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.
{"title":"Constructing a personalized prognostic risk model for colorectal cancer using machine learning and multi-omics approach based on epithelial–mesenchymal transition-related genes","authors":"Shuze Zhang, Wanli Fan, Dong He","doi":"10.1002/jgm.3660","DOIUrl":"https://doi.org/10.1002/jgm.3660","url":null,"abstract":"<p>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.</p>","PeriodicalId":56122,"journal":{"name":"Journal of Gene Medicine","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139480506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ziwei Su, Yang Wang, Jialing Cao, Jie Ma, Guangzhao Wang, Huijuan Ren, Yihan Zhang, Kangliang Sheng, Xueying Zhu, Yongzhong Wang
Background
The primary reason for tumor-related deaths worldwide is lung adenocarcinoma (LUAD). The oncogene IQ motif-containing GTPase activating protein 3 (IQGAP3) is crucial for contributing to tumor initiation and progression. However, the precise function and molecular mechanism of IQGAP3 in LUAD remain unknown. The present study aimed to investigate the expression, prognosis, mechanism and tumor immunity associated with IQGAP3 in LUAD.
Methods
The relationship between IQGAP3 and the poor prognosis of LUAD was analyzed using The Cancer Genome Atlas (TCGA) database. This analysis was further validated on lung cancer tissues and cell lines. The function of IQGAP3 was investigated by silencing it in LUAD cell lines. To predict microRNA (miRNA) and long non-coding RNA associated with IQGAP3, the starBase database was utilized, and the predictions were verified by enhancing the function of miRNA. Finally, the relationship between IQGAP3 and tumor immunity was evaluated using Spearman's correlation analysis.
Results
TCGA database revealed that higher levels of IQGAP3 were associated with advanced tumor stage, N stage and poor prognosis in LUAD patients. To confirm that, we conducted experiments on lung cancer tissues and cell lines and found that silencing IQGAP3 significantly inhibited tumor cell proliferation and migration. The expression of IQGAP3 showed a negative correlation with has-miR-101-3p and has-miR-135a-5p, whereas it showed a positive correlation with GSEC, AC005034.3 and TYMSOS. Furthermore, the introduction of miRNA-mimics into lung cancer cell resulted in a significant inhibition of cancer cell growth and migration. Following that, the level of IQGAP3 showed a positive correlation with the infiltration of immune cells in tumors.
Conclusions
These results reveal that IQGAP3 significantly promotes LUAD progression and could serve as a prognostic biomarker for LUAD. Furthermore, IQGAP3 is most likely regulated by the GSEC/TYMSOS-hsa-miR-101-3p axis and the AC005034.3-hsa-miR-135a-5p axis in LUAD.
{"title":"Identification and validation of non-coding RNA-mediated high expression of IQGAP3 in poor prognosis of lung adenocarcinoma","authors":"Ziwei Su, Yang Wang, Jialing Cao, Jie Ma, Guangzhao Wang, Huijuan Ren, Yihan Zhang, Kangliang Sheng, Xueying Zhu, Yongzhong Wang","doi":"10.1002/jgm.3664","DOIUrl":"https://doi.org/10.1002/jgm.3664","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The primary reason for tumor-related deaths worldwide is lung adenocarcinoma (LUAD). The oncogene IQ motif-containing GTPase activating protein 3 (IQGAP3) is crucial for contributing to tumor initiation and progression. However, the precise function and molecular mechanism of IQGAP3 in LUAD remain unknown. The present study aimed to investigate the expression, prognosis, mechanism and tumor immunity associated with IQGAP3 in LUAD.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The relationship between IQGAP3 and the poor prognosis of LUAD was analyzed using The Cancer Genome Atlas (TCGA) database. This analysis was further validated on lung cancer tissues and cell lines. The function of IQGAP3 was investigated by silencing it in LUAD cell lines. To predict microRNA (miRNA) and long non-coding RNA associated with IQGAP3, the starBase database was utilized, and the predictions were verified by enhancing the function of miRNA. Finally, the relationship between IQGAP3 and tumor immunity was evaluated using Spearman's correlation analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>TCGA database revealed that higher levels of IQGAP3 were associated with advanced tumor stage, N stage and poor prognosis in LUAD patients. To confirm that, we conducted experiments on lung cancer tissues and cell lines and found that silencing IQGAP3 significantly inhibited tumor cell proliferation and migration. The expression of IQGAP3 showed a negative correlation with has-miR-101-3p and has-miR-135a-5p, whereas it showed a positive correlation with GSEC, AC005034.3 and TYMSOS. Furthermore, the introduction of miRNA-mimics into lung cancer cell resulted in a significant inhibition of cancer cell growth and migration. Following that, the level of IQGAP3 showed a positive correlation with the infiltration of immune cells in tumors.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>These results reveal that IQGAP3 significantly promotes LUAD progression and could serve as a prognostic biomarker for LUAD. Furthermore, IQGAP3 is most likely regulated by the GSEC/TYMSOS-hsa-miR-101-3p axis and the AC005034.3-hsa-miR-135a-5p axis in LUAD.</p>\u0000 </section>\u0000 </div>","PeriodicalId":56122,"journal":{"name":"Journal of Gene Medicine","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139480454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ribosome biogenesis protein BRX1 homolog (BRIX1) is critically required for the synthesis of the 60S ribosome subunit. However, the role and mechanism of BRIX1 in colorectal cancer (CRC) remain unclear.
