Translational cancer research最新文献

{"title":"Construction and validation of senescence risk score signature as a novel biomarker in liver hepatocellular carcinoma: a bioinformatic analysis.","authors":"Tianqi Lai, Feilong Li, Leyang Xiang, Zhilong Liu, Qiang Li, Mingrong Cao, Jian Sun, Youzhu Hu, Tongzheng Liu, Junjie Liang","doi":"10.21037/tcr-23-2373","DOIUrl":"10.21037/tcr-23-2373","url":null,"abstract":"<p><strong>Background: </strong>Globally, liver cancer as one of the most frequent fatal malignancies, hits hard and fast. And the lack of effective treatments for liver hepatocellular carcinoma (LIHC), activates the researchers to promote promising precision medicine. Interestingly, emerging evidence proves that cellular senescence is involved in the progression of cancers and is recognized for its hallmark-promoting capabilities. Hence, efforts have been made to construct and validate the senescence risk score signature (SRSS) model as a novel prognostic biomarker for LIHC.</p><p><strong>Methods: </strong>The existing databases were mined for the following bioinformatics analyses. GSE22405, GSE57957, and senescence-related genes (SRGs) from public databases were utilized as a training set and the validation set was constituted by LIHC and pancreatic adenocarcinoma (PAAD) from The Cancer Genome Atlas (TCGA). After overlapping differentially expressed genes (DEGs) with SRGs, differentially expressed SRGs were identified with the progression of liver cancer through univariate and multivariate Cox regression and enrichment analyses. The model that utilized three SRGs was constructed using the least absolute shrinkage and selection operator (LASSO) regression algorithm. Next, to evaluate the predictive performance of the SRSS model, the overall survival (OS) and survival rates were assessed through Kaplan-Meier (KM) and the receiver operating characteristic (ROC) curves. The predictive value for LIHC prognosis was further evaluated by capitalizing on risk score, nomograms, decision curve analysis (DCA) curves, and clinical information including tumor stages, gender, age, and race.</p><p><strong>Results: </strong>DEGs were revealed as enriching in multiple tumor-related biological processes (BPs) and pathways. <i>IGFBP3</i>, <i>SOCS2</i>, and <i>RACGAP1</i> were identified as the three considerable SRGs for the model. The high-risk group had a worse prognosis [both hazard ratio (HR) >1, P<0.001] and ROC curves showed a reliable predictive model with area under the curve (AUC) predictive values ranging from 0.673-0.816 for different-year survival rates respectively. The univariate and multivariate Cox regression analyses exhibited that risk score was the only credible prognostic predictor (HR >1, P<0.001) among clinical features such as tumor stage, age, etc., in LIHC. The nomograms, and DCA curves, combined with multiple clinical information, proved that the predictive ability of SRSS was strongest, followed by nomogram and traditional tumor node metastasis (TNM) stage was the weakest.</p><p><strong>Conclusions: </strong>In summary, comprehensive analyses supported that the SRSS model can better predict survival and risk in LIHC patients. Promisingly, it may point out a brand-new direction for LIHC therapy.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"13 9","pages":"4786-4799"},"PeriodicalIF":1.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142475541","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}

{"title":"Development and validation of a nomogram to predict overall survival of gastroenteropancreatic neuroendocrine carcinoma: a SEER database analysis.","authors":"Qishuang Chen, Yiying Guo, Zihan Wang, Xiaoying Chen, Chao Tian, Jiabin Zheng, Huangying Tan","doi":"10.21037/tcr-23-2215","DOIUrl":"10.21037/tcr-23-2215","url":null,"abstract":"<p><strong>Background: </strong>Gastroenteropancreatic neuroendocrine carcinoma (GEP-NEC) is a rare group of diseases with poor prognosis and the assessment of its prognosis is a significant challenge. This study aimed to develop and validate a prognostic nomogram to assess overall survival (OS) in patients with GEP-NEC.</p><p><strong>Methods: </strong>Patients diagnosed with poorly differentiated GEP-NEC were collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2011 and 2015 and were randomly assigned to the training or validation cohort in a 7:3 ratio. The data included details of clinicopathological characteristics, therapeutic interventions and survival outcomes. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. Nomogram was used to predict OS at 1 and 2 years. The nomogram was internally validated with validation cohort, and its predictive ability was evaluated using concordance index (C-index), receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and integrated discrimination improvement (IDI) index.</p><p><strong>Results: </strong>A total of 887 patients were divided into the training group (n=623) and the validation group (n=264). A total of 476 patients (53.66%) were in stage IV. Based on multivariate analysis, a nomogram was constructed with age, gender, N stage, tumor size, primary tumor resection, radiotherapy and chemotherapy (P<0.05). The C-index was 0.701 [95% confidential interval (CI): 0.677-0.725] and 0.731 (95% CI: 0.698-0.764) for the training and validation groups, respectively. The C-index, ROC, IDI and DCA results indicated that this nomogram model has a good predictive value.</p><p><strong>Conclusions: </strong>In this study, a nomogram model based on seven independent prognostic factors provided visualization of the risk and could help clinicians predict the 1-year and 2-year OS for GEP-NEC. This tool can provide personalized survival predictions and improve clinical decision making for the management of GEP-NEC.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"13 9","pages":"4678-4693"},"PeriodicalIF":1.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142475545","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}

{"title":"Prognostic biomarkers based on <i>GUF1</i>, <i>EFTUD2</i> and <i>GSPT1</i> targets affecting migration of gastric cancer cells.","authors":"Haixiu Ma, Lina Suo, Jing Zhao, Ronghua Ma, Qi Wang, Jun Liu, Jinwan Qiao, Juan Wu, Juan An, Yan Liu, Yonghua Xing, Haiyan Wang, Zhanhai Su","doi":"10.21037/tcr-24-125","DOIUrl":"10.21037/tcr-24-125","url":null,"abstract":"<p><strong>Background: </strong>Eukaryotic elongation factor 1 alpha 2 (eEF1A2) is a protein coding gene which is involved in tumor development and progression in several types of human cancer, but little is known about the function of eEF1A2 proteins in gastric cancer (GC). This study aimed to investigate the effects of <i>GUF1</i>, <i>EFTUD2</i> and <i>GSPT1</i> on the migration of GC cells.</p><p><strong>Methods: </strong>The Oncomine and The Cancer Genome Atlas (TCGA) databases were used to evaluate the expression of <i>GUF1</i>, <i>EFTUD2</i>, <i>GSPT1</i> and <i>GSPT2</i> in GC and the association of eEF1A2 family with individual clinical characteristics. Kaplan-Meier (K-M) Plotter hinted the prognostic value of <i>GUF1</i>, <i>EFTUD2</i>, <i>GSPT1</i> and <i>GSPT2</i>. GSE62254 and GSE66222 datasets were used to validate the expression of <i>GUF1</i>, <i>EFTUD2</i>, <i>GSPT1</i>. AGS cell line and GES line were also used for validating the function of <i>GUF1</i>, <i>EFTUD2</i>, <i>GSPT1</i>. RNA interference (RNAi) of GUF1, EFTUD2 and GSPT1 had been used to query those genes expression pattern and dissect the proliferation and migration in GC cell lines.</p><p><strong>Results: </strong><i>GUF1</i>, <i>EFTUD2</i> and <i>GSPT1</i> were significantly up-regulated in GC cell lines. High expression of <i>GUF1</i>, <i>EFTUD2</i> and <i>GSPT1</i> was correlated with cell proliferation and migration induced in GC cells. <i>GUF1</i>, <i>EFTUD2</i> and <i>GSPT1</i> may be potential novel oncogenes that helps to maintain the survival of GC cells.</p><p><strong>Conclusions: </strong>This study identified that high levels of <i>GUF1</i>, <i>EFTUD2</i> and <i>GSPT1</i> expression are predictive biomarkers for a poor prognosis in GC.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"13 9","pages":"4827-4845"},"PeriodicalIF":1.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483415/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142475567","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}

{"title":"Heterogeneity of tumor microenvironment cell groups in inflammatory and adenomatous polyposis coli mutant colorectal cancer based on single cell sequencing.","authors":"Liyang Liang, Chao Zhang, Jiawang Han, Zhipeng Liu, Jinzhong Liu, Suhang Wu, Hongyu Wang","doi":"10.21037/tcr-24-689","DOIUrl":"10.21037/tcr-24-689","url":null,"abstract":"<p><strong>Background: </strong>The prognosis of colorectal cancer (CRC) is known to vary across different etiologies. Inflammatory bowel disease (IBD) is often identified as a factor contributing to poorer outcomes. However, the mechanisms that link IBD to a worse CRC prognosis remain to be elucidated. We aim to reveal the complex tumor microenvironment of inflammatory CRC and provide a weak theoretical basis for the treatment of different subtypes of CRC.</p><p><strong>Methods: </strong>We conducted a bioinformatics analysis using single-cell RNA sequencing (scRNA-seq) data from 8,494 individual CRC cells derived from azoxymethane (AOM)/dextran sodium sulfate (DSS) and adenomatous polyposis coli (APC) mutant datasets. The expression of implicated genes in both tumor and adjacent normal tissues was examined via immunohistochemistry and immunofluorescence.</p><p><strong>Results: </strong>CRC from AOM/DSS treatment contained fewer immune cells relative to APC-mutant CRC. However, a macrophage subcluster enriched for inflammatory factors was more prevalent in AOM/DSS datasets. This subcluster exhibited elevated expression of APOE and BNIP3. Immunofluorescence and immunohistochemistry of patient samples confirmed that the expression of APOE and BNIP3 was higher in adjacent normal tissues compared to tumors.</p><p><strong>Conclusions: </strong>Our findings shed light on the heterogeneous microenvironments in IBD and APC-mutant CRC. Furthermore, we identify APOE as a potential biomarker for CRC recurrence.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"13 9","pages":"4813-4826"},"PeriodicalIF":1.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142475560","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}

