COL3A1 and Its Related Molecules as Potential Biomarkers in the Development of Human Ewing’s Sarcoma

Tang, Min; Liu, Peiqing; Wu, Xinmin; Gong, Jie; Wen, Jiacheng; Gao, Guangyu; Liu, Yulong; Gan, Lei

doi:10.1155/2021/7453500

Cited by 8 publications

(4 citation statements)

References 42 publications

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“…Additionally, the oncogene SOX4 is prominently featured in our differential analysis of tumor cells (72). These genes are also identified as diagnostic or prognostic biomarkers in previous studies (70, 73–76). Notably, some cell types also share similar gene expression patterns (Figure 6C).…”

Section: Resultsmentioning

confidence: 81%

stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multi-modal feature representation

Zhang,

Yu,

et al. 2024

Preprint

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Characterizing and understanding the structure of tissues from spatially resolved transcriptomics (SRT) is of great value for deciphering the functionality of spatial domains. However, the inherent heterogeneity and varying spatial resolutions among SRT multi-modal data present challenges in the joint analysis of these modalities. In this study, we introduce a multi-modal feature representation method, named stMMR, to effectively integrate gene expression, spatial location and histological imaging information for accurate identifying spatial domains from SRT data. stMMR uses self-attention module for deep embedding of features within unimodal and incorporates similarity contrastive learning for integrating features across modalities. stMMR demonstrates superior performances in multiple analyses using datasets generated by different platforms, including spatial domain identification, pseudo-spatiotemporal analysis as well as domain-specific gene discovery. Using stMMR, we systematically analyzed the evolving lineage structures of the chicken heart and conducted an in-depth examination of domain-specific genes in breast cancer and lung cancer. In conclusion, stMMR is capable of effectively integrating the multi-modal information for spatial domain identification and exhibits superior adaptability as well as stability in handling different types of SRT data.

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Section: Resultsmentioning

confidence: 81%

stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multi-modal feature representation

Zhang,

Yu,

et al. 2024

Preprint

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“…COL3A1 encoded the polypeptide chain which was the building block of type III collagen. The expression of this gene has an essential role in the proliferation and migration of tumor cells and is dysregulated in several malignancies, such as ovarian cancer and brain tumor [ 58 , 59 ]. Collagen, type II, alpha 1 peptide encoded with COL2A1 form the homotrimer Type II collagen [ 60 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of gene profiles related to the development of oral cancer using a deep learning technique

et al. 2023

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Background Oral cancer (OC) is a debilitating disease that can affect the quality of life of these patients adversely. Oral premalignant lesion patients have a high risk of developing OC. Therefore, identifying robust survival subgroups among them may significantly improve patient therapy and care. This study aimed to identify prognostic biomarkers that predict the time-to-development of OC and survival stratification for patients using state-of-the-art machine learning and deep learning. Methods Gene expression profiles (29,096 probes) related to 86 patients from the GSE26549 dataset from the GEO repository were used. An autoencoder deep learning neural network model was used to extract features. We also used a univariate Cox regression model to select significant features obtained from the deep learning method (P < 0.05). High-risk and low-risk groups were then identified using a hierarchical clustering technique based on 100 encoded features (the number of units of the encoding layer, i.e., bottleneck of the network) from autoencoder and selected by Cox proportional hazards model and a supervised random forest (RF) classifier was used to identify gene profiles related to subtypes of OC from the original 29,096 probes. Results Among 100 encoded features extracted by autoencoder, seventy features were significantly related to time-to-OC-development, based on the univariate Cox model, which was used as the inputs for the clustering of patients. Two survival risk groups were identified (P value of log-rank test = 0.003) and were used as the labels for supervised classification. The overall accuracy of the RF classifier was 0.916 over the test set, yielded 21 top genes (FUT8-DDR2-ATM-CD247-ETS1-ZEB2-COL5A2-GMAP7-CDH1-COL11A2-COL3A1-AHR-COL2A1-CHORDC1-PTP4A3-COL1A2-CCR2-PDGFRB-COL1A1-FERMT2-PIK3CB) associated with time to developing OC, selected among the original 29,096 probes. Conclusions Using deep learning, our study identified prominent transcriptional biomarkers in determining high-risk patients for developing oral cancer, which may be prognostic as significant targets for OC therapy. The identified genes may serve as potential targets for oral cancer chemoprevention. Additional validation of these biomarkers in experimental prospective and retrospective studies will launch them in OC clinics.

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“…Additionally, the COL1A2 gene was shown to be upregulated in ES tissues [ 40 ], and we also obtained consistent results in RD-ES cells. Zhang et al [ 41 ] found that COL1A2 was involved in the development of ES and was significantly associated with the survival time of ES patients. Our survival curves also suggest that patients with low COL1A2 expression have shorter survival times.…”

Section: Discussionmentioning

confidence: 99%

Downregulated annexin A1 expression correlates with poor prognosis, metastasis, and immunosuppressive microenvironment in Ewing’s sarcoma

Shi

Tang

et al. 2023

Aging

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Objective: Ewing's sarcoma (ES) is a common bone malignancy in children and adolescents that severely affects the prognosis of patients. The aim of this study was to identify novel biomarkers and potential therapeutic targets for ES. Methods: Highly prognosis-related hub genes were identified by independent prognostic analysis in the GSE17679 dataset. We then performed survival analysis, Cox regression analysis and clinical correlation analysis on the key gene and validated them with the GSE63157, GSE45544 and GSE73166 datasets. Differentially expressed genes (DEGs) were screened based on the high and low expression of key gene, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were performed to explore the underlying mechanisms of ES, and significant module genes were established based on protein-protein interaction (PPI) networks. Furthermore, the correlations between module genes and the immune microenvironment were analyzed and the correlations between the key gene and immune infiltration levels in sarcoma were investigated using TIMER and TISIDB. Finally, the expression levels of these key genes in ES cell lines (RD-ES and A673 cells) were further validated by real-time quantitative PCR (RT-qPCR). CCK-8 and EdU assays were performed to assess the effect of ANXA1 knockdown on RD-ES cell proliferation.Results: ANXA1 was identified as a key gene for ES prognosis. The overall survival (OS) time of patients with low ANXA1 expression was shorter, and the expression level of ANXA1 in the metastatic group was significantly lower than that in the primary group (P<0.01). Additionally, the abundance of 12 immune cells in the ANXA1 low-expression group was significantly lower than that in the high-expression group (all P<0.05), which may be related to the inhibition of the immune microenvironment. A PPI network was constructed based on 96 DEGs to further identify the five ANXA1-related module genes (COL1A2, MMP9, VIM, S100A11 and S100A4). The expression levels of ANXA1, COL1A2, MMP9, VIM, S100A11 and S100A4 were significantly different between ES cell lines and mesenchymal stem cells after validation in two ES cell lines (all P<0.01). Among these genes, ANXA1, COL1A2, MMP9, VIM and S100A4 were significantly associated with the prognosis of ES patients (all www.aging-us.com

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COL3A1 and Its Related Molecules as Potential Biomarkers in the Development of Human Ewing’s Sarcoma

Cited by 8 publications

References 42 publications

stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multi-modal feature representation

stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multi-modal feature representation

Identification of gene profiles related to the development of oral cancer using a deep learning technique

Downregulated annexin A1 expression correlates with poor prognosis, metastasis, and immunosuppressive microenvironment in Ewing’s sarcoma

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