Methods
Kyoto Encyclopedia of Gene and Genome pathway and Gene Ontology analyses were used for bioinformatics analysis. The rRNA levels were detected in CRC tissues and cells. Nascent RNA synthesis was detected via cellular immunofluorescence. The correlation was analyzed between patient Positron Emission Tomography-Computed Tomography (PET–CT) values and their BRIX1 expression. The extracellular acidification rate (ECAR) and oxygen consumption rate were determined via live metabolic analyses. Polysome fractions were collected for BRIX1 mRNA used in translation. The orthotopic model and Cell Counting Kit-8 (CCK8) assay were used to assess BRIX1 function in CRC.
Results
BRIX1 is a core protein involved in ribosome-related pathway changes in CRC. Gene Ontology analysis showed that BRIX1 was primarily enriched in ribosome assembly and ribosome biogenesis pathways. In fresh CRC tissue, rRNA levels (5S, 5.8S, 18S and 28S) were higher in the BRIX1 high-expression group than in the BRIX1 low-expression group. Similarly, BRIX1 knockdown significantly decreased rRNA levels for 5S, 5.8S, 18S and 28S in CRC cells, whereas overexpression of BRIX1 significantly increased these levels. In addition, BRIX1 knockdown inhibited nascent RNA synthesis in CRC cells. In clinical data analysis, BRIX1 expression was related to the glucose uptake in PET–CT. BRIX1 knockdown significantly decreased the ECAR value, glucose uptake and lactic acid production in CRC cells, whereas BRIX1 overexpression significantly increased these. Furthermore, BRIX1 knockdown significantly decreased the protein expression of GLUT1, whereas BRIX1 overexpression significantly increased this; however, expression of BRIX1 mRNA was unaffected in either case. Blocking glycolysis by si-GLUT1 or galactose reversed BRIX1 promotion of glycolysis and cell proliferation in CRC cells.
{"title":"BRIX1 promotes ribosome synthesis and enhances glycolysis by selected translation of GLUT1 in colorectal cancer","authors":"Chunhui Jiang, Longci Sun, Siyuan Wen, Yuan Tian, Chunjie Xu, Qing Xu, Hanbing Xue","doi":"10.1002/jgm.3632","DOIUrl":"https://doi.org/10.1002/jgm.3632","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Ribosome biogenesis protein BRX1 homolog (BRIX1) is critically required for the synthesis of the 60S ribosome subunit. However, the role and mechanism of BRIX1 in colorectal cancer (CRC) remain unclear.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Kyoto Encyclopedia of Gene and Genome pathway and Gene Ontology analyses were used for bioinformatics analysis. The rRNA levels were detected in CRC tissues and cells. Nascent RNA synthesis was detected via cellular immunofluorescence. The correlation was analyzed between patient Positron Emission Tomography-Computed Tomography (PET–CT) values and their BRIX1 expression. The extracellular acidification rate (ECAR) and oxygen consumption rate were determined via live metabolic analyses. Polysome fractions were collected for <i>BRIX1</i> mRNA used in translation. The orthotopic model and Cell Counting Kit-8 (CCK8) assay were used to assess BRIX1 function in CRC.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>BRIX1 is a core protein involved in ribosome-related pathway changes in CRC. Gene Ontology analysis showed that BRIX1 was primarily enriched in ribosome assembly and ribosome biogenesis pathways. In fresh CRC tissue, rRNA levels (5S, 5.8S, 18S and 28S) were higher in the BRIX1 high-expression group than in the BRIX1 low-expression group. Similarly, BRIX1 knockdown significantly decreased rRNA levels for 5S, 5.8S, 18S and 28S in CRC cells, whereas overexpression of BRIX1 significantly increased these levels. In addition, BRIX1 knockdown inhibited nascent RNA synthesis in CRC cells. In clinical data analysis, BRIX1 expression was related to the glucose uptake in PET–CT. BRIX1 knockdown significantly decreased the ECAR value, glucose uptake and lactic acid production in CRC cells, whereas BRIX1 overexpression significantly increased these. Furthermore, BRIX1 knockdown significantly decreased the protein expression of GLUT1, whereas BRIX1 overexpression significantly increased this; however, expression of <i>BRIX1</i> mRNA was unaffected in either case. Blocking glycolysis by si-GLUT1 or galactose reversed BRIX1 promotion of glycolysis and cell proliferation in CRC cells.</p>\u0000 </section>\u0000 </div>","PeriodicalId":56122,"journal":{"name":"Journal of Gene Medicine","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139473902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Upregulation of SMC1A (Structural maintenance of chromosomes 1A) is linked with many types of cancer and its oncogenic function, which has been associated with crucial cellular mechanisms (cell division, cell cycle checkpoints regulation and DNA repair). Recent studies have shown that SMC1A was involved in breast cancer, although the exact mechanisms of SMC1A remain to be determined.
