Pub Date : 2024-04-18DOI: 10.1177/15330338241239188
Ali Tavakoli Pirzaman, Ali Alishah, Bahareh Babajani, Pouyan Ebrahimi, Seyyed Ali Sheikhi, Farhad Moosaei, Amirhossein Salarfar, Shahrbanoo Doostmohamadian, Sohrab Kazemi
Globally, hepatic cancer ranks fourth in terms of cancer-related mortality and is the sixth most frequent kind of cancer. Around 80% of liver cancers are hepatocellular carcinomas (HCC), which are the leading cause of cancer death. It is well known that HCC may develop resistance to the available chemotherapy treatments very fast. One of the biggest obstacles in providing cancer patients with appropriate care is drug resistance. According to reports, more than 90% of cancer-specific fatalities are caused by treatment resistance. By binding to the 3'-untranslated region of target messenger RNAs (mRNAs), microRNAs (miRNAs), a group of noncoding RNAs which are around 17 to 25 nucleotides long, regulate target gene expression. Moreover, they play role in the control of signaling pathways, cell proliferation, and cell death. As a result, miRNAs play an important role in the microenvironment of HCC by changing immune phenotypes, hypoxic conditions, and acidification, as well as angiogenesis and extracellular matrix components. Moreover, changes in miRNA levels in HCC can effectively resist cancer cells to chemotherapy by affecting various cellular processes such as autophagy, apoptosis, and membrane transporter activity. In the current work, we narratively reviewed the role of miRNAs in HCC, with a special focus on tumor microenvironment and drug resistance.
{"title":"The Role of microRNAs in Hepatocellular Cancer: A Narrative Review Focused on Tumor Microenvironment and Drug Resistance","authors":"Ali Tavakoli Pirzaman, Ali Alishah, Bahareh Babajani, Pouyan Ebrahimi, Seyyed Ali Sheikhi, Farhad Moosaei, Amirhossein Salarfar, Shahrbanoo Doostmohamadian, Sohrab Kazemi","doi":"10.1177/15330338241239188","DOIUrl":"https://doi.org/10.1177/15330338241239188","url":null,"abstract":"Globally, hepatic cancer ranks fourth in terms of cancer-related mortality and is the sixth most frequent kind of cancer. Around 80% of liver cancers are hepatocellular carcinomas (HCC), which are the leading cause of cancer death. It is well known that HCC may develop resistance to the available chemotherapy treatments very fast. One of the biggest obstacles in providing cancer patients with appropriate care is drug resistance. According to reports, more than 90% of cancer-specific fatalities are caused by treatment resistance. By binding to the 3'-untranslated region of target messenger RNAs (mRNAs), microRNAs (miRNAs), a group of noncoding RNAs which are around 17 to 25 nucleotides long, regulate target gene expression. Moreover, they play role in the control of signaling pathways, cell proliferation, and cell death. As a result, miRNAs play an important role in the microenvironment of HCC by changing immune phenotypes, hypoxic conditions, and acidification, as well as angiogenesis and extracellular matrix components. Moreover, changes in miRNA levels in HCC can effectively resist cancer cells to chemotherapy by affecting various cellular processes such as autophagy, apoptosis, and membrane transporter activity. In the current work, we narratively reviewed the role of miRNAs in HCC, with a special focus on tumor microenvironment and drug resistance.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"12 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627879","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}
Pub Date : 2024-04-17DOI: 10.1177/15330338241246636
Zhiyu Zhang, Can Hu, Yuxin Lin, Ouyang Song, Dongkui Gong, Xuefeng Zhang, Nan Wang
ObjectiveThis study intends to examine the anticipatory power of clinical and radiological parameters in detecting clinically significant prostate cancer in patients demonstrating Prostate Imaging Reporting and Data System 3 lesions.MethodsThis was a retrospective study. The study included participation from 453 patients at the First Affiliated Hospital of Soochow University, sampled between September 2017 through August 2022. Each patient underwent a routine 12-core prostate biopsy followed by a 2 to 5 core fusion-targeted biopsy. We utilized both univariate and multivariate logistic regression analyses to identify the parameters that have a correlation with clinically significant prostate cancer. The predictive ability of these parameters was assessed using the receiver operating characteristic curve, leading to the creation of a nomogram.ResultsClinically significant prostate cancer was detected in 68 out of 453 patients with Prostate Imaging Reporting and Data System 3 lesions (15.01%). Among Prostate Imaging Reporting and Data System 3a and 3b patients, 4.78% (3.09% of the total) and 33.75% (11.92% of the total), respectively, had clinically significant prostate cancer. Systematic biopsy improved prostate cancer and clinically significant prostate cancer detection rates by 7.72% and 3.09%, respectively, compared to targeted biopsy. Without systematic biopsy, there would be an undetected rate of 15% for prostate cancer and 8.13% for clinically significant prostate cancer in Prostate Imaging Reporting and Data System 3b patients. Several clinical parameters, including age, prostate-specific antigen density, lesion volume, apparent diffusion coefficient, and digital rectal examination, were statistically significant in the logistic regression analysis for clinically significant prostate cancer. The individual diagnostic accuracies of these parameters for clinically significant prostate cancer were 0.648, 0.645, 0.75, 0.763, and 0.7, respectively, but their combined accuracy improved to 0.866. A well-fit nomogram based on the identified risk factors was constructed (χ2 = 10.254, P = .248).ConclusionThe combination of age, prostate-specific antigen density, lesion volume, apparent diffusion coefficient, and digital rectal examination presented a higher diagnostic value for clinically significant prostate cancer than any single parameter in patients with Prostate Imaging Reporting and Data System 3 lesions. Systematic biopsy proved crucial for biopsy-naive patients with Prostate Imaging Reporting and Data System 3 lesions and should not be omitted.
