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Liu Q, Huang S, Desautels D, McManus KJ, Murphy L, Hu P. Development and validation of a prognostic 15-gene signature for stratifying HER2+/ER+ breast cancer. Comput Struct Biotechnol J 2023; 21:2940-2949. [PMID: 37216014 PMCID: PMC10196919 DOI: 10.1016/j.csbj.2023.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 05/02/2023] [Accepted: 05/03/2023] [Indexed: 05/24/2023] Open
Abstract
Background Human epidermal growth receptor 2-positive (HER2+) breast cancer (BC) is a heterogeneous subgroup. Estrogen receptor (ER) status is emerging as a predictive marker within HER2+ BCs, with the HER2+/ER+ cases usually having better survival in the first 5 years after diagnosis but have higher recurrence risk after 5 years compared to HER2+/ER-. This is possibly because sustained ER signaling in HER2+ BCs helps escape the HER2 blockade. Currently HER2+/ER+ BC is understudied and lacks biomarkers. Thus, a better understanding of the underlying molecular diversity is important to find new therapy targets for HER2+/ER+ BCs. Methods In this study, we performed unsupervised consensus clustering together with genome-wide Cox regression analyses on the gene expression data of 123 HER2+/ER+ BC from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) cohort to identify distinct HER2+/ER+ subgroups. A supervised eXtreme Gradient Boosting (XGBoost) classifier was then built in TCGA using the identified subgroups and validated in another two independent datasets (Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO) (accession number GSE149283)). Computational characterization analyses were also performed on the predicted subgroups in different HER2+/ER+ BC cohorts. Results We identified two distinct HER2+/ER+ subgroups with different survival outcomes using the expression profiles of 549 survival-associated genes from the Cox regression analyses. Genome-wide gene expression differential analyses found 197 differentially expressed genes between the two identified subgroups, with 15 genes overlapping the 549 survival-associated genes.XGBoost classifier, using the expression values of the 15 genes, achieved a strong cross-validated performance (Area under the curve (AUC) = 0.85, Sensitivity = 0.76, specificity = 0.77) in predicting the subgroup labels. Further investigation partially confirmed the differences in survival, drug response, tumor-infiltrating lymphocytes, published gene signatures, and CRISPR-Cas9 knockout screened gene dependency scores between the two identified subgroups. Conclusion This is the first study to stratify HER2+/ER+ tumors. Overall, the initial results from different cohorts showed there exist two distinct subgroups in HER2+/ER+ tumors, which can be distinguished by a 15-gene signature. Our findings could potentially guide the development of future precision therapies targeted on HER2+/ER+ BC.
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Affiliation(s)
- Qian Liu
- Department of Biochemistry, Western University, London, Ontario, Canada
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada
- Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Shujun Huang
- Department of Biochemistry, Western University, London, Ontario, Canada
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Danielle Desautels
- CancerCare Manitoba Research Institute, Winnipeg, Manitoba, Canada
- Department of Internal Medicine, Winnipeg, Manitoba, Canada
| | - Kirk J. McManus
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
- CancerCare Manitoba Research Institute, Winnipeg, Manitoba, Canada
| | - Leigh Murphy
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
- CancerCare Manitoba Research Institute, Winnipeg, Manitoba, Canada
| | - Pingzhao Hu
- Department of Biochemistry, Western University, London, Ontario, Canada
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, Canada
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada
- CancerCare Manitoba Research Institute, Winnipeg, Manitoba, Canada
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Epidemiological, Clinical, and Genomic Profile in Head and Neck Cancer Patients and Their Families. Biomedicines 2022; 10:biomedicines10123278. [PMID: 36552033 PMCID: PMC9775590 DOI: 10.3390/biomedicines10123278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Inherited cancer predisposition genes are described as risk factors in head and neck cancer (HNC) families. To explore the clinical and epidemiological data and their association with a family history of cancer, we recruited 74 patients and 164 relatives affected by cancer. The germline copy number alterations were evaluated in 18 patients using array comparative genomic hybridization. Two or more first-degree relatives with HNC, tobacco-associated tumor sites (lung, esophagus, and pancreas), or other related tumors (breast, colon, kidney, bladder, cervix, stomach carcinomas, and melanoma) were reported in 74 families. Ten index patients had no exposure to any known risk factors. Family members presented tumors of 19 topographies (30 head and neck, 26 breast, 21 colon). In first-degree relatives, siblings were frequently affected by cancer (n = 58, 13 had HNC). Breast cancer (n = 21), HNC (n = 19), and uterine carcinoma (n = 15) were commonly found in first-degree relatives and HNC in second-degree relatives (n = 11). Nineteen germline genomic imbalances were detected in 13 patients; three presented gains of WRD genes. The number of HNC patients, the degree of kinship, and the tumor types detected in each relative support the role of heredity in these families. Germline alterations may potentially contribute to cancer development.