Methods
Using The Cancer Genome Atlas (TCGA) database, we examined SMC1A expression and its relation to other genes, including FOXM1 and STMN1. Short hairpin RNA was used to subsequently examine the biological roles of SMC1A in MDA-MB-231 and MDA-MB-468 cell lines. Bioinformatics were performed to identify the SMC1A-related gene FOXM1.
Results
Here, we used the TCGA database to show that SMC1A is overexpressed in breast cancer. Later investigations showed SMC1A's role in breast cancer cell survival, apoptosis and invasion. Using bioinformatics and western blot assays, we confirmed that FOXM1 acted as the downstream of SMC1A, and SMC1A knockdown significantly downregulated the FOXM1 expression via the AKT signal pathway. Interestingly, the inhibition effects induced by SMC1A downregulation could be reversed by FOXM1 overexpression. In the clinic, SMC1A expression is favorably linked with FOXM1 expression in breast cancer tumor tissues.
Conclusions
Collectively, our results not only enhance our knowledge of SMC1A's molecular pathways in breast cancer, but also suggest a potential new therapeutic target.
{"title":"AKT/FOXM1/STMN1 signaling pathway activation by SMC1A promotes tumor growth in breast cancer","authors":"Kaichun Li, Ping Dai, Jian Li, Long Liu, Shiyu Cheng, Qingliang Fang, Bingxiang Wu","doi":"10.1002/jgm.3661","DOIUrl":"https://doi.org/10.1002/jgm.3661","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Upregulation of SMC1A (Structural maintenance of chromosomes 1A) is linked with many types of cancer and its oncogenic function, which has been associated with crucial cellular mechanisms (cell division, cell cycle checkpoints regulation and DNA repair). Recent studies have shown that SMC1A was involved in breast cancer, although the exact mechanisms of SMC1A remain to be determined.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Using The Cancer Genome Atlas (TCGA) database, we examined SMC1A expression and its relation to other genes, including FOXM1 and STMN1. Short hairpin RNA was used to subsequently examine the biological roles of SMC1A in MDA-MB-231 and MDA-MB-468 cell lines. Bioinformatics were performed to identify the SMC1A-related gene FOXM1.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Here, we used the TCGA database to show that SMC1A is overexpressed in breast cancer. Later investigations showed SMC1A's role in breast cancer cell survival, apoptosis and invasion. Using bioinformatics and western blot assays, we confirmed that FOXM1 acted as the downstream of SMC1A, and SMC1A knockdown significantly downregulated the FOXM1 expression via the AKT signal pathway. Interestingly, the inhibition effects induced by SMC1A downregulation could be reversed by FOXM1 overexpression. In the clinic, SMC1A expression is favorably linked with FOXM1 expression in breast cancer tumor tissues.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Collectively, our results not only enhance our knowledge of SMC1A's molecular pathways in breast cancer, but also suggest a potential new therapeutic target.</p>\u0000 </section>\u0000 </div>","PeriodicalId":56122,"journal":{"name":"Journal of Gene Medicine","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139473987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shujie Liu, Kun Wang, Zhendan Zhao, Yu Pang, Fang Liu, Pengling Wang, Zhiling Wang, Xingsheng Yang
Background
Ovarian cancer is one of the most common cancers in women. Profiles changes of microRNAs (miRNAs) are closely linked to malignant tumors. In the present study, we investigated expression of miR-451a in high-grade serous ovarian cancer (HGSOC). We also investigated the potential pathological roles and the likely mechanism of miR-451a in the development of HGSOC using animal models and cell lines.
Methods
Using bioinformatics techniques and a real-time PCR, we analyzed differently expressed miRNAs in HGSOC compared to normal tissue. MTT (i.e. 3-[4, 5-dimethyl thiazol-2-yl]-2,5-diphenyl tetrazolium bromide), EDU (i.e. 5-ethynyl-2′-deoxyuridine) and transwell assays were performed to investigate the effect of miR-451a on the proliferation and migration of HGSOC SKOV-3 cells. A dual luciferase reporter assay was performed to verify the targeting relationship of miR-451 and RAB5A (one of the Rab GTPase proteins that regulates endocytosis and vesicle transport). Also, we analyzed levels of the RAB5A mRNA and protein by real-time PCR, western blotting and immunohistochemistry assays in HGSOC cells and tissues. Finally, we performed in vivo experiments using HGSOC mice.