{"title":"Clinical and Radiological Factors for Predicting Clinically Significant Prostate Cancer in Biopsy-Naive Patients With PI-RADS 3 Lesions","authors":"Zhiyu Zhang, Can Hu, Yuxin Lin, Ouyang Song, Dongkui Gong, Xuefeng Zhang, Nan Wang","doi":"10.1177/15330338241246636","DOIUrl":"https://doi.org/10.1177/15330338241246636","url":null,"abstract":"ObjectiveThis study intends to examine the anticipatory power of clinical and radiological parameters in detecting clinically significant prostate cancer in patients demonstrating Prostate Imaging Reporting and Data System 3 lesions.MethodsThis was a retrospective study. The study included participation from 453 patients at the First Affiliated Hospital of Soochow University, sampled between September 2017 through August 2022. Each patient underwent a routine 12-core prostate biopsy followed by a 2 to 5 core fusion-targeted biopsy. We utilized both univariate and multivariate logistic regression analyses to identify the parameters that have a correlation with clinically significant prostate cancer. The predictive ability of these parameters was assessed using the receiver operating characteristic curve, leading to the creation of a nomogram.ResultsClinically significant prostate cancer was detected in 68 out of 453 patients with Prostate Imaging Reporting and Data System 3 lesions (15.01%). Among Prostate Imaging Reporting and Data System 3a and 3b patients, 4.78% (3.09% of the total) and 33.75% (11.92% of the total), respectively, had clinically significant prostate cancer. Systematic biopsy improved prostate cancer and clinically significant prostate cancer detection rates by 7.72% and 3.09%, respectively, compared to targeted biopsy. Without systematic biopsy, there would be an undetected rate of 15% for prostate cancer and 8.13% for clinically significant prostate cancer in Prostate Imaging Reporting and Data System 3b patients. Several clinical parameters, including age, prostate-specific antigen density, lesion volume, apparent diffusion coefficient, and digital rectal examination, were statistically significant in the logistic regression analysis for clinically significant prostate cancer. The individual diagnostic accuracies of these parameters for clinically significant prostate cancer were 0.648, 0.645, 0.75, 0.763, and 0.7, respectively, but their combined accuracy improved to 0.866. A well-fit nomogram based on the identified risk factors was constructed (χ<jats:sup>2 </jats:sup>= 10.254, P = .248).ConclusionThe combination of age, prostate-specific antigen density, lesion volume, apparent diffusion coefficient, and digital rectal examination presented a higher diagnostic value for clinically significant prostate cancer than any single parameter in patients with Prostate Imaging Reporting and Data System 3 lesions. Systematic biopsy proved crucial for biopsy-naive patients with Prostate Imaging Reporting and Data System 3 lesions and should not be omitted.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"29 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140617719","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}
Pub Date : 2024-04-13DOI: 10.1177/15330338241246651
Qi Yuan, Chunhua Xu, Wei Wang, Qian Zhang
ObjectiveTo investigate the predictive value of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) for the efficacy and prognosis of programmed cell death-1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors in driver-gene-negative advanced non-small-cell lung cancer (NSCLC).MethodsA retrospective analysis of 107 advanced NSCLC patients without gene mutations who received PD-1/PD-L1 inhibitors in our hospital from January 2020 to June 2022 was performed. NLR and PLR were collected before PD-1/PD-L1 inhibitors, the optimal cut-off values of NLR and PLR were determined according to the receiver operating characteristic (ROC) curve, and the effects of NLR and PLR on the efficacy of PD-1/PD-L1 inhibitors in advanced NSCLC patients were analyzed.ResultsA total of 107 patients were included in this study. Receiver operating characteristic analysis showed that the optimal cut-off values of NLR and PLR were 3.825, 179, respectively. Kaplan–Meier curve showed that low baseline levels NLR and PLR were associated with an improvement in both progression-free survival (PFS) ( P < .001, < .001, respectively) and overall survival (OS) ( P = .009, .006, respectively). In first-line treatment and non-first-line treatment, low baseline levels NLR and PLR were associated with an improvement in PFS. In multivariate analysis, low baseline NLR and PLR showed a strong association with both better PFS ( P = .011, .027, respectively) and longer OS ( P = .042, .039, respectively).ConclusionLow baseline NLR and PLR levels are significantly associated with better response in advanced NSCLC patients treated with PD-1/PD-L1 inhibitors, which may be indicators to predict the efficacy of immunotherapy in advanced NSCLC with driver-gene-negative.