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Chen J, Ma J, Li C, Shao S, Su Y, Wu R, Yao M. Multi-parameter ultrasonography-based predictive model for breast cancer diagnosis. Front Oncol 2022; 12:1027784. [PMID: 36465370 PMCID: PMC9714455 DOI: 10.3389/fonc.2022.1027784] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 11/01/2022] [Indexed: 12/31/2023] Open
Abstract
OBJECTIVES To develop, validate, and evaluate a predictive model for breast cancer diagnosis using conventional ultrasonography (US), shear wave elastography (SWE), and contrast-enhanced US (CEUS). MATERIALS AND METHODS This retrospective study included 674 patients with 674 breast lesions. The data, a main and an independent datasets, were divided into three cohorts. Cohort 1 (80% of the main dataset; n = 448) was analyzed by logistic regression analysis to identify risk factors and establish the predictive model. The area under the receiver operating characteristic curve (AUC) was analyzed in Cohort 2 (20% of the main dataset; n = 119) to validate and in Cohort 3 (the independent dataset; n = 107) to evaluate the predictive model. RESULTS Multivariable regression analysis revealed nine independent breast cancer risk factors, including age > 40 years; ill-defined margin, heterogeneity, rich blood flow, and abnormal axillary lymph nodes on US; enhanced area enlargement, contrast agent retention, and irregular shape on CEUS; mean SWE higher than the cutoff value (P < 0.05 for all). The diagnostic performance of the model was good, with AUC values of 0.847, 0.857, and 0.774 for Cohorts 1, 2, and 3, respectively. The model increased the diagnostic specificity (from 31% to 81.3% and 7.3% to 73.1% in cohorts 2 and 3, respectively) without a significant loss in sensitivity (from 100.0% to 90.1% and 100.0% to 81.8% in cohorts 2 and 3, respectively). CONCLUSION The multi-parameter US-based model showed good performance in breast cancer diagnosis, improving specificity without a significant loss in sensitivity. Using the model could reduce unnecessary biopsies and guide clinical diagnosis and treatment.
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Affiliation(s)
| | | | | | | | | | - Rong Wu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Minghua Yao
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Shi W, Chen Z, Liu H, Miao C, Feng R, Wang G, Chen G, Chen Z, Fan P, Pang W, Li C. COL11A1 as an novel biomarker for breast cancer with machine learning and immunohistochemistry validation. Front Immunol 2022; 13:937125. [PMID: 36389832 PMCID: PMC9660229 DOI: 10.3389/fimmu.2022.937125] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 10/07/2022] [Indexed: 12/03/2022] Open
Abstract
Machine learning (ML) algorithms were used to identify a novel biological target for breast cancer and explored its relationship with the tumor microenvironment (TME) and patient prognosis. The edgR package identified hub genes associated with overall survival (OS) and prognosis, which were validated using public datasets. Of 149 up-regulated genes identified in tumor tissues, three ML algorithms identified COL11A1 as a hub gene. COL11A1was highly expressed in breast cancer samples and associated with a poor prognosis, and positively correlated with a stromal score (r=0.49, p<0.001) and the ESTIMATE score (r=0.29, p<0.001) in the TME. Furthermore, COL11A1 negatively correlated with B cells, CD4 and CD8 cells, but positively associated with cancer-associated fibroblasts. Forty-three related immune-regulation genes associated with COL11A1 were identified, and a five-gene immune regulation signature was built. Compared with clinical factors, this gene signature was an independent risk factor for prognosis (HR=2.591, 95%CI 1.831–3.668, p=7.7e-08). A nomogram combining the gene signature with clinical variables, showed better predictive performance (C-index=0.776). The model correction prediction curve showed little bias from the ideal curve. COL11A1 is a potential therapeutic target in breast cancer and may be involved in the tumor immune infiltration; its high expression is strongly associated with poor prognosis.