Results
miR-451a was substantially upregulated in HGSOC and associated with favorable clinical characteristics. miR-451a knockdown significantly increased growth and metastasis of HGSOC cell line SKOV-3 through Ras/Raf/mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK) signaling. In addition, RAB5A, an early endosome marker, was shown to be a direct target of miR-451a. Moreover, RAB5A is correlated with unfavorable clinical features and shows independent prognostic significance in HGSOC.
Conclusions
We found that the miR-451a/RAB5A axis is associated with tumorigenesis and progression through the Ras/Raf/MEK/ERK pathway, providing prognostic indicators and therapeutic targets for patients with HGSOC.
{"title":"miR-451a suppresses the proliferation and migration of high-grade serous ovarian cancer by targeting RAB5A through the Ras/Raf/MEK/ERK pathway","authors":"Shujie Liu, Kun Wang, Zhendan Zhao, Yu Pang, Fang Liu, Pengling Wang, Zhiling Wang, Xingsheng Yang","doi":"10.1002/jgm.3649","DOIUrl":"https://doi.org/10.1002/jgm.3649","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Ovarian cancer is one of the most common cancers in women. Profiles changes of microRNAs (miRNAs) are closely linked to malignant tumors. In the present study, we investigated expression of miR-451a in high-grade serous ovarian cancer (HGSOC). We also investigated the potential pathological roles and the likely mechanism of miR-451a in the development of HGSOC using animal models and cell lines.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Using bioinformatics techniques and a real-time PCR, we analyzed differently expressed miRNAs in HGSOC compared to normal tissue. MTT (i.e. 3-[4, 5-dimethyl thiazol-2-yl]-2,5-diphenyl tetrazolium bromide), EDU (i.e. 5-ethynyl-2′-deoxyuridine) and transwell assays were performed to investigate the effect of miR-451a on the proliferation and migration of HGSOC SKOV-3 cells. A dual luciferase reporter assay was performed to verify the targeting relationship of miR-451 and RAB5A (one of the Rab GTPase proteins that regulates endocytosis and vesicle transport). Also, we analyzed levels of the RAB5A mRNA and protein by real-time PCR, western blotting and immunohistochemistry assays in HGSOC cells and tissues. Finally, we performed in vivo experiments using HGSOC mice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>miR-451a was substantially upregulated in HGSOC and associated with favorable clinical characteristics. miR-451a knockdown significantly increased growth and metastasis of HGSOC cell line SKOV-3 through Ras/Raf/mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinase (ERK) signaling. In addition, RAB5A, an early endosome marker, was shown to be a direct target of miR-451a. Moreover, RAB5A is correlated with unfavorable clinical features and shows independent prognostic significance in HGSOC.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>We found that the miR-451a/RAB5A axis is associated with tumorigenesis and progression through the Ras/Raf/MEK/ERK pathway, providing prognostic indicators and therapeutic targets for patients with HGSOC.</p>\u0000 </section>\u0000 </div>","PeriodicalId":56122,"journal":{"name":"Journal of Gene Medicine","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139473978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shen Jiang, Xiping Yang, Yang Lin, Yunfei Liu, Lisa Jia Tran, Jing Zhang, Chengjun Qiu, Fangdie Ye, Zhou Sun
Background
Bladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis-related genes in the prognosis of BLCA.
Methods
We obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine-learning algorithms to develop the artificial intelligence-derived prognostic signature (AIDPS).
Results
The performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA-BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC50 values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high- and low-risk groups.
Conclusions
In conclusion, these findings indicated Anoikis-related prognostic genes in BLCA and constructed an innovative machine-learning model of AIDPS with high prognostic value for BLCA.