{"title":"Predictive Value of NLR and PLR in Driver-Gene-Negative Advanced Non-Small Cell Lung Cancer Treated with PD-1/PD-L1 Inhibitors: A Single Institutional Cohort Study","authors":"Qi Yuan, Chunhua Xu, Wei Wang, Qian Zhang","doi":"10.1177/15330338241246651","DOIUrl":"https://doi.org/10.1177/15330338241246651","url":null,"abstract":"ObjectiveTo investigate the predictive value of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) for the efficacy and prognosis of programmed cell death-1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors in driver-gene-negative advanced non-small-cell lung cancer (NSCLC).MethodsA retrospective analysis of 107 advanced NSCLC patients without gene mutations who received PD-1/PD-L1 inhibitors in our hospital from January 2020 to June 2022 was performed. NLR and PLR were collected before PD-1/PD-L1 inhibitors, the optimal cut-off values of NLR and PLR were determined according to the receiver operating characteristic (ROC) curve, and the effects of NLR and PLR on the efficacy of PD-1/PD-L1 inhibitors in advanced NSCLC patients were analyzed.ResultsA total of 107 patients were included in this study. Receiver operating characteristic analysis showed that the optimal cut-off values of NLR and PLR were 3.825, 179, respectively. Kaplan–Meier curve showed that low baseline levels NLR and PLR were associated with an improvement in both progression-free survival (PFS) ( P < .001, < .001, respectively) and overall survival (OS) ( P = .009, .006, respectively). In first-line treatment and non-first-line treatment, low baseline levels NLR and PLR were associated with an improvement in PFS. In multivariate analysis, low baseline NLR and PLR showed a strong association with both better PFS ( P = .011, .027, respectively) and longer OS ( P = .042, .039, respectively).ConclusionLow baseline NLR and PLR levels are significantly associated with better response in advanced NSCLC patients treated with PD-1/PD-L1 inhibitors, which may be indicators to predict the efficacy of immunotherapy in advanced NSCLC with driver-gene-negative.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"55 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583506","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}
Pub Date : 2024-04-13DOI: 10.1177/15330338241245924
Ling Yang, Ying Cai, Yunjia Wang, Yue Huang, Chi Zhang, Hu Ma, Jian-Guo Zhou
BackgroundUterine sarcoma (US) is a highly malignant cancer with poor prognosis and high mortality in women. In this study, we evaluated the expression of human fibroblast growth factor 23 (FGF23) in different US subtypes and the relationship between survival and clinicopathological characteristics.MethodsWe conducted a comparative analysis of FGF23 gene expression in different pathological types of US. Utilizing a cohort from The Cancer Genome Atlas of 57 patients, a 50-patient microarray dataset (GSE119043) from the Gene Expression Omnibus and a Suining cohort of 44 patients, we analyzed gene expression profiles and corresponding clinicopathological information. Immunohistochemistry was used to examine the expression level of FGF23 in four US subtypes. Survival analysis was used to assess the relationship between FGF23 expression and prognosis in US patients.ResultsCompared with uterine normal smooth muscle and uterine leiomyoma, FGF23 expression was significantly upregulated in US and was differentially expressed in four US subtypes. Uterine carcinosarcoma exhibited the highest expression of FGF23 among the subtypes. Survival analysis revealed no correlation between FGF23 expression and either overall survival or progression-free survival in US ( P > 0.05). Similar results were obtained from the validation cohorts. Univariate and multivariate analyses showed no significant correlation between FGF23 expression and the US prognosis. Tumor stage, CA125, and tumor recurrence were independent prognostic factors for survival of US patients.ConclusionFGF23 was highly expressed in US and was promising as a novel potential biomarker for the diagnosis and prognosis of US.
