Collagen Type X Alpha 1 Chain (COL10A1), Collagen Triple Helix Repeat Containing 1 (CTHRC1), and Fibroblast Activation Protein (FAP) correlated with poor prognosis. The area under the curve (AUC) of ADAM Metallopeptidase With Thrombospondin Type 1 Motif 2 (ADAMTS2), COL10A1, Collagen Type XI Alpha 1 Chain (COL11A1), and CTHRC1 was >0.9. A nomogram model based on CTHRC1 was developed. Infiltration of macrophages, neutrophils, and dendritic cells positively correlated with COL8A1, COL10A1, CTHRC1, and FAP. Meta-analysis confirmed poor prognosis of overexpressed CTHRC1.

Conclusion: ADAMTS2, COL10A1, COL11A1, and CTHRC1 have diagnostic values in GC. COL8A1, COL10A1, CTHRC1, and FAP correlated with worse prognosis, showing prognostic and therapeutic values. The immune cell infiltration needs further investigations.

Collagen type X alpha 1 promotes proliferation, invasion and epithelial-mesenchymal transition of cervical cancer through activation of TGF-β/Smad signaling

Background

Collagen type X alpha 1 (COL10A1) belongs to the collagen family and constitutes the main component of the interstitial matrix. COL10A1 was found to be dysregulated in various cancers, and to participate in tumorigenesis. However, the role of COL10A1 in cervical cancer (CC) remains unclear.

Methods

Expression of COL10A1 in CC cells and tissues was detected by western blot and qRT-PCR. CC cells were transfected with pcDNA-COL10A1 or si-COL10A1, and the effect of COL10A1 on cell proliferation of CC was assessed by MTT and colony formation assays. Cell metastasis was detected by wound healing and transwell assays. Western blot was applied to evaluate epithelial-mesenchymal transition.

Results

COL10A1 was significantly elevated in CC tissues and cells (P < 0.001). Over-expression of COL10A1 increased cell viability of CC (P < 0.001), and enhanced the number of colonies (P < 0.001). However, knockdown of COL10A1 reduced the cell proliferation of CC (P < 0.001). Over-expression of COL10A1 also promoted cell migration (P < 0.001) and invasion (P < 0.001) of CC, whereas silencing of COL10A1 suppressed cell metastasis (P < 0.001). Protein level of E-cadherin in CC was reduced (P < 0.05), whereas N-cadherin and vimentin were enhanced by COL10A1 over-expression (P < 0.001). Silencing of COL10A1 reduced the protein level of TGF-β1 (P < 0.01), and down-regulated the phosphorylation of Smad2 and Smad3 in CC (P < 0.001).

Conclusion

Down-regulation of COL10A1 suppressed cell proliferation, metastasis, and epithelial-mesenchymal transition of CC through inactivation of TGF-β/Smad signaling.

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.

Proteomic Identification of a Gastric Tumor ECM Signature Associated With Cancer Progression

The extracellular matrix (ECM) plays an undisputable role in tissue homeostasis and its deregulation leads to altered mechanical and biochemical cues that impact cancer development and progression. Herein, we undertook a novel approach to address the role of gastric ECM in tumorigenesis, which remained largely unexplored. By combining decellularization techniques with a high-throughput quantitative proteomics approach, we have performed an extensive characterization of human gastric mucosa, uncovering its composition and distribution among tumor, normal adjacent and normal distant mucosa. Our results revealed a common ECM signature composed of 142 proteins and indicated that gastric carcinogenesis encompasses ECM remodeling through alterations in the abundance of 24 components, mainly basement membrane proteins. Indeed, we could only identify one de novo tumor-specific protein, the collagen alpha-1(X) chain (COL10A1). Functional analysis of the data demonstrated that gastric ECM remodeling favors tumor progression by activating ECM receptors and cellular processes involved in angiogenesis and cell-extrinsic metabolic regulation. By analyzing mRNA expression in an independent GC cohort available at the TGCA, we validated the expression profile of 12 differentially expressed ECM proteins. Importantly, the expression of COL1A2, LOX and LTBP2 significantly correlated with high tumor stage, with LOX and LTBP2 further impacting patient overall survival. These findings contribute for a better understanding of GC biology and highlight the role of core ECM components in gastric carcinogenesis and their clinical relevance as biomarkers of disease prognosis.