{"title":"Unveiling Anoikis-related genes: A breakthrough in the prognosis of bladder cancer","authors":"Shen Jiang, Xiping Yang, Yang Lin, Yunfei Liu, Lisa Jia Tran, Jing Zhang, Chengjun Qiu, Fangdie Ye, Zhou Sun","doi":"10.1002/jgm.3651","DOIUrl":"https://doi.org/10.1002/jgm.3651","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Bladder cancer (BLCA) is a prevalent malignancy worldwide. Anoikis remains a new form of cell death. It is necessary to explore Anoikis-related genes in the prognosis of BLCA.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We obtained RNA expression profiles from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases for dimensionality reduction analysis and isolated epithelial cells, T cells and fibroblasts for copy number variation analysis, pseudotime analysis and transcription factor analysis based on R package. We integrated machine-learning algorithms to develop the artificial intelligence-derived prognostic signature (AIDPS).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The performance of AIDPS with clinical indicators was stable and robust in predicting BLCA and showed better performance in every validation dataset compared to other models. Mendelian randomization analysis was conducted. Single nucleotide polymorphism (SNP) sites of rs3100578 (HK2) and rs66467677 (HSP90B1) exhibited significant correlation of bladder problem (not cancer) and bladder cancer, whereasSNP sites of rs3100578 (HK2) and rs947939 (BAD) had correlation between bladder stone and bladder cancer. The immune infiltration analysis of the TCGA-BLCA cohort was calculated via the ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) algorithm which contains stromal, immune and estimate scores. We also found significant differences in the IC<sub>50</sub> values of Bortezomib_1191, Docetaxel_1007, Staurosporine_1034 and Rapamycin_1084 among the high- and low-risk groups.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In conclusion, these findings indicated Anoikis-related prognostic genes in BLCA and constructed an innovative machine-learning model of AIDPS with high prognostic value for BLCA.</p>\u0000 </section>\u0000 </div>","PeriodicalId":56122,"journal":{"name":"Journal of Gene Medicine","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139434994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhen Jin, Yadong Han, Jiaxin Zhang, Zhao Liu, Ran Li, Zhao Liu
Background
Natural killer (NK) cells are crucial to cancer development and prognosis. However, the role of NK cell-related genes in immunotherapy and the tumor immune microenvironment (TIME) is not well understood. This study aimed to develop reliable risk signatures associated with NK cell-related genes for predicting thyroid cancer (THCA).
Methods
The single-cell RNA sequencing (scRNA-seq) data from seven THCA samples (GSE184362) and bulk-RNA-seq data of 502 THCA patients (TCGA-THCA) were included. The scRNA-seq data was analyzed using the “Seurat” R package to identify differentially expressed genes in NK cells. The clustering analysis was carried out using the R package “ConsensusClusterPlus”. The gene set variation analysis (GSVA) algorithm was applied to assess the variations in biological pathways among subtypes. The ESTIMATE algorithm was utilized to calculate the scores for stromal, immune and estimate variables. In addition, we used the single sample Gene Set Enrichment Analysis and CIBERSORT algorithms to assess the degree to which immune cells and pathways related to immunity were enriched based on the meta-cohort. In the TCGA-THCA cohort, the “glmnet” R package was used for the gene selection, and LASSO Cox analysis was used to construct prognostic features. The “maftools” R package was used to examine the somatic mutation landscape of THCA in both low- and high-risk groups.
Results
One-hundred and eighty-five NK cell marker genes were screened, and nine genes were associated with the THCA prognosis. KLF2, OSTF1 and TAPBP were finally identified and constructed a risk signature with significant prognostic value. KLF2 and OSTF1 were protective genes, and TAPBP was a risk gene. Patients at high risk had a considerably lower overall survival compared with those at low risk. Mutations in the TCGA-THCA cohort were predominantly C > T. Increased tumor mutation burden (TMB) levels were linked to overall survival. The low-risk H-TMB+ group had a better prognosis, while the high-risk L-TMB+ group had the worst prognosis.
Conclusion
Natural killer cell-related genes KLF2, OSTF1 and TAPBP were used to develop a novel prognostic risk signature, offering a new perspective on the prognosis and treatment of THCA.
{"title":"Prognosis and therapy in thyroid cancer by gene signatures related to natural killer cells","authors":"Zhen Jin, Yadong Han, Jiaxin Zhang, Zhao Liu, Ran Li, Zhao Liu","doi":"10.1002/jgm.3657","DOIUrl":"https://doi.org/10.1002/jgm.3657","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Natural killer (NK) cells are crucial to cancer development and prognosis. However, the role of NK cell-related genes in immunotherapy and the tumor immune microenvironment (TIME) is not well understood. This study aimed to develop reliable risk signatures associated with NK cell-related genes for predicting thyroid cancer (THCA).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The single-cell RNA sequencing (scRNA-seq) data from seven THCA samples (GSE184362) and bulk-RNA-seq data of 502 THCA patients (TCGA-THCA) were included. The scRNA-seq data was analyzed using the “Seurat” R package to identify differentially expressed genes in NK cells. The clustering analysis was carried out using the R package “ConsensusClusterPlus”. The gene set variation analysis (GSVA) algorithm was applied to assess the variations in biological pathways among subtypes. The ESTIMATE algorithm was utilized to calculate the scores for stromal, immune and estimate variables. In addition, we used the single sample Gene Set Enrichment Analysis and CIBERSORT algorithms to assess the degree to which immune cells and pathways related to immunity were enriched based on the meta-cohort. In the TCGA-THCA cohort, the “glmnet” R package was used for the gene selection, and LASSO Cox analysis was used to construct prognostic features. The “maftools” R package was used to examine the somatic mutation landscape of THCA in both low- and high-risk groups.