背景子宫肉瘤（US）是一种高度恶性的癌症，女性患者预后差、死亡率高。在这项研究中，我们评估了人成纤维细胞生长因子 23（FGF23）在不同 US 亚型中的表达以及生存率与临床病理特征之间的关系。我们利用癌症基因组图谱（The Cancer Genome Atlas）中的57例患者队列、基因表达总库（Gene Expression Omnibus）中的50例患者微阵列数据集（GSE119043）和44例患者的遂宁队列，分析了基因表达谱和相应的临床病理学信息。免疫组化技术用于检测 FGF23 在四种 US 亚型中的表达水平。结果与子宫正常平滑肌和子宫白肌瘤相比，FGF23在US中的表达明显上调，并且在四种US亚型中存在差异表达。在所有亚型中，子宫癌肉瘤的FGF23表达量最高。生存期分析显示，FGF23的表达与US的总生存期或无进展生存期均无相关性（P >0.05）。验证队列也得出了类似的结果。单变量和多变量分析表明，FGF23的表达与美国的预后无明显相关性。结论FGF23在US中高表达，有望成为US诊断和预后的潜在生物标记物。
{"title":"Fibroblast Growth Factor 23 is a Potential Prognostic Biomarker in Uterine Sarcoma","authors":"Ling Yang, Ying Cai, Yunjia Wang, Yue Huang, Chi Zhang, Hu Ma, Jian-Guo Zhou","doi":"10.1177/15330338241245924","DOIUrl":"https://doi.org/10.1177/15330338241245924","url":null,"abstract":"BackgroundUterine sarcoma (US) is a highly malignant cancer with poor prognosis and high mortality in women. In this study, we evaluated the expression of human fibroblast growth factor 23 (FGF23) in different US subtypes and the relationship between survival and clinicopathological characteristics.MethodsWe conducted a comparative analysis of FGF23 gene expression in different pathological types of US. Utilizing a cohort from The Cancer Genome Atlas of 57 patients, a 50-patient microarray dataset (GSE119043) from the Gene Expression Omnibus and a Suining cohort of 44 patients, we analyzed gene expression profiles and corresponding clinicopathological information. Immunohistochemistry was used to examine the expression level of FGF23 in four US subtypes. Survival analysis was used to assess the relationship between FGF23 expression and prognosis in US patients.ResultsCompared with uterine normal smooth muscle and uterine leiomyoma, FGF23 expression was significantly upregulated in US and was differentially expressed in four US subtypes. Uterine carcinosarcoma exhibited the highest expression of FGF23 among the subtypes. Survival analysis revealed no correlation between FGF23 expression and either overall survival or progression-free survival in US ( P > 0.05). Similar results were obtained from the validation cohorts. Univariate and multivariate analyses showed no significant correlation between FGF23 expression and the US prognosis. Tumor stage, CA125, and tumor recurrence were independent prognostic factors for survival of US patients.ConclusionFGF23 was highly expressed in US and was promising as a novel potential biomarker for the diagnosis and prognosis of US.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"1 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583821","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}
Pub Date : 2024-04-13DOI: 10.1177/15330338241240683
Xiuqin Zhang, Yan Qin, Xu Chen, Mengrui Xiong, Song Shu
Objective: Human endogenous retrovirus-H long terminal repeat associating 2 (HHLA2) is a new immune checkpoint in the B7 family, and the value of HHLA2 in small cell lung cancer (SCLC) is unknown. Methods: We retrospectively detected HHLA2 expression by immunohistochemistry in SCLC patients. Moreover, plasma biomarkers of SCLC were detected retrospectively. Results: Seventy-four percent of SCLC patients exhibited HHLA2 expression. HHLA2 staining was localised within the nucleus of SCLC cells, while no staining was detected in normal lung tissue specimens. The correlation between HHLA2 expression and clinical factors was also analysed. Limited stage (LS) SCLC was more common than extensive stage (ES) SCLC among patients with HHLA2 staining. SCLC patients without metastasis had higher HHLA2 expression than SCLC patients with metastasis. HHLA2 expression was more frequently detected in the group with a tumour size greater than 5 cm than in the group with a tumour size less than 5 cm. The proportion of patients with HHLA2-positive staining was greater in the stage III and IV SCLC groups than in the stage I and II SCLC groups. A high proportion of SCLC patients with HHLA2-positive staining had a survival time <2 years. Neuron-specific enolase (NSE), CEA and Ki-67 levels were measured. The NSE level in the HHLA2-positive group was significantly greater than that in the HHLA2-negative group. The CEA and Ki-67 levels did not significantly differ between the HHLA2-positive and HHLA2-negative patients, nor were age, sex, smoking status, nodal metastasis status, Karnofsky Performance Scale (KPS) score, or Ki-67 expression score. HHLA2-positive SCLC patients had higher tumour stages and shorter 2-year survival times than HHLA2-negative patients did. Conclusion: The new immune molecule HHLA2 may be an ideal clinical biomarker for predicting SCLC progression and could serve as a new immunotherapy target in SCLC.