{"title":"Construction of a prognostic model with exosome biogenesis- and release-related genes and identification of RAB27B in immune infiltration of pancreatic cancer.","authors":"Tian-Yu Li, Cheng Qin, Bang-Bo Zhao, Ze-Ru Li, Yuan-Yang Wang, Yu-Tong Zhao, Wei-Bin Wang","doi":"10.21037/tcr-24-54","DOIUrl":"10.21037/tcr-24-54","url":null,"abstract":"<p><strong>Background: </strong>Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive and fatal disease. Exosomes are extracellular vesicles that plays a vital rule in the progression and metastasis of PDAC. However, the specific mechanism of exosome biogenesis and release in the tumorigenesis and development of pancreatic cancer remains elusive. The aim of this study is to develop novel biomarkers and construct a reliable prognostic signature to accurately stratify patients and optimize clinical decision-making.</p><p><strong>Methods: </strong>Gene expression and clinical data were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Univariate Cox regression analysis, random forest analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis were used to construct the risk signature. The effectiveness of the model was validated by survival point plot, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) curve in training, testing and entire cohorts. Meanwhile, single sample gene set enrichment analysis (ssGSEA), ESTIMATE and CIBERSORT algorithm were utilized to assess the association of the risk signature with the immune status in the PDAC tumor microenvironment. We also performed functional enrichment, tumor mutation analysis, and DNA methylation analyses based on the risk signature. The function of the core gene was further verified by polymerase chain reaction (PCR), western blot, bicinchoninic acid (BCA), immunohistochemistry (IHC) and <i>in vitro</i> experiments including cell proliferation, migration, and apoptosis experiments.</p><p><strong>Results: </strong>We constructed an exosome biogenesis- and release-related risk model which could serve as an effective and independent prognosis predictor for PDAC patients. The immune infiltration analysis revealed that our signature was related to the PDAC immune microenvironment, mainly associated with a lower proportion of natural killer (NK) cells and CD8⁺ T cells. Tissue microarray IHC confirmed the association of RAB27B with poor prognosis in PDAC. Knockdown of RAB27B expression promoted PDAC cells' apoptosis, while decreased cellular proliferation and migration. Also, knockdown of RAB27B expression led to reduced exosome secretion, while RAB27B overexpression promoted exosome secretion.</p><p><strong>Conclusions: </strong>The predictive signature can predict overall survival, help elucidate the mechanism of exosome biogenesis and release, and provide immunotherapy guidance for PDAC patients.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"13 9","pages":"4846-4865"},"PeriodicalIF":1.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483359/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142475543","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}

{"title":"Development and validation of a clinical diagnostic model for surgical site infection after surgery in patients with gastric cancer.","authors":"Yiyun Peng, Yuqi Ma, Guoyuan Yang, Yalong Huang, Hao Lin, Xiaolong Ma, Yongjiang Yu, Yuntao Ma","doi":"10.21037/tcr-24-79","DOIUrl":"10.21037/tcr-24-79","url":null,"abstract":"<p><strong>Background: </strong>Surgical site infection (SSI) is a common and serious complication following gastric cancer surgery, often linked to patient age, surgery duration, and the surgical approach taken. Accurate prediction and personalized mitigation of SSI risk are crucial for improving surgical outcomes. While prior studies have focused on SSI rates after open and laparoscopic gastric cancer surgeries, it is important to also consider robot-assisted procedures. This study aims to develop a predictive model for SSI after radical gastric cancer surgery, validate it through external testing, and provide a reliable tool for clinical use.</p><p><strong>Methods: </strong>Data from 763 postoperative gastric cancer patients were analyzed, with 601 in the training set from Gansu Provincial People's Hospital and 162 in the validation set from The First Hospital of Lanzhou University. All available variables were considered as potential predictors, and factors influencing SSI post-surgery were identified using logistic regression. A nomogram model was then created for precise SSI risk prediction.</p><p><strong>Results: </strong>Among the 763 gastric cancer patients, 10.9% experienced postoperative SSI. Significant differences were noted in the American Society of Anesthesiologists (ASA) physical status classification system classification, preoperative albumin levels, surgical approach, and reconstruction techniques between groups. Age, surgery duration, surgical approach, total gastrectomy, and tumor diameter were identified as significant predictors of SSI. The nomogram model showed high predictive accuracy, with concordance index (C-index) values of 0.834 in the training set and 0.798 in the validation set. Calibration plots and decision curve analysis (DCA) further validated the model's performance.</p><p><strong>Conclusions: </strong>This study identified five key predictors of postoperative SSI in gastric cancer and developed a nomogram model to enhance SSI prediction. These findings have important implications for preventing SSI in gastric cancer surgeries.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"13 9","pages":"4659-4670"},"PeriodicalIF":1.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483434/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142475544","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}