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>One-hundred and eighty-five NK cell marker genes were screened, and nine genes were associated with the THCA prognosis. KLF2, OSTF1 and TAPBP were finally identified and constructed a risk signature with significant prognostic value. KLF2 and OSTF1 were protective genes, and TAPBP was a risk gene. Patients at high risk had a considerably lower overall survival compared with those at low risk. Mutations in the TCGA-THCA cohort were predominantly C > T. Increased tumor mutation burden (TMB) levels were linked to overall survival. The low-risk H-TMB+ group had a better prognosis, while the high-risk L-TMB+ group had the worst prognosis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Natural killer cell-related genes KLF2, OSTF1 and TAPBP were used to develop a novel prognostic risk signature, offering a new perspective on the prognosis and treatment of THCA.</p>\u0000 </section>\u0000 </div>","PeriodicalId":56122,"journal":{"name":"Journal of Gene Medicine","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139406957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yan Wang, Yuxia Zou, Xianghui Chen, Xiaoyan Wang, Hao Zheng, Qing Ye
Background
Nasopharyngeal carcinoma (NPC) is a highly aggressive and metastatic malignancy originating in the nasopharyngeal tissue. Pyroptosis is a relatively newly discovered, regulated form of necrotic cell death induced by inflammatory caspases that is associated with a variety of diseases. However, the role and mechanism of pyroptosis in NPC are not fully understood.
Methods
We analyzed the differential expression of pyroptosis-related genes (PRGs) between patients with and without NPC from the GSE53819 and GSE64634 datasets of the Gene Expression Omnibus (GEO) database. We mapped receptor operating characteristic profiles for these key PRGs to assess the accuracy of the genes for disease diagnosis and prediction of patient prognosis. In addition, we constructed a nomogram based on these key PRGs and carried out a decision curve analysis. The NPC patients were classified into different pyroptosis gene clusters by the consensus clustering method based on key PRGs, whereas the expression profiles of the key PRGs were analyzed by applying principal component analysis. We also analyzed the differences in key PRGs, immune cell infiltration and NPC-related genes between the clusters. Finally, we performed differential expression analysis for pyroptosis clusters and obtained differentially expressed genes (DEGs) and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses.
Results
We obtained 14 differentially expressed PRGs from GEO database. Based on these 14 differentially expressed PRGs, we applied least absolute shrinkage and selection operator analysis and the random forest algorithm to obtain four key PRGs (CHMP7, IL1A, TP63 and GSDMB). We completely distinguished the NPC patients into two pyroptosis gene clusters (pyroptosis clusters A and B) based on four key PRGs. Furthermore, we determined the immune cell abundance of each NPC sample, estimated the association between the four PRGs and immune cells, and determined the difference in immune cell infiltration between the two pyroptosis gene clusters. Finally, we obtained and functional enrichment analyses 259 DEGs by differential expression analysis for both pyroptosis clusters.
Conclusions
PRGs are critical in the development of NPC, and our research on the pyroptosis gene cluster may help direct future NPC therapeutic approaches.
{"title":"Relevance of pyroptosis-associated genes in nasopharyngeal carcinoma diagnosis and subtype classification","authors":"Yan Wang, Yuxia Zou, Xianghui Chen, Xiaoyan Wang, Hao Zheng, Qing Ye","doi":"10.1002/jgm.3653","DOIUrl":"https://doi.org/10.1002/jgm.3653","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Nasopharyngeal carcinoma (NPC) is a highly aggressive and metastatic malignancy originating in the nasopharyngeal tissue. Pyroptosis is a relatively newly discovered, regulated form of necrotic cell death induced by inflammatory caspases that is associated with a variety of diseases. However, the role and mechanism of pyroptosis in NPC are not fully understood.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We analyzed the differential expression of pyroptosis-related genes (PRGs) between patients with and without NPC from the GSE53819 and GSE64634 datasets of the Gene Expression Omnibus (GEO) database. We mapped receptor operating characteristic profiles for these key PRGs to assess the accuracy of the genes for disease diagnosis and prediction of patient prognosis. In addition, we constructed a nomogram based on these key PRGs and carried out a decision curve analysis. The NPC patients were classified into different pyroptosis gene clusters by the consensus clustering method based on key PRGs, whereas the expression profiles of the key PRGs were analyzed by applying principal component analysis. We also analyzed the differences in key PRGs, immune cell infiltration and NPC-related genes between the clusters. Finally, we performed differential expression analysis for pyroptosis clusters and obtained differentially expressed genes (DEGs) and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We obtained 14 differentially expressed PRGs from GEO database. Based on these 14 differentially expressed PRGs, we applied least absolute shrinkage and selection operator analysis and the random forest algorithm to obtain four key PRGs (CHMP7, IL1A, TP63 and GSDMB). We completely distinguished the NPC patients into two pyroptosis gene clusters (pyroptosis clusters A and B) based on four key PRGs. Furthermore, we determined the immune cell abundance of each NPC sample, estimated the association between the four PRGs and immune cells, and determined the difference in immune cell infiltration between the two pyroptosis gene clusters. Finally, we obtained and functional enrichment analyses 259 DEGs by differential expression analysis for both pyroptosis clusters.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>PRGs are critical in the development of NPC, and our research on the pyroptosis gene cluster may help direct future NPC therapeutic approaches.</p>\u0000 </section>\u0000 </div>","PeriodicalId":56122,"journal":{"name":"Journal of Gene Medicine","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139400163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rheumatoid arthritis (RA), a common autoimmune disease, exhibits a vital genetic component. Polygenic risk scores (PRS) derived from genome-wide association studies (GWAS) offer potential utility in predicting disease susceptibility. The present study aimed to develop and validate a PRS for predicting RA risk in postmenopausal women.