{"title":"Clinical Value of Human Endogenous Retrovirus-H Long Terminal Repeat Associating 2 (HHLA2) in Small Cell Lung Cancer","authors":"Xiuqin Zhang, Yan Qin, Xu Chen, Mengrui Xiong, Song Shu","doi":"10.1177/15330338241240683","DOIUrl":"https://doi.org/10.1177/15330338241240683","url":null,"abstract":"Objective: Human endogenous retrovirus-H long terminal repeat associating 2 (HHLA2) is a new immune checkpoint in the B7 family, and the value of HHLA2 in small cell lung cancer (SCLC) is unknown. Methods: We retrospectively detected HHLA2 expression by immunohistochemistry in SCLC patients. Moreover, plasma biomarkers of SCLC were detected retrospectively. Results: Seventy-four percent of SCLC patients exhibited HHLA2 expression. HHLA2 staining was localised within the nucleus of SCLC cells, while no staining was detected in normal lung tissue specimens. The correlation between HHLA2 expression and clinical factors was also analysed. Limited stage (LS) SCLC was more common than extensive stage (ES) SCLC among patients with HHLA2 staining. SCLC patients without metastasis had higher HHLA2 expression than SCLC patients with metastasis. HHLA2 expression was more frequently detected in the group with a tumour size greater than 5 cm than in the group with a tumour size less than 5 cm. The proportion of patients with HHLA2-positive staining was greater in the stage III and IV SCLC groups than in the stage I and II SCLC groups. A high proportion of SCLC patients with HHLA2-positive staining had a survival time <2 years. Neuron-specific enolase (NSE), CEA and Ki-67 levels were measured. The NSE level in the HHLA2-positive group was significantly greater than that in the HHLA2-negative group. The CEA and Ki-67 levels did not significantly differ between the HHLA2-positive and HHLA2-negative patients, nor were age, sex, smoking status, nodal metastasis status, Karnofsky Performance Scale (KPS) score, or Ki-67 expression score. HHLA2-positive SCLC patients had higher tumour stages and shorter 2-year survival times than HHLA2-negative patients did. Conclusion: The new immune molecule HHLA2 may be an ideal clinical biomarker for predicting SCLC progression and could serve as a new immunotherapy target in SCLC.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"94 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583501","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}
Pub Date : 2024-04-13DOI: 10.1177/15330338241239139
Qiang Liu, Jianpeng Zhu, Zhicheng Huang, Xiaofeng Zhang, Jianfeng Yang
BackgroundCuproptosis is a novel type of mediated cell death strongly associated with the progression of several cancers and has been implicated as a potential therapeutic target. However, the role of cuproptosis in cholangiocarcinoma for prognostic prediction, subgroup classification, and therapeutic strategies remains largely unknown.MethodsA systematic analysis was conducted among 146 cuproptosis-related genes and clinical information based on independent mRNA and protein datasets to elucidate the potential mechanisms and prognostic prediction value of cuproptosis-related genes. A 10-cuproptosis-related gene prediction model was constructed, and its effects on cholangiocarcinoma prognosis were significantly connected to poor patient survival. Additionally, the expression patterns of our model included genes that were validated with several cholangiocarcinoma cancer cell lines and a normal biliary epithelial cell line.ResultsFirst, a 10-cuproptosis-related gene signature ( ADAM9, ADAM17, ALB, AQP1, CDK1, MT2A, PAM, SOD3, STEAP3, and TMPRSS6) displayed excellent predictive performance for the overall survival of cholangiocarcinoma. The low-cuproptosis group had a significantly better prognosis than the high-cuproptosis group with transcriptome and protein cohorts. Second, compared with the high-risk and low-risk groups, the 2 groups displayed distinct tumor microenvironments, reduced proportions of endothelial cells, and increased levels of cancer-associated fibroblasts based on CIBERSORTx and EPIC analyses. Third, patients’ sensitivities to chemotherapeutic drugs and immune checkpoints revealed distinctive differences between the 2 groups. Finally, in replicating the expression patterns of the 10 genes, these results were validated with quantitative real-time polymerase chain reaction results validating the abnormal expression pattern of the target genes in cholangiocarcinoma.ConclusionsCollectively, we established and verified an effective prognostic model that could separate cholangiocarcinoma patients into 2 heterogeneous cuproptosis subtypes based on the molecular or protein characteristics of 10 cuproptosis-related genes. These findings may provide potential benefits for unveiling molecular characteristics and defining subgroups could improve the early diagnosis and individualized treatment of cholangiocarcinoma patients.
{"title":"Identification of Novel Cuproptosis-Related Genes Mediating the Prognosis and Immune Microenvironment in Cholangiocarcinoma","authors":"Qiang Liu, Jianpeng Zhu, Zhicheng Huang, Xiaofeng Zhang, Jianfeng Yang","doi":"10.1177/15330338241239139","DOIUrl":"https://doi.org/10.1177/15330338241239139","url":null,"abstract":"BackgroundCuproptosis is a novel type of mediated cell death strongly associated with the progression of several cancers and has been implicated as a potential therapeutic target. However, the role of cuproptosis in cholangiocarcinoma for prognostic prediction, subgroup classification, and therapeutic strategies remains largely unknown.MethodsA systematic analysis was conducted among 146 cuproptosis-related genes and clinical information based on independent mRNA and protein datasets to elucidate the potential mechanisms and prognostic prediction value of cuproptosis-related genes. A 10-cuproptosis-related gene prediction model was constructed, and its effects on cholangiocarcinoma prognosis were significantly connected to poor patient survival. Additionally, the expression patterns of our model included genes that were validated with several cholangiocarcinoma cancer cell lines and a normal biliary epithelial cell line.ResultsFirst, a 10-cuproptosis-related gene signature ( ADAM9, ADAM17, ALB, AQP1, CDK1, MT2A, PAM, SOD3, STEAP3, and TMPRSS6) displayed excellent predictive performance for the overall survival of cholangiocarcinoma. The low-cuproptosis group had a significantly better prognosis than the high-cuproptosis group with transcriptome and protein cohorts. Second, compared with the high-risk and low-risk groups, the 2 groups displayed distinct tumor microenvironments, reduced proportions of endothelial cells, and increased levels of cancer-associated fibroblasts based on CIBERSORTx and EPIC analyses. Third, patients’ sensitivities to chemotherapeutic drugs and immune checkpoints revealed distinctive differences between the 2 groups. Finally, in replicating the expression patterns of the 10 genes, these results were validated with quantitative real-time polymerase chain reaction results validating the abnormal expression pattern of the target genes in cholangiocarcinoma.ConclusionsCollectively, we established and verified an effective prognostic model that could separate cholangiocarcinoma patients into 2 heterogeneous cuproptosis subtypes based on the molecular or protein characteristics of 10 cuproptosis-related genes. These findings may provide potential benefits for unveiling molecular characteristics and defining subgroups could improve the early diagnosis and individualized treatment of cholangiocarcinoma patients.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"31 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583516","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}