AGT May Serve as a Prognostic Biomarker and Correlated with Immune Infiltration in Gastric Cancer

Purpose

Angiotensinogen (AGT), as a component of the renin–angiotensin system (RAS), is associated with multiple risk factors for gastric cancer (GC). However, the relationship between AGT and tumor-infiltrating lymphocytes in GC remains elusive.

Methods

AGT expression was analyzed based on the Cancer Genome Atlas (TCGA) dataset. Kaplan–Meier curve was employed to assess the role of AGT expression in gastric patients’ prognosis. The association between AGT expression and tumor immune infiltration was further evaluated via exploring Tumour Immune Estimation Resource (TIMER) and The Gene Expression Profiling Interactive Analysis (GEPIA). We also used multiple public databases to analyse the aberrant methylation of AGT, construct protein–protein interaction (PPI) and gene ontology (GO) analyses.

Results

AGT was overexpressed in GC tissues compared with normal gastric tissues (P<0.05). High AGT expression related with poorer overall survival of patients with GC, especially in advanced GC patients. Immune infiltration analysis revealed that AGT was associated with several immune cells (including B cells, CD4+ T cells, macrophages), and AGT expression was also associated with the markers of NK cells, TAMs, Tregs, and so on (all P<0.05). Methylation analysis indicated that hypomethylation may lead to abnormal upregulation of the AGT. GO analysis showed that AGT and its related genes were enriched in systemic arterial blood pressure by hormone, regulation of blood volume by renin-angiotensin, NIK/NF-kappaB signaling, ficolin-1-rich granule and so on.

Conclusion

AGT could act as a promising biomarker for prognosis and immune infiltration in GC.

COL10A1 is a novel factor in the development of choroidal neovascularization

With the dramatic rise in the aging population, researching age-related macular degeneration (AMD), especially the severe form neovascular AMD (nAMD), has become more important than ever. In this study, we found that collagen type X was increased in retina-choroid tissue of mice with laser-induced choroidal neovascularization (CNV) based on immunohistofluorescence. RNA sequencing and bioinformatic analyses were performed to compare the retina-choroid tissue complex of the CNV mouse model to normal controls. Collagen type X alpha 1 chain (Col10a1) was among the most significantly upregulated genes, and the results were validated with an animal model at the mRNA and protein levels by quantitative real-time polymerase chain reaction (qPCR) and western blotting, respectively. COL10A1 was also upregulated in human retinal microvascular endothelial cells (HRMECs), human umbilical vein endothelial cells (HUVECs), RPE19 cells and RF/6A cells under hypoxic conditions. Next, in vitro and in vivo experiments were performed to study the effect of COL10A1 on neovascularization. siRNA knockdown of COL10A1 suppressed the proliferation and tube formation ability of HRMECs under hypoxic conditions. Snail family transcriptional repressor 1 (SNAIL1) and angiopoietin-2 (ANGPT2) were downregulated in COL10A1 knockdown HRMECs under hypoxic conditions and thus were potential downstream genes. Significant decreases in CNV leakage and CNV lesion area, as assessed by fundus fluorescein angiography (FFA) and immunofluorescence of choroidal flat mounts, respectively, were observed in a mouse model intravitreally injected with anti-collagen X monoclonal antibody (mAb) compared to the controls. In conclusion, COL10A1 promotes CNV formation and may represent a new candidate target for the treatment and diagnosis of nAMD and other neovascular diseases.

Development and Validation of a Novel Prognosis Prediction Model for Patients With Stomach Adenocarcinoma

Background: Stomach adenocarcinoma (STAD) is a significant global health problem. It is urgent to identify reliable predictors and establish a potential prognostic model.

Methods: RNA-sequencing expression data of patients with STAD were downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) database. Gene expression profiling and survival analysis were performed to investigate differentially expressed genes (DEGs) with significant clinical prognosis value. Overall survival (OS) analysis and univariable and multivariable Cox regression analyses were performed to establish the prognostic model. Protein–protein interaction (PPI) network, functional enrichment analysis, and differential expression investigation were also performed to further explore the potential mechanism of the prognostic genes in STAD. Finally, nomogram establishment was undertaken by performing multivariate Cox regression analysis, and calibration plots were generated to validate the nomogram.

