Analysis of SEC24D gene in breast cancer based on UALCAN database

Li, Zhi; Zhou, Jing; Wang, Zhibao; Zhou, Zhiqiang

doi:10.1515/biol-2019-0080

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…The UALCAN database (https://ualcan.path.uab.edu/) was utilized to analyze the methylation status of the risk model genes in colon and rectal cancer samples 24 …”

Section: Methodsmentioning

confidence: 99%

Constructing a personalized prognostic risk model for colorectal cancer using machine learning and multi‐omics approach based on epithelial–mesenchymal transition‐related genes

Zhang,

Fan,

2024

The Journal of Gene Medicine

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The progression and the metastatic potential of colorectal cancer (CRC) are intricately linked to the epithelial–mesenchymal transition (EMT) process. The present study harnesses the power of machine learning combined with multi‐omics data to develop a risk stratification model anchored on EMT‐associated genes. The aim is to facilitate personalized prognostic assessments in CRC. We utilized publicly accessible gene expression datasets to pinpoint EMT‐associated genes, employing a CoxBoost algorithm to sift through these genes for prognostic significance. The resultant model, predicated on gene expression levels, underwent rigorous independent validation across various datasets. Our model demonstrated a robust capacity to segregate CRC patients into distinct high‐ and low‐risk categories, each correlating with markedly different survival probabilities. Notably, the risk score emerged as an independent prognostic indicator for CRC. High‐risk patients were characterized by an immunosuppressive tumor milieu and a heightened responsiveness to certain chemotherapeutic agents, underlining the model's potential in steering tailored oncological therapies. Moreover, our research unearthed a putative repressive interaction between the long non‐coding RNA PVT1 and the EMT‐associated genes TIMP1 and MMP1, offering new insights into the molecular intricacies of CRC. In essence, our research introduces a sophisticated risk model, leveraging machine learning and multi‐omics insights, which accurately prognosticates outcomes for CRC patients, paving the way for more individualized and effective oncological treatment paradigms.

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“…The UALCAN database (https://ualcan.path.uab.edu/) was utilized to analyze the methylation status of the risk model genes in colon and rectal cancer samples 24 …”

Section: Methodsmentioning

confidence: 99%

Constructing a personalized prognostic risk model for colorectal cancer using machine learning and multi‐omics approach based on epithelial–mesenchymal transition‐related genes

Zhang,

Fan,

2024

The Journal of Gene Medicine

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“…The log2 [transcripts per million (TPM) + 1] converted expression data were applied to box-line plots or violin plots. Moreover, LHPP protein expression differences between normal tissues and tumor tissues were described via using the UALCAN portal ( http://ualcan.path.uab.edu/analysisprot.html ) [ 20 , 21 ] and Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset [ 22 ]. Then immunohistochemistry (IHC) staining images of LHPP expression were downloaded and analyzed from the Human Protein Atlas (HPA) database ( http://www.proteinatlas.org/ ) [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

“…edu/ analy sispr ot. html) [20,21] and Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset [22]. Then immunohistochemistry (IHC) staining images of LHPP expression were downloaded and analyzed from the Human Protein Atlas (HPA) database (http:// www.…”

Section: Gene and Protein Expression Analysismentioning

confidence: 99%

Does the LHPP gene share a common biological function in pancancer progression?

et al. 2022

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Although emerging evidence has revealed that LHPP, a histidine phosphatase protein, suppresses the progression of different cancers, a pan-cancer analysis still remains unavailable. Therefore, we first utilized different bioinformatics tools to explore the tumor inhibitory role of LHPP protein across 33 tumor types based on the TCGA project. Additionally, HGC-27 gastric cancer cells were used to evaluate the biological functions of LHPP after stable transfection with lentiviruses. Consequently, LHPP mRNA and protein expression were down-regulated in the most cancer tissues corresponding to normal tissues. The data showed that patients with higher LHPP performance had a better prognosis of overall survival (OS) and disease-free survival (DFS) in brain glioma and renal carcinoma. In addition, we found that enhancement of LHPP expression attenuated the proliferation, migration and invasion of gastric cancer cells. The expression levels of cell-cycle-related and EMT-related molecules, such as CDK4, CyclinD1, Vimentin and Snail, were clearly reduced. Moreover, a genetic alteration analysis showed that the most frequent mutation types in LHPP protein was amplification. The patients without LHPP mutation showed a better tendency of prognosis in UCEC, STAD and COAD. Cancer-associated fibroblast infiltration was also observed in head and neck squamous cell carcinoma, stomach adenocarcinoma and testicular germ cell tumors. In summary, our pancancer analysis among various tumor types could provide a comprehensive understanding of LHPP biological function in the progression of malignant diseases and promote the development of novel therapeutic targets.

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“…The UALCAN online tool was used to investigate the correlation between EXOSC1 and the HNSC clinical information. 31 The "TCGA analysis" module in UALCAN was used to evaluate the gene expression levels of EXOSC1 in HNSC cancer tissues and normal tissues. In addition, the tumor microenvironment is a complex environment with immune cells around.…”

Section: Identification Of Clinical Relevance and Immune Infiltrationmentioning

confidence: 99%

Comparative Proteomics Identified EXOSC1 as a Target Protein of Anticancer Peptide LVTX-8 in Nasopharyngeal Carcinoma Cells

Zhu,

Wang,

Wang

et al. 2024

J. Proteome Res.

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Nasopharyngeal carcinoma (NPC) is a prevalent malignancy that usually occurs among the nose and throat. Due to mild initial symptoms, most patients are diagnosed in the late stage, and the recurrence rate of tumors is high, resulting in many deaths every year. Traditional radiotherapy and chemotherapy are prone to causing drug resistance and significant side effects. Therefore, searching for new bioactive drugs including anticancer peptides is necessary and urgent. LVTX-8 is a peptide toxin synthesized from the cDNA library of the spider Lycosa vittata, which is consisting of 25 amino acids. In this study, a series of in vitro cell experiments such as cell toxicity, colony formation, and cell migration assays were performed to exam the anticancer activity of LVTX-8 in NPC cells (5-8F and CNE-2). The results suggested that LVTX-8 significantly inhibited cell proliferation and migration of NPC cells. To find the potential molecular targets for the anticancer capability of LVTX-8, high-throughput proteomic and bioinformatics analysis were conducted on NPC cells. The results identified EXOSC1 as a potential target protein with significantly differential expression levels under LVTX-8+/LVTX-8− conditions. The results in this research indicate that spider peptide toxin LVTX-8 exhibits significant anticancer activity in NPC, and EXOSC1 may serve as a target protein for its anticancer activity. These findings provide a reference for the development of new therapeutic drugs for NPC and offer new ideas for the discovery of biomarkers related to NPC diagnosis. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository with the data set identifier PXD050542.

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Analysis of SEC24D gene in breast cancer based on UALCAN database

Cited by 4 publications

References 18 publications

Constructing a personalized prognostic risk model for colorectal cancer using machine learning and multi‐omics approach based on epithelial–mesenchymal transition‐related genes

Constructing a personalized prognostic risk model for colorectal cancer using machine learning and multi‐omics approach based on epithelial–mesenchymal transition‐related genes

Does the LHPP gene share a common biological function in pancancer progression?

Comparative Proteomics Identified EXOSC1 as a Target Protein of Anticancer Peptide LVTX-8 in Nasopharyngeal Carcinoma Cells

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