{"title":"Erratum to reduction of Hip2 suppresses gastric cancer cell proliferation, migration, invasion and tumorigenesis.","authors":"","doi":"10.21037/tcr-2024-4","DOIUrl":"10.21037/tcr-2024-4","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.21037/tcr.2019.12.12.].</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"13 9","pages":"5159-5160"},"PeriodicalIF":1.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483339/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142475549","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}

{"title":"GLYAT suppresses liver cancer and clear cell renal cell carcinoma progression by downregulating ROCK1 expression.","authors":"Yechen Xia, Wentao Huang, Guang-Zhi Jin","doi":"10.21037/tcr-24-1412","DOIUrl":"10.21037/tcr-24-1412","url":null,"abstract":"<p><strong>Background: </strong>The liver and kidney are important metabolic organs in the body and common sites of tumor occurrence. Glycine-N-acyltransferase (GLYAT) is primarily expressed in the liver and kidney and downregulated in several tumors. But its specific functions and molecular mechanisms in liver cancer and clear cell renal cell carcinoma (ccRCC) have not yet been fully elucidated. The aim of this study was to explore the role and clinical significance of GLYAT in liver cancer and ccRCC.</p><p><strong>Methods: </strong>This study used proteomics technology to identify differentially expressed proteins in liver cancer. Western blot and immunohistochemistry (IHC) were used to analyze the protein expression pattern of GLYAT. assays were performed in liver cancer and ccRCC cells. Xenograft models in nude mice were used to confirm the roles of GLYAT in liver cancer. Moreover, the downstream regulatory proteins of GLYAT were identified by proteomics.</p><p><strong>Results: </strong>GLYAT was lowly expressed in liver cancer and ccRCC. Immunofluorescence staining indicated that GLYAT was mainly expressed in the cytoplasm, particularly the mitochondria. Kaplan-Meier curves showed that the low protein expression of GLYAT was correlated with a poor prognosis in liver cancer and ccRCC patients. Moreover, GLYAT expression was associated with several clinical parameters in liver cancer. Cell experiments showed that the overexpression of GLYAT inhibited cell proliferation and migration abilities; however, interfering GLYAT protein expression rescued these abilities in GLYAT overexpression (GLYAT-OE) cells. <i>In vivo</i> assays confirmed the tumor-suppressor function of GLYAT in liver cancer. Moreover, our research showed that GLYAT downregulated Rho-associated coiled-coil-containing protein kinase 1 (ROCK1).</p><p><strong>Conclusions: </strong>Our study showed that GLYAT is lowly expressed in liver cancer and ccRCC, emphasizing its prognostic significance. It also showed that GLYAT inhibits the progression of liver cancer and ccRCC by downregulating ROCK1.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"13 9","pages":"5097-5111"},"PeriodicalIF":1.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483444/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142475559","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}