Methods
The study developed a novel PRS using 225,000 genetic variants from a GWAS dataset. The PRS was developed in a cohort of 8967 postmenopausal women and validated in an independent cohort of 6269 postmenopausal women. Among the development cohort, approximately 70% were Hispanic and approximately 30% were African American. The testing cohort comprised approximately 50% Hispanic and 50% Caucasian individuals. Stratification according to PRS quintiles revealed a pronounced gradient in RA prevalence and odds ratios.
Results
High PRS was significantly associated with increased RA risk in individuals aged 60–70 years, ≥ 70 years, and overweight and obese participants. Furthermore, at age 65 years, individuals in the bottom 5% of the PRS distribution have an absolute risk of RA at 30.6% (95% confidence interval = 18.5%–42.6%). The risk increased to 53.8% (95% confidence interval = 42.8%–64.9%) for those in the top 5% of the PRS distribution.
Conclusions
The PRS developed in the present study is significantly associated with RA risk, showing the potential for early screening of RA in postmenopausal women. This work demonstrates the feasibility of personalized medicine in identifying high-risk individuals for RA, indicating the need for further studies to test the utility of PRS in other populations.
背景类风湿性关节炎（RA）是一种常见的自身免疫性疾病，具有重要的遗传因素。从全基因组关联研究（GWAS）中得出的多基因风险评分（PRS）可用于预测疾病的易感性。本研究旨在开发并验证用于预测绝经后女性 RA 风险的 PRS。 方法 该研究利用 GWAS 数据集中的 225,000 个基因变异开发了一种新型 PRS。该PRS是在8967名绝经后妇女的队列中开发的，并在6269名绝经后妇女的独立队列中进行了验证。在开发队列中，约 70% 为西班牙裔，约 30% 为非裔美国人。测试队列中约有 50%为西班牙裔，50%为白种人。根据 PRS 五分位数进行的分层显示了 RA 患病率和几率的明显梯度。 结果 在 60-70 岁、≥ 70 岁以及超重和肥胖的参与者中，高 PRS 与 RA 风险的增加明显相关。此外，在 65 岁时，PRS 分布中最低 5%的人患 RA 的绝对风险为 30.6%（95% 置信区间 = 18.5%-42.6%）。而PRS分布前5%的人的风险则增加到53.8%（95%置信区间=42.8%-64.9%）。 结论 本研究开发的 PRS 与 RA 风险显著相关，显示了对绝经后妇女进行 RA 早期筛查的潜力。这项工作证明了个性化医疗在识别 RA 高危人群方面的可行性，表明有必要开展进一步研究，以测试 PRS 在其他人群中的实用性。
{"title":"Genome-wide polygenic risk score for rheumatoid arthritis prediction in postmenopausal women","authors":"Yingke Xu, Qing Wu","doi":"10.1002/jgm.3659","DOIUrl":"https://doi.org/10.1002/jgm.3659","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Rheumatoid arthritis (RA), a common autoimmune disease, exhibits a vital genetic component. Polygenic risk scores (PRS) derived from genome-wide association studies (GWAS) offer potential utility in predicting disease susceptibility. The present study aimed to develop and validate a PRS for predicting RA risk in postmenopausal women.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The study developed a novel PRS using 225,000 genetic variants from a GWAS dataset. The PRS was developed in a cohort of 8967 postmenopausal women and validated in an independent cohort of 6269 postmenopausal women. Among the development cohort, approximately 70% were Hispanic and approximately 30% were African American. The testing cohort comprised approximately 50% Hispanic and 50% Caucasian individuals. Stratification according to PRS quintiles revealed a pronounced gradient in RA prevalence and odds ratios.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>High PRS was significantly associated with increased RA risk in individuals aged 60–70 years, ≥ 70 years, and overweight and obese participants. Furthermore, at age 65 years, individuals in the bottom 5% of the PRS distribution have an absolute risk of RA at 30.6% (95% confidence interval = 18.5%–42.6%). The risk increased to 53.8% (95% confidence interval = 42.8%–64.9%) for those in the top 5% of the PRS distribution.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The PRS developed in the present study is significantly associated with RA risk, showing the potential for early screening of RA in postmenopausal women. This work demonstrates the feasibility of personalized medicine in identifying high-risk individuals for RA, indicating the need for further studies to test the utility of PRS in other populations.</p>\u0000 </section>\u0000 </div>","PeriodicalId":56122,"journal":{"name":"Journal of Gene Medicine","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jgm.3659","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139400164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Autophagy plays an important role in immunity and inflammation. The present study aimed to explore the prognostic significance of autophagy-related genes (ARGs) in endometrial cancer (EC) using bioinformatics.
Methods
The list of ARGs was obtained from the Human Autophagy Database. The differentially expressed ARGs (DEARGs) between the EC and normal endometrial tissue samples were screened from The Cancer Genome Atlas database. Cox regression analysis was performed on the DEARGs to screen the prognostic ARGs and construct risk signatures for overall survival (OS) and progression-free survival (PFS). The hub ARGs were identified from a protein–protein interaction network, and CDKN2A was obtained from the intersection of prognostic ARGs and hub ARGs. The association of CDKN2A expression with clinical characteristics and immune infiltration were analyzed. Finally, the role of CDKN2A in autophagy was confirmed in EC cell lines.
Results
CDKN2A, PTK6 and DLC1 were used to establish risk signatures for predicting the survival of EC patients. Receiver operating characteristic curve analysis indicated that the risk signatures can accurately predict both OS and PFS. CDKN2A was the only hub prognostic ARG, and showed significant association with the age, survival status, grade, histological type, body mass index and FIGO (i.e. International Federation of Gynecology and Obstetrics) stage (p < 0.05). Furthermore, CDKN2A expression was also correlated with the infiltration of immune cells, indicating that CDKN2A might play a critical role in regulating the immune microenvironment and immune responses in EC. In addition, silencing of CDKN2A gene promoted autophagy in the HEC-1A cell line and upregulated the expression levels of autophagy-related proteins.
Conclusions
CDKN2A is a prognostic factor and therapeutic target in EC, and is likely associated with the tumor immune landscape and autophagy.
{"title":"Autophagy-related risk signature based on CDNK2A to facilitate survival prediction of patients with endometrial cancer","authors":"Chaomin Yue, Baohua Lin, Xiang Sun, Xindi Xu, Chufan Zhou, Jiaying Fan","doi":"10.1002/jgm.3648","DOIUrl":"https://doi.org/10.1002/jgm.3648","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Autophagy plays an important role in immunity and inflammation. The present study aimed to explore the prognostic significance of autophagy-related genes (ARGs) in endometrial cancer (EC) using bioinformatics.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The list of ARGs was obtained from the Human Autophagy Database. The differentially expressed ARGs (DEARGs) between the EC and normal endometrial tissue samples were screened from The Cancer Genome Atlas database. Cox regression analysis was performed on the DEARGs to screen the prognostic ARGs and construct risk signatures for overall survival (OS) and progression-free survival (PFS). The hub ARGs were identified from a protein–protein interaction network, and <i>CDKN2A</i> was obtained from the intersection of prognostic ARGs and hub ARGs. The association of <i>CDKN2A</i> expression with clinical characteristics and immune infiltration were analyzed. Finally, the role of <i>CDKN2A</i> in autophagy was confirmed in EC cell lines.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p><i>CDKN2A, PTK6</i> and <i>DLC1</i> were used to establish risk signatures for predicting the survival of EC patients. Receiver operating characteristic curve analysis indicated that the risk signatures can accurately predict both OS and PFS. <i>CDKN2A</i> was the only hub prognostic ARG, and showed significant association with the age, survival status, grade, histological type, body mass index and FIGO (i.e. International Federation of Gynecology and Obstetrics) stage (<i>p</i> < 0.05). Furthermore, <i>CDKN2A</i> expression was also correlated with the infiltration of immune cells, indicating that <i>CDKN2A</i> might play a critical role in regulating the immune microenvironment and immune responses in EC. In addition, silencing of <i>CDKN2A</i> gene promoted autophagy in the HEC-1A cell line and upregulated the expression levels of autophagy-related proteins.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p><i>CDKN2A</i> is a prognostic factor and therapeutic target in EC, and is likely associated with the tumor immune landscape and autophagy.</p>\u0000 </section>\u0000 </div>","PeriodicalId":56122,"journal":{"name":"Journal of Gene Medicine","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139110062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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