Pub Date : 2024-04-09DOI: 10.1177/15330338241234791
Sheng Zhou, Chujiao Hu, Shanshan Wei, Xiaofan Yan
IntroductionThe incidence of breast cancer has steadily risen over the years owing to changes in lifestyle and environment. Presently, breast cancer is one of the primary causes of cancer-related deaths among women, making it a crucial global public health concern. Thus, the creation of an automated diagnostic system for breast cancer bears great importance in the medical community.ObjectivesThis study analyses the Wisconsin breast cancer dataset and develops a machine learning algorithm for accurately classifying breast cancer as benign or malignant.MethodsOur research is a retrospective study, and the main purpose is to develop a high-precision classification algorithm for benign and malignant breast cancer. To achieve this, we first preprocessed the dataset using standard techniques such as feature scaling and handling missing values. We assessed the normality of the data distribution initially, after which we opted for Spearman correlation analysis to examine the relationship between the feature subset data and the labeled data, considering the normality test results. We subsequently employed the Wilcoxon rank sum test to investigate the dissimilarities in distribution among various breast cancer feature data. We constructed the feature subset based on statistical results and trained 7 machine learning algorithms, specifically the decision tree, stochastic gradient descent algorithm, random forest algorithm, support vector machine algorithm, logistics algorithm, and AdaBoost algorithm.ResultsThe results of the evaluation indicated that the AdaBoost-Logistic algorithm achieved an accuracy of 99.12%, outperforming the other 6 algorithms and previous techniques.ConclusionThe constructed AdaBoost-Logistic algorithm exhibits significant precision with the Wisconsin breast cancer dataset, achieving commendable classification performance for both benign and malignant breast cancer cases.
{"title":"Breast Cancer Prediction Based on Multiple Machine Learning Algorithms","authors":"Sheng Zhou, Chujiao Hu, Shanshan Wei, Xiaofan Yan","doi":"10.1177/15330338241234791","DOIUrl":"https://doi.org/10.1177/15330338241234791","url":null,"abstract":"IntroductionThe incidence of breast cancer has steadily risen over the years owing to changes in lifestyle and environment. Presently, breast cancer is one of the primary causes of cancer-related deaths among women, making it a crucial global public health concern. Thus, the creation of an automated diagnostic system for breast cancer bears great importance in the medical community.ObjectivesThis study analyses the Wisconsin breast cancer dataset and develops a machine learning algorithm for accurately classifying breast cancer as benign or malignant.MethodsOur research is a retrospective study, and the main purpose is to develop a high-precision classification algorithm for benign and malignant breast cancer. To achieve this, we first preprocessed the dataset using standard techniques such as feature scaling and handling missing values. We assessed the normality of the data distribution initially, after which we opted for Spearman correlation analysis to examine the relationship between the feature subset data and the labeled data, considering the normality test results. We subsequently employed the Wilcoxon rank sum test to investigate the dissimilarities in distribution among various breast cancer feature data. We constructed the feature subset based on statistical results and trained 7 machine learning algorithms, specifically the decision tree, stochastic gradient descent algorithm, random forest algorithm, support vector machine algorithm, logistics algorithm, and AdaBoost algorithm.ResultsThe results of the evaluation indicated that the AdaBoost-Logistic algorithm achieved an accuracy of 99.12%, outperforming the other 6 algorithms and previous techniques.ConclusionThe constructed AdaBoost-Logistic algorithm exhibits significant precision with the Wisconsin breast cancer dataset, achieving commendable classification performance for both benign and malignant breast cancer cases.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"25 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583514","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}
Pub Date : 2024-04-08DOI: 10.1177/15330338241242654
Zhe Wu, Mujun Liu, Ya Pang, Lihua Deng, Yi Yang, Yi Wu
Purpose: Deep learning (DL) is widely used in dose prediction for radiation oncology, multiple DL techniques comparison is often lacking in the literature. To compare the performance of 4 state-of-the-art DL models in predicting the voxel-level dose distribution for cervical cancer volumetric modulated arc therapy (VMAT). Methods and Materials: A total of 261 patients’ plans for cervical cancer were retrieved in this retrospective study. A three-channel feature map, consisting of a planning target volume (PTV) mask, organs at risk (OARs) mask, and CT image was fed into the three-dimensional (3D) U-Net and its 3 variants models. The data set was randomly divided into 80% as training-validation and 20% as testing set, respectively. The model performance was evaluated on the 52 testing patients by comparing the generated dose distributions against the clinical approved ground truth (GT) using mean absolute error (MAE), dose map difference (GT-predicted), clinical dosimetric indices, and dice similarity coefficients (DSC). Results: The 3D U-Net and its 3 variants DL models exhibited promising performance with a maximum MAE within the PTV 0.83% ± 0.67% in the UNETR model. The maximum MAE among the OARs is the left femoral head, which reached 6.95% ± 6.55%. For the body, the maximum MAE was observed in UNETR, which is 1.19 ± 0.86%, and the minimum MAE was 0.94 ± 0.85% for 3D U-Net. The average error of the Dmean difference for different OARs is within 2.5 Gy. The average error of V40 difference for the bladder and rectum is about 5%. The mean DSC under different isodose volumes was above 90%. Conclusions: DL models can predict the voxel-level dose distribution accurately for cervical cancer VMAT treatment plans. All models demonstrated almost analogous performance for voxel-wise dose prediction maps. Considering all voxels within the body, 3D U-Net showed the best performance. The state-of-the-art DL models are of great significance for further clinical applications of cervical cancer VMAT.
{"title":"A Comparative Study of Deep Learning Dose Prediction Models for Cervical Cancer Volumetric Modulated Arc Therapy","authors":"Zhe Wu, Mujun Liu, Ya Pang, Lihua Deng, Yi Yang, Yi Wu","doi":"10.1177/15330338241242654","DOIUrl":"https://doi.org/10.1177/15330338241242654","url":null,"abstract":"Purpose: Deep learning (DL) is widely used in dose prediction for radiation oncology, multiple DL techniques comparison is often lacking in the literature. To compare the performance of 4 state-of-the-art DL models in predicting the voxel-level dose distribution for cervical cancer volumetric modulated arc therapy (VMAT). Methods and Materials: A total of 261 patients’ plans for cervical cancer were retrieved in this retrospective study. A three-channel feature map, consisting of a planning target volume (PTV) mask, organs at risk (OARs) mask, and CT image was fed into the three-dimensional (3D) U-Net and its 3 variants models. The data set was randomly divided into 80% as training-validation and 20% as testing set, respectively. The model performance was evaluated on the 52 testing patients by comparing the generated dose distributions against the clinical approved ground truth (GT) using mean absolute error (MAE), dose map difference (GT-predicted), clinical dosimetric indices, and dice similarity coefficients (DSC). Results: The 3D U-Net and its 3 variants DL models exhibited promising performance with a maximum MAE within the PTV 0.83% ± 0.67% in the UNETR model. The maximum MAE among the OARs is the left femoral head, which reached 6.95% ± 6.55%. For the body, the maximum MAE was observed in UNETR, which is 1.19 ± 0.86%, and the minimum MAE was 0.94 ± 0.85% for 3D U-Net. The average error of the Dmean difference for different OARs is within 2.5 Gy. The average error of V40 difference for the bladder and rectum is about 5%. The mean DSC under different isodose volumes was above 90%. Conclusions: DL models can predict the voxel-level dose distribution accurately for cervical cancer VMAT treatment plans. All models demonstrated almost analogous performance for voxel-wise dose prediction maps. Considering all voxels within the body, 3D U-Net showed the best performance. The state-of-the-art DL models are of great significance for further clinical applications of cervical cancer VMAT.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"47 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583505","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}
Pub Date : 2024-04-08DOI: 10.1177/15330338241242637
San-feng Wang, Chong-yang Du, Mi Li, Bin Wen, Qing-jun Shen, Fang Ma, Liang Zhang, Hua Deng
Background: Endometrial cancer (EC) is the leading gynecological cancer worldwide, yet current EC screening approaches are not satisfying. The purpose of this retrospective study was to evaluate the feasibility and capability of DNA methylation analysis in cervical Papanicolaou (Pap) brush samples for EC detection. Methods: We used quantitative methylation-sensitive PCR (qMS-PCR) to determine the methylation status of candidate genes in EC tissue samples, as well as cervical Pap brushes. The ability of RASSF1A and HIST1H4F to serve as diagnostic markers for EC was then examined in cervical Pap brush samples from women with endometrial lesions of varying degrees of severity. Results: Methylated RASSF1A and HIST1H4F were found in EC tissues. Further, methylation of the two genes was also observed in cervical Pap smear samples from EC patients. Methylation levels of RASSF1A and HIST1H4F increased as endometrial lesions progressed, and cervical Pap brush samples from women affected by EC exhibited significantly higher levels of methylated RASSF1A and HIST1H4F compared to noncancerous controls ( P < .001). Receiver operating characteristic (ROC) curves and area under the curve (AUC) analyses revealed RASSF1A and HIST1H4F methylation with a combined AUC of 0.938 and 0.951 for EC/pre-EC detection in cervical Pap brush samples, respectively. Conclusion: These findings demonstrate that DNA methylation analysis in cervical Pap brush samples may be helpful for EC detection, broadening the scope of the commonly used cytological screening. Our proof-of-concept study provides new insights into the field of clinical EC diagnosis.