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Affiliation(s)
- Wenjie Shi
- University Hospital for Gynecology, Pius-Hospital, University Medicine Oldenburg, Oldenburg, Germany
- University Clinic for General, Visceral, Vascular and Transplantation Surgery, Faculty of Medicine, Otto-von-Guericke-University, Magdeburg, Germany
| | - Zhilin Chen
- University Hospital for Gynecology, Pius-Hospital, University Medicine Oldenburg, Oldenburg, Germany
- Department of Breast Surgery, Hainan Medical University, Haikou, China
| | - Hui Liu
- Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Heath, Guilin Medical University, Guilin, China
| | - Chen Miao
- Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ruifa Feng
- Breast Center of The Second Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Guilin Wang
- Breast Center of The Second Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Guoping Chen
- Department of Breast Surgery, Hainan Medical University, Haikou, China
| | - Zhitong Chen
- University Hospital for Gynecology, Pius-Hospital, University Medicine Oldenburg, Oldenburg, Germany
| | - Pingming Fan
- Department of Breast Surgery, Hainan Medical University, Haikou, China
- *Correspondence: Pingming Fan, ; Weiyi Pang, ; Chen Li,
| | - Weiyi Pang
- Guangxi Key Laboratory of Environmental Exposomics and Entire Lifecycle Heath, Guilin Medical University, Guilin, China
- *Correspondence: Pingming Fan, ; Weiyi Pang, ; Chen Li,
| | - Chen Li
- Department of Biology, Chemistry, Pharmacy, Free University of Berlin, Berlin, Germany
- *Correspondence: Pingming Fan, ; Weiyi Pang, ; Chen Li,
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Prognostic Assessment of Oxidative Stress-Related Genes in Colorectal Cancer and New Insights into Tumor Immunity. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2022; 2022:2518340. [PMID: 36299603 PMCID: PMC9590115 DOI: 10.1155/2022/2518340] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 09/21/2022] [Indexed: 11/19/2022]
Abstract
Oxidative stress is crucial to the biology of tumors. Oxidative stress' potential predictive significance in colorectal cancer (CRC) has not been studied; nevertheless here, we developed a forecasting model based on oxidative stress to forecast the result of CRC survival and enhance clinical judgment. The training set was chosen from the transcriptomes of 177 CRC patients in GSE17536. For validation, 65 samples of colon cancer from GSE29621 were utilized. For the purpose of choosing prognostic genes, the expression of oxidative stress-related genes (OXEGs) was found. Prognostic risk models were built using multivariate Cox regression analysis, univariate Cox regression analysis, and LASSO regression analysis. The outcomes of the western blot and transcriptome sequencing tests were finally confirmed. ATF4, CARS2, CRP, GPX1, IL1B, MAPK8, MRPL44, MTFMT, NOS1, OSGIN2, SOD2, AARS2, and FOXO3 were among the 14 OXEGs used to build prognostic characteristics. Patients with CRC were categorized into low-risk and high-risk groups according on their median risk scores. Cox regression analysis using single and multiple variables revealed that OXEG-related signals were independent risk factors for CRC. Additionally, the validation outcomes from western blotting and transcriptome sequencing demonstrated that OXEGs were differently expressed. Using 14 OXEGs, our work creates a predictive signature that may be applied to the creation of new prognostic models and the identification of possible medication candidates for the treatment of CRC.
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Pan-Cancer Analysis Reveals the Relation between TRMT112 and Tumor Microenvironment. JOURNAL OF ONCOLOGY 2022; 2022:1445932. [PMID: 36081672 PMCID: PMC9448524 DOI: 10.1155/2022/1445932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/29/2022] [Accepted: 08/09/2022] [Indexed: 11/17/2022]
Abstract
Dysregulated epigenetic modifications play a critical role in cancer development where TRMT112 is a member of the transfer RNA (tRNA) methyltransferase family. Till now, no studies have revealed the linkage between TRMT112 expression and diverse types of tumors. Based on TCGA data, we first probed into the relation between TRMT112 and prognosis and the potential role of TRMT112 in tumor microenvironment across 33 types of tumor. TRMT112 presented with increased expression in most cancers, which was significantly prognostic. Furthermore, TRMT112 was associated with tumor-associated fibroblasts in a variety of cancers. Additionally, a positive relationship was identified between TRMT112 expression and multiple tumor-related immune infiltrations, such as dendritic cells, CD8+ T cells, macrophages, CD4+ T cells, neutrophils, and B cells in lung adenocarcinoma and breast invasive carcinoma. In summary, our results suggest that TRMT112 might be a potential prognostic predictor of cancers and involved in regulating multiple cancer-related immune responses to some extent.
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Developing a 5-Gene Signature Related to Pyroptosis for Osteosarcoma Patients. JOURNAL OF ONCOLOGY 2022; 2022:1317990. [PMID: 36035315 PMCID: PMC9410960 DOI: 10.1155/2022/1317990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/27/2022] [Accepted: 07/04/2022] [Indexed: 11/18/2022]
Abstract
Although the incidence of osteosarcoma (OS) is relatively low compared with other cancer types, the overall survival of metastatic OS was less than 30%. This study aimed to reveal the role of pyroptosis in osteosarcoma and develop a prognostic model related to pyroptosis. Weighted correlation network analysis (WGCNA) was applied to identify key gene modules related to pyroptosis. Univariate Cox regression analysis was used to screen prognostic genes related to pyroptosis. The least absolute shrinkage and selection operator (LASSO) and stepwise Akaike information criterion (stepAIC) were employed to optimize and construct a prognostic model. Five prognostic genes (COL13A1, TNFRSF1A, LILRA6, CTNNBIP1, and CD180) related to pyroptosis were identified. According to the 5-gene signature, OS samples were divided into high- and low-PPRS groups with differential prognosis. Immune-related pathways were more activated in the low-PPRS group. The 5-gene signature was effective and robust to predict OS prognosis. These five prognostