Technology in Cancer Research & Treatment最新文献

{"title":"Constructing a Classification Model for Cervical Cancer Tumor Tissue and Normal Tissue Based on CT Radiomics.","authors":"Jinghong Pei, Jing Yu, Ping Ge, Liman Bao, Haowen Pang, Huaiwen Zhang","doi":"10.1177/15330338241298554","DOIUrl":"10.1177/15330338241298554","url":null,"abstract":"<p><p>This study aimed to develop an automated classification framework for distinguishing between cervical cancer tumor and normal uterine tissue, leveraging CT images for radiomics feature extraction. We retrospectively analyzed CT images from 117 cervical cancer patients. To distinguish between cancerous and healthy tissue, we segmented gross tumor volume and normal uterine tissue as distinct regions of interest (ROIs) using manual segmentation techniques. Key radiomic parameters were extracted from these ROIs. To bolster model's predictive capability, the data was stratified into train data (70%) and validation data (30%). During feature selection phase, we applied Least Absolute Shrinkage and Selection Operator regression algorithm to identify most relevant features. Subsequently, we built classification models using five state-of-the-art machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Decision Tree (DT). Ultimately, the performance of each model was evaluated. Through stringent feature selection process, we identified 18 pivotal radiomic features for classification of cervical cancer and normal uterine tissue. When applied to test data, all five models achieved excellent performance, with area under the curve (AUC) values ranging from 0.8866 to 0.9190 (SVM: 0.9144, RF: 0.9078, KNN: 0.9051, DT: 0.8866, XGBoost: 0.9190), all surpassing threshold of 0.8. In terms of test data, all five models had high sensitivity; accuracy of SVM, RF, and XGBoost models was comparable; and specificity of five models was similar. XGBoost model outperformed the others in terms of diagnostic accuracy, achieving an AUC of 0.8737 (95% CI: 0.8198-0.9277) for train data and 0.9190 (95% CI: 0.8525-0.9854) for test data. Our findings underscore the potential of CT radiomics combined with machine learning algorithms for accurately classifying cervical cancer tumors and normal uterine tissue with high recognition capabilities. This approach holds significant promise for clinical diagnostics.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"23 ","pages":"15330338241298554"},"PeriodicalIF":2.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11562001/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142628744","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 \"Integrating Therapeutic Ultrasound With Nanosized Drug Delivery Systems in the Battle Against Cancer\".","authors":"","doi":"10.1177/15330338231223384","DOIUrl":"10.1177/15330338231223384","url":null,"abstract":"","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"23 ","pages":"15330338231223384"},"PeriodicalIF":2.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10860455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139724032","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":"Decanoylcarnitine Inhibits Triple-Negative Breast Cancer Progression via Mmp9 in an Intermittent Fasting Obesity Mouse.","authors":"Yifan Tang, Shuai Chen, Saijun Wang, Ke Xu, Kun Zhang, Dongmei Wang, Ninghan Feng","doi":"10.1177/15330338241233443","DOIUrl":"10.1177/15330338241233443","url":null,"abstract":"<p><p><b>Purpose:</b> Treatment of triple-negative breast cancer (TNBC) remains challenging. Intermittent fasting (IF) has emerged as a promising approach to improve metabolic health of various metabolic disorders. Clinical studies indicate IF is essential for TNBC progression. However, the molecular mechanisms underlying metabolic remodeling in regulating IF and TNBC progression are still unclear. <b>Methods:</b> In this study, we utilized a robust mouse model of TNBC and exposed subjects to a high-fat diet (HFD) with IF to explore its impact on the metabolic reprogramming linked to cancer progression. To identify crucial serum metabolites and signaling events, we utilized targeted metabolomics and RNA sequencing (RNA-seq). Furthermore, we conducted immunoblotting, real-time quantitative polymerase chain reaction (RT-qPCR), cell migration assays, lentivirus-mediated Mmp9 overexpression, and Mmp9 inhibitor experiments to elucidate the role of decanoylcarnitine/Mmp9 in TNBC cell migration. <b>Results:</b> Our observations indicate that IF exerts notable inhibitory effects on both the proliferation and cancer metastasis. Utilizing targeted metabolomics and RNA-seq, we initially identified pivotal serum metabolites and signaling events in the progression of TNBC. Among the 349 serum metabolites identified, decanoylcarnitine was picked out to inhibit TNBC cell proliferation and migration. RNA-seq analysis of TNBC cells treated with decanoylcarnitine revealed its suppressive effects on extracellular matrix-related protein components, with a notable reduction observed in Mmp9. Further investigations confirmed that decanoylcarnitine could inhibit Mmp9 expression in TNBC cells, primary tumors, lung, and liver metastasis tissues. Mmp9 overexpression abolished the inhibitory effect of decanoylcarnitine on cell migration. <b>Conclusion:</b> This study pioneers the exploration of IF intervention and the role of decanoylcarnitine/Mmp9 in the progression of TNBC in obese mice, enhancing our comprehension of the potential roles of various dietary patterns in the process of cancer treatment.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"23 ","pages":"15330338241233443"},"PeriodicalIF":2.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10898300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139973564","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":"HCC-Check: A Novel Diagnostic Tool for Early Detection of Hepatocellular Carcinoma Based on Cytokeratin-1 and Epithelial Membrane Antigen: A Cross-Sectional Study.","authors":"Kareem A Attallah, Mohamed S Albannan, Khaled Farid, Sherine M Rizk, Nevine Fathy","doi":"10.1177/15330338241234790","DOIUrl":"10.1177/15330338241234790","url":null,"abstract":"<p><p><b>Background:</b> Hepatocellular carcinoma is frequently diagnosed in advanced stages, leading to a poorer prognosis. Therefore, early diagnosis and identification of biomarkers may significantly improve outcomes. <b>Methods:</b> This cross-sectional study enrolled 486 participants distributed among 3 groups: F1 to F3 = 184, F4 = 183, and hepatocellular carcinoma = 119. Liver fibrosis staging was performed using FibroScan, while imaging features were used for hepatocellular carcinoma detection. Epithelial membrane antigen and cytokeratin-1 levels in serum were quantified through Western blot and ELISA, respectively. <b>Results:</b> Patients diagnosed with hepatocellular carcinoma exhibited significantly elevated levels of epithelial membrane antigen and cytokeratin-1 compared to non-hepatocellular carcinoma patients, with a highly significant statistical difference (<i>P </i>< .0001). Epithelial membrane antigen demonstrated diagnostic performance with an area under the curve of 0.75, a sensitivity of 69.0%, and a specificity of 68.5%. Cytokeratin-1 for the identification of hepatocellular carcinoma showed a sensitivity of 79.0% and a specificity of 81.4%, resulting in an area under the curve of 0.87. The developed HCC-Check, which incorporates epithelial membrane antigen, cytokeratin-1, albumin, and alpha-fetoprotein, displayed a higher area under the curve of 0.95 to identify hepatocellular carcinoma, with a sensitivity of 89.8% and a specificity of 83.9%. Notably, HCC-Check values exceeding 2.57 substantially increased the likelihood of hepatocellular carcinoma, with an estimated odds ratio of 50.65, indicating a higher susceptibility to hepatocellular carcinoma development than those with lower values. The HCC-Check diagnostic test exhibited high precision in identifying patients with hepatocellular carcinoma, particularly those with small tumor sizes (<5 cm) and a single nodule, as reflected in area under the curve values of 0.92 and 0.85, respectively. HCC-Check was then applied to the validation study to test its accuracy and reproducibility, showing superior area under the curves for identifying different stages of hepatocellular carcinoma. These outcomes underscore the effectiveness of the test in the early detection of hepatocellular carcinoma. <b>Conclusion:</b> The HCC-Check test presents a highly accurate diagnostic method for detecting hepatocellular carcinoma in its early stages.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"23 ","pages":"15330338241234790"},"PeriodicalIF":2.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10913511/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140022589","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":"The Value of Topological Radiomics Analysis in Predicting Malignant Risk of Pulmonary Ground-Glass Nodules: A Multi-Center Study.","authors":"Miaoyu Wang, Yuanhui Wei, Minghui Zhu, Hang Yu, Chaomin Guo, Zhigong Chen, Wenjia Shi, Jiabo Ren, Wei Zhao, Zhen Yang, Liang-An Chen","doi":"10.1177/15330338241287089","DOIUrl":"10.1177/15330338241287089","url":null,"abstract":"<p><strong>Background: </strong>Early detection and accurate differentiation of malignant ground-glass nodules (GGNs) in lung CT scans are crucial for the effective treatment of lung adenocarcinoma. However, existing imaging diagnostic methods often struggle to distinguish between benign and malignant GGNs in the early stages. This study aims to predict the malignancy risk of GGNs observed in lung CT scans by applying two radiomics methods: topological data analysis and texture analysis.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 3223 patients from two centers between January 2018 and June2023. The dataset was divided into training, testing, and validation sets to ensure robust model development and validation. We developed topological features applied to GGNs using radiomics analysis based on homology. This innovative approach emphasizes the integration of topological information, capturing complex geometric and spatial relationships within GGNs. By combining machine learning and deep learning algorithms, we established a predictive model that integrates clinical parameters, previous radiomics features, and topological radiomics features.</p><p><strong>Results: </strong>Incorporating topological radiomics into our model significantly enhanced the ability to distinguish between benign and malignant GGNs. The topological radiomics model achieved areas under the curve (AUC) of 0.85 and 0.862 in two independent validation sets, outperforming previous radiomics models. Furthermore, this model demonstrated higher sensitivity compared to models based solely on clinical parameters, with sensitivities of 80.7% in validation set 1 and 82.3% in validation set 2. The most comprehensive model, which combined clinical parameters, previous radiomics features, and topological radiomics features, achieved the highest AUC value of 0.879 across all datasets.</p><p><strong>Conclusion: </strong>This study validates the potential of topological radiomics in improving the predictive performance for distinguishing between benign and malignant GGNs. By integrating topological features with previous radiomics and clinical parameters, our comprehensive model provides a more accurate and reliable basis for developing treatment strategies for patients with GGNs.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"23 ","pages":"15330338241287089"},"PeriodicalIF":2.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452904/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142372935","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":"Assessing the Impact of a 1.5 T Transverse Magnetic Field in Radiotherapy for Esophageal Cancer Patients.","authors":"Yukun Li, Baosheng Li, Jian Zhu, Yong Yin, Zhenjiang Li","doi":"10.1177/15330338241227291","DOIUrl":"10.1177/15330338241227291","url":null,"abstract":"<p><p><b>Purpose:</b> Magnetic resonance (MR)-guided radiotherapy enables visualization of static anatomy, capturing tumor motion, and extracting quantitative image features for treatment verification and outcome monitoring. However, magnetic fields in online MR imaging (MRI) require efforts to ensure accurate dose measurements. This study aimed to assess the dosimetric impact of a 1.5 T magnetic field in esophageal cancer radiotherapy using MR-linac, exploring treatment adaptation potential and personalized medicine benefits. <b>Methods:</b> A prospective cohort study enrolled 100 esophageal squamous cell carcinoma patients undergoing 4DCT and 3DCT scans before radiotherapy. The heart was contoured on 3DCT, 4DCT end expiration (EE), and 4DCT end inhalation (EI) images by the same radiation oncologist. Reference RT plans were designed on 3DCT, with adjustments for different phases generating 5 plan types per patient. Variations in dose-volume parameters for organs at risk and the target area among different plans were compared using Monaco 5.40.04. <b>Results:</b> Slight dose distortions at air-tissue interfaces were observed in the magnetic field's presence. Dose at air-tissue interfaces (chest wall and heart wall) was slightly higher in some patients (3.0% tissue increased by 4.3 Gy on average) compared to nonmagnetic conditions. Average clinical target volume coverage V100 dropped from 99% to 95% compared to reference plans (planEI and planEE). Dose-volume histogram variation between the original plan and reference plans was within 2.3%. Superior-inferior (SI) direction displacement was significantly larger than lateral and anterior-posterior directions (<i>P</i> < .05). <b>Conclusion:</b> Significant SI direction shift in lower esophageal cancerous regions during RT indicates the magnetic field's dosimetric impact, including the electron return effect at tissue-air boundaries. Changes in OAR dose could serve as valuable indicators of organ impairment and target dose alterations, especially for cardiac tissue when using the 1.5 T linac method. Reoptimizing the plan with the magnetic field enhances the feasibility of achieving a clinically acceptable treatment plan for esophageal cancer patients.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"23 ","pages":"15330338241227291"},"PeriodicalIF":2.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10807384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139521825","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":"Recent Advances and Challenges in Stereotactic Body Radiotherapy.","authors":"Atsuto Katano, Masanari Minamitani, Shingo Ohira, Hideomi Yamashita","doi":"10.1177/15330338241229363","DOIUrl":"10.1177/15330338241229363","url":null,"abstract":"","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"23 ","pages":"15330338241229363"},"PeriodicalIF":2.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10851756/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139698371","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":"Correlation Between ImageJ and Conventional Manual Scoring Methods for Programmed Death-Ligand 1 Immuno-Histochemically Stained Sections.","authors":"Rand Suleiman Al Taher, Manal A Abbas, Khalid Halahleh, Maher A Sughayer","doi":"10.1177/15330338241242635","DOIUrl":"10.1177/15330338241242635","url":null,"abstract":"<p><p><b>Background:</b> One of the most frequently used methods for quantifying PD-L1 (programmed cell death-ligand 1) expression in tumor tissue is IHC (immunohistochemistry). This may predict the patient's response to anti-PD1/PD-L1 therapy in cancer. <b>Methods:</b> ImageJ software was used to score IHC-stained sections for PD-L1 and compare the results with the conventional manual method. <b>Results:</b> In diffuse large B cell lymphoma, no significant difference between the scores obtained by the conventional method and ImageJ scores obtained using the option \"RGB\" or \"Brightness/Contrast.\" On the other hand, a significant difference was found between the conventional and HSB scoring methods. ImageJ faced some challenges in analyzing head and neck squamous cell carcinoma tissues because of tissue heterogenicity. A significant difference was found between the conventional and ImageJ scores using HSB or RGB but not with the \"Brightness/Contrast\" option. Scores obtained by ImageJ analysis after taking images using 20 × objective lens gave significantly higher readings compared to 40 × magnification. A significant difference between camera-captured images' scores and scanner whole slide images' scores was observed. <b>Conclusion:</b> ImageJ can be used to score homogeneous tissues. In the case of highly heterogeneous tissues, it is advised to use the conventional method rather than ImageJ scoring.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"23 ","pages":"15330338241242635"},"PeriodicalIF":2.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10989033/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140336871","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":"USP39 Promotes the Viability and Migration of Head and Neck Squamous Cell Carcinoma Cell by Regulating STAT1.","authors":"Yu Hu, Yang Wang, Wenrui Hu, Chenrui Hu, Bin Wang, Congli Liu, Anqi Deng, Bing Shen, Kaile Wu, Yehai Liu","doi":"10.1177/15330338241250298","DOIUrl":"10.1177/15330338241250298","url":null,"abstract":"<p><p><b>Objective:</b> Ubiquitin-specific peptidase 39 (USP39) plays a carcinogenic role in many cancers, but little research has been conducted examining whether it is involved in head and neck squamous cell carcinoma (HNSCC). Therefore, this study explored the functional role of USP39 in HNSCC. <b>Method:</b> Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to identify differentially expressed proteins (DEPs) between the HNSCC tumor and adjacent healthy tissues. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to assess the functional enrichment of DEPs. Immunohistochemistry was used to detect protein expression. The viability and migration of two HNSCC cell lines, namely CAL27 and SCC25, were detected using the cell counting kit-8 assay and a wound healing assay, respectively. Quantitative real-time PCR was used to detect the expression level of signal transducer and activator of transcription 1 (<i>STAT1</i>) mRNA. <b>Results:</b> LC-MS/MS results identified 590 DEPs between HNSCC and adjacent tissues collected from 4 patients. Through GO and KEGG pathway analyses, 34 different proteins were found to be enriched in the spliceosome pathway. The expression levels of USP39 and STAT1 were significantly higher in HNSCC tumor tissue than in adjacent healthy tissue as assessed by LC-MS/MS analysis, and the increased expression of USP39 and STAT1 protein was confirmed by immunohistochemistry in clinical samples collected from 7 additional patients with HNSCC. Knockdown of USP39 or STAT1 inhibited the viability and migration of CAL27 and SCC25 cells. In addition, USP39 knockdown inhibited the expression of <i>STAT1</i> mRNA in these cells. <b>Conclusion:</b> Our findings indicated that USP39 knockdown may inhibit HNSCC viability and migration by suppressing STAT1 expression. The results of this study suggest that USP39 may be a potential new target for HNSCC clinical therapy or a new biomarker for HNSCC.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"23 ","pages":"15330338241250298"},"PeriodicalIF":2.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11072062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140851333","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":"Identification of Immune Infiltrating Cell-Related Biomarkers in Early Gastric Cancer Progression.","authors":"Chenguang Ji, Hongmei Cai, Xiaoxu Jin, Kaige Yin, Dongqiang Zhao, Zhijie Feng, Li Liu","doi":"10.1177/15330338241262724","DOIUrl":"10.1177/15330338241262724","url":null,"abstract":"<p><strong>Objectives: </strong>Gastric cancer (GC) is one of the most prevalent malignancies worldwide, and early detection is crucial for improving patient survival rates. We aimed to identify immune infiltrating cell-related biomarkers in early gastric cancer (EGC) progression.</p><p><strong>Methods: </strong>The GSE55696 and GSE130823 datasets with low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and EGC samples were downloaded from the Gene Expression Omnibus database to perform an observational study. Immune infiltration analysis was performed by single sample gene set enrichment analysis and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data. Weighted gene co-expression network analysis was used to explore the co-expression modules and genes, and further enrichment analysis was performed on these genes. A protein-protein interaction (PPI) network of these genes was constructed to identify biomarkers associated with EGC progression. Screened hub genes were validated by the rank sum test and reverse transcription quantitative polymerase chain reaction.</p><p><strong>Results: </strong>Immune scores were significantly elevated in EGC samples compared to LGIN and HGIN samples. The green-yellow module exhibited the strongest correlation with both immune score and disease progression. The 87 genes within this module were associated with the chemokine signaling pathways, the PI3K-Akt signaling pathways, leukocyte transendothelial migration, and Ras signaling pathways. Through PPI network analysis, the hub genes identified were protein tyrosine phosphatase receptor-type C (PTPRC), pleckstrin, CD53, CD48, lymphocyte cytosolic protein 1 (LCP1), hematopoietic cell-specific Lyn substrate 1, IKAROS Family Zinc Finger 1, Bruton tyrosine kinase, and Vav guanine nucleotide exchange factor 1. Notably, CD48, LCP1, and PTPRC showed high expression levels in EGC samples, with the remaining hub genes demonstrating a similar expression trend.</p><p><strong>Conclusion: </strong>This study identified 9 immune cell-related biomarkers that may be actively involved in the progression of EGC and serve as potential targets for GC diagnosis and treatment.</p>","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"23 ","pages":"15330338241262724"},"PeriodicalIF":2.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141301654","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}