背景：子宫内膜癌（EC）是全球最主要的妇科癌症，但目前的EC筛查方法并不令人满意。这项回顾性研究的目的是评估宫颈巴氏涂片样本中的 DNA 甲基化分析用于检测子宫内膜癌的可行性和能力。方法：我们使用甲基化敏感定量 PCR（qMS-PCR）来确定宫颈癌组织样本和宫颈巴氏涂片刷样本中候选基因的甲基化状态。然后在患有不同严重程度子宫内膜病变的妇女的宫颈巴氏涂片样本中检测了 RASSF1A 和 HIST1H4F 作为宫颈癌诊断标记物的能力。结果显示在EC组织中发现了甲基化的RASSF1A和HIST1H4F。此外，在宫颈癌患者的宫颈涂片样本中也观察到了这两个基因的甲基化。RASSF1A和HIST1H4F的甲基化水平随着子宫内膜病变的进展而增加，与非癌变对照组相比，宫颈癌患者的宫颈涂片样本中RASSF1A和HIST1H4F的甲基化水平明显更高（P <.001）。接收者操作特征（ROC）曲线和曲线下面积（AUC）分析显示，在宫颈涂片样本中检测EC/EC前，RASSF1A和HIST1H4F甲基化的综合AUC分别为0.938和0.951。结论这些研究结果表明，宫颈涂片刷状样本中的DNA甲基化分析有助于检测EC，从而扩大了常用细胞学筛查的范围。我们的概念验证研究为临床EC诊断领域提供了新的见解。
{"title":"Endometrial Cancer Detection by DNA Methylation Analysis in Cervical Papanicolaou Brush Samples","authors":"San-feng Wang, Chong-yang Du, Mi Li, Bin Wen, Qing-jun Shen, Fang Ma, Liang Zhang, Hua Deng","doi":"10.1177/15330338241242637","DOIUrl":"https://doi.org/10.1177/15330338241242637","url":null,"abstract":"Background: Endometrial cancer (EC) is the leading gynecological cancer worldwide, yet current EC screening approaches are not satisfying. The purpose of this retrospective study was to evaluate the feasibility and capability of DNA methylation analysis in cervical Papanicolaou (Pap) brush samples for EC detection. Methods: We used quantitative methylation-sensitive PCR (qMS-PCR) to determine the methylation status of candidate genes in EC tissue samples, as well as cervical Pap brushes. The ability of RASSF1A and HIST1H4F to serve as diagnostic markers for EC was then examined in cervical Pap brush samples from women with endometrial lesions of varying degrees of severity. Results: Methylated RASSF1A and HIST1H4F were found in EC tissues. Further, methylation of the two genes was also observed in cervical Pap smear samples from EC patients. Methylation levels of RASSF1A and HIST1H4F increased as endometrial lesions progressed, and cervical Pap brush samples from women affected by EC exhibited significantly higher levels of methylated RASSF1A and HIST1H4F compared to noncancerous controls ( P < .001). Receiver operating characteristic (ROC) curves and area under the curve (AUC) analyses revealed RASSF1A and HIST1H4F methylation with a combined AUC of 0.938 and 0.951 for EC/pre-EC detection in cervical Pap brush samples, respectively. Conclusion: These findings demonstrate that DNA methylation analysis in cervical Pap brush samples may be helpful for EC detection, broadening the scope of the commonly used cytological screening. Our proof-of-concept study provides new insights into the field of clinical EC diagnosis.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"121 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583412","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}
Pub Date : 2024-04-03DOI: 10.1177/15330338241245342
Bhagyashree V. Salvi, Maithali Kantak, Kalyani Kharangate, Francesco Trotta, Timothy Maher, Pravin Shende
The field of nanomedicine demonstrates immense advantages and noteworthy expansion compared to conventional drug delivery systems like tablet, capsules, etc. Despite the innumerable advantages, it holds certain shortcomings in the form of blind spots that need to be assessed before the successful clinical translation. This perspective highlights the foremost blind spots in nanomedicine and emphasizes the challenges faced before the entry into the market, including the need for provision of safety and efficacy data by the regulatory agencies like FDA. The significant revolution of nanomedicine in the human life, particularly in patient well-being, necessitates to identify the blind spots and overcome them for effective management and treatment of ailments.
{"title":"Blind Spots in Development of Nanomedicines","authors":"Bhagyashree V. Salvi, Maithali Kantak, Kalyani Kharangate, Francesco Trotta, Timothy Maher, Pravin Shende","doi":"10.1177/15330338241245342","DOIUrl":"https://doi.org/10.1177/15330338241245342","url":null,"abstract":"The field of nanomedicine demonstrates immense advantages and noteworthy expansion compared to conventional drug delivery systems like tablet, capsules, etc. Despite the innumerable advantages, it holds certain shortcomings in the form of blind spots that need to be assessed before the successful clinical translation. This perspective highlights the foremost blind spots in nanomedicine and emphasizes the challenges faced before the entry into the market, including the need for provision of safety and efficacy data by the regulatory agencies like FDA. The significant revolution of nanomedicine in the human life, particularly in patient well-being, necessitates to identify the blind spots and overcome them for effective management and treatment of ailments.","PeriodicalId":22203,"journal":{"name":"Technology in Cancer Research & Treatment","volume":"20 1","pages":""},"PeriodicalIF":2.8,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583502","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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