Results: A total of 229 overlapping DEGs were identified. Following Kaplan–Meier survival analysis and univariate and multivariate Cox regression analysis, 11 genes significantly associated with prognosis were screened and five of these genes, including COL10A1, MFAP2, CTHRC1, P4HA3, and FAP, were used to establish the risk model. The results showed that patients with high-risk scores have a poor prognosis, compared with those with low-risk scores (p = 0.0025 for the training dataset and p = 0.045 for the validation dataset). Subsequently, a nomogram (including TNM stage, age, gender, histologic grade, and risk score) was created. In addition, differential expression and immunohistochemistry stain of the five core genes in STAD and normal tissues were verified.

Conclusion: We develop a prognostic-related model based on five core genes, which may serve as an independent risk factor for survival prediction in patients with STAD.

mRBioM: An Algorithm for the Identification of Potential mRNA Biomarkers From Complete Transcriptomic Profiles of Gastric Adenocarcinoma

Purpose

In this work, an algorithm named mRBioM was developed for the identification of potential mRNA biomarkers (PmBs) from complete transcriptomic RNA profiles of gastric adenocarcinoma (GA).

Methods

mRBioM initially extracts differentially expressed (DE) RNAs (mRNAs, miRNAs, and lncRNAs). Next, mRBioM calculates the total information amount of each DE mRNA based on the coexpression network, including three types of RNAs and the protein-protein interaction network encoded by DE mRNAs. Finally, PmBs were identified according to the variation trend of total information amount of all DE mRNAs. Four PmB-based classifiers without learning and with learning were designed to discriminate the sample types to confirm the reliability of PmBs identified by mRBioM. PmB-based survival analysis was performed. Finally, three other cancer datasets were used to confirm the generalization ability of mRBioM.

Results

mRBioM identified 55 PmBs (41 upregulated and 14 downregulated) related to GA. The list included thirteen PmBs that have been verified as biomarkers or potential therapeutic targets of gastric cancer, and some PmBs were newly identified. Most PmBs were primarily enriched in the pathways closely related to the occurrence and development of gastric cancer. Cancer-related factors without learning achieved sensitivity, specificity, and accuracy of 0.90, 1, and 0.90, respectively, in the classification of the GA and control samples. Average accuracy, sensitivity, and specificity of the three classifiers with machine learning ranged within 0.94–0.98, 0.94–0.97, and 0.97–1, respectively. The prognostic risk score model constructed by 4 PmBs was able to correctly and significantly (^∗∗∗p < 0.001) classify 269 GA patients into the high-risk (n = 134) and low-risk (n = 135) groups. GA equivalent classification performance was achieved using the complete transcriptomic RNA profiles of colon adenocarcinoma, lung adenocarcinoma, and hepatocellular carcinoma using PmBs identified by mRBioM.

Conclusions

GA-related PmBs have high specificity and sensitivity and strong prognostic risk prediction. MRBioM has also good generalization. These PmBs may have good application prospects for early diagnosis of GA and may help to elucidate the mechanism governing the occurrence and development of GA. Additionally, mRBioM is expected to be applied for the identification of other cancer-related biomarkers.

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.

Analysis of Collagen type X alpha 1 (COL10A1) expression and prognostic significance in gastric cancer based on bioinformatics

Collagen type X alpha 1 (COL10A1) is a member of the collagen family and the main matrix component. However, COL10A1 expression and prognosis relationship remains unclear in gastric cancer (GC). Through the analysis of database of Oncomine, the Cancer Genome Atlas (TCGA) as well as the Gene Expression Omnibus (GEO), in contrast to the tissue of normal gastric, COL10A1 in gastric cancer, had been upregulated. The high expression of COL10A1 was obviously related to T stage (P = 0.025) and lymph node metastasis (P = 0.025). It has been illustrated by the analysis of logistic regression that COL10A1's heightened expression in gastric cancer had been essentially linked with pathological stage, tumor differentiation, and T classification. The Kaplan-Meier curve in the Kaplan-Meier plotter database (P = 0.0371) and GSE84437 (P = 0.002) indicate that patients with high COL10A1 expression possess poor prognosis, specifically GC patients with lymph node metastasis have it. TCGA's Multivariate analysis (P = 0.025) and GSE84437 dataset (P = 0.034) show that high expression COL10A1 is a key independent predictor of poor overall survival. Searching KEGG pathway enrichment by GSEA, the results suggested that 29 pathways were enriched. qRT-PCR technique was used for verification of the COL10A1's high expression in gastric cancer in contrast to the normal gastric tissues. In conclusion, COL10A1 is of great importance in predicting the survival rate of GC patients.