{"title":"Prognosis and progression of phagocytic regulatory factor-related gene combinations in clear cell renal cell carcinoma (ccRCC).","authors":"Ruihai Xiao, Zepeng Luo, Hongwei Huang, Yingqun Yin","doi":"10.21037/tcr-24-139","DOIUrl":"10.21037/tcr-24-139","url":null,"abstract":"<p><strong>Background: </strong>Developing signatures based on specific characteristics to predict prognosis has become a research hotspot in oncology. However, the prognostic value of phagocytosis regulators in clear cell renal cell carcinoma (ccRCC) remains unclear. The aim of the present study was to investigate the prognostic significance of phagocytosis regulators in ccRCC by constructing a prognostic model related to phagocytosis regulators, and to use this model to evaluate the prognosis and treatment effects in ccRCC patients.</p><p><strong>Methods: </strong>Firstly, kidney renal clear cell carcinoma (KIRC) transcriptome data (RNA-Seq) and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. Based on literatures PMID 34497417 and PMID 30397336, 167 of the 173 phagocytosis regulator genes collected in the literature were expressed in TCGA-KIRC. The relationship between these regulators and macrophages was revealed through single-sample gene set enrichment analysis (ssGSEA), and their biological and pathway involvements were further analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) method were employed to further select phagocytosis regulators with prognostic potential, leading to the construction of a prognostic regression model. Additionally, univariate and multivariate Cox regression analyses were conducted to confirm the prognostic independence of genes associated with phagocytosis regulators. Finally, the relationship between phagocytosis regulator-related genes and patients' immune microenvironments and immunotherapy responses was explored.</p><p><strong>Results: </strong>We have constructed a prognostic model of a combination of genes associated with phagocytosis regulators using LASSO Cox regression analysis of genes, and our combined model was shown to be an independent prognostic factor. The model had optimal performance in predicting long-term survival. Clinical features were significantly correlated with phagocytosis regulatory gene scores. Tumors with higher levels of grade and stage were more prone to have higher phagocytosis regulatory genes. And our study suggests that phagocytosis regulatory genes do not play an ideal role in predicting the efficacy of immunotherapy in patients.</p><p><strong>Conclusions: </strong>We have constructed a prognostic model using a combination of genes associated with phagocytosis regulators, providing new insights into the prognosis and progression of ccRCC.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"13 9","pages":"4878-4895"},"PeriodicalIF":1.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483360/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142475566","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}

{"title":"A novel prognostic model based on telomere-related lncRNAs in gastric cancer.","authors":"Xuetong Ding, Yi Zhang, Shijie You","doi":"10.21037/tcr-24-295","DOIUrl":"10.21037/tcr-24-295","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Telomeres are specialized structures at the ends of chromosomes that are important for their protection. Over time, long non-coding RNAs (lncRNAs) have gradually come into the spotlight as essential biomarkers of proliferation, migration, and invasion of human malignant tumors. Nevertheless, the impact of telomere-related lncRNAs (TRLs) in gastric cancer is currently unknown. In the present study, we screen the TRLs and identify a prognostic TRLs signature in gastric cancer.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;First, telomere-related genes (TRGs) were retrieved from the website, and RNA sequencing (RNA-seq) data and clinical data of stomach adenocarcinoma (STAD) patients were gathered from The Cancer Genome Atlas (TCGA) database. Gastric cancer patients' lncRNAs and overall survival (OS) were found to be related using univariate Cox regression analysis. Next, least absolute shrinkage and selection operator (LASSO) regression analysis and multifactorial Cox regression analysis were used to further screen telomere-related differentially expressed lncRNAs (TRDELs), and finally six lncRNAs were obtained, including &lt;i&gt;LINC01537&lt;/i&gt;, &lt;i&gt;CFAP61-AS1&lt;/i&gt;, &lt;i&gt;DIRC1&lt;/i&gt;, &lt;i&gt;RABGAP1L-IT1&lt;/i&gt;, &lt;i&gt;DBH-AS1&lt;/i&gt;, and &lt;i&gt;REPIN1-AS1&lt;/i&gt;. According to these six TRDELs, a prognostic model for gastric cancer was constructed. The samples were divided into the training group and the testing group at random, and the reliability of prognostic model was validated in both groups and overall samples. In addition, we performed Kaplan-Meier (K-M) survival curve analysis, independent prognostic analysis, and functional enrichment analysis to validate the predictive value and independence of the model, as well as immune cell correlation analysis, clustering analysis, and principal component analysis (PCA) to further explore the relationship between this model and the tumor cells. Finally, we performed the drug sensitivity analysis to identify a few small molecules that may have a therapeutic effect on gastric cancer.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Finally, we constructed a prognostic model for gastric cancer consisting of six TRDELs. According to the K-M curve, the prognosis of the low-risk group was noticeably superior than that of the high-risk group. Multivariate Cox regression analysis suggested that risk score was an independent prognostic element. Receiver operating characteristic (ROC) curves, nomogram, and calibration curve indicated that the prognostic model had good predictive ability. Functional enrichment analysis demonstrated major pathways with high- and low-risk groups. Next, both tumor microenvironment (TME) and immune correlation analysis showed discrepancy in the high- and low-risk groups. Through drug sensitivity analysis, we screened four small molecules that might be beneficial for gastric cancer treatment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;A prognostic model consisting of these six TRDELs was capable to predict the prognosis o","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"13 9","pages":"4608-4624"},"PeriodicalIF":1.5,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11483442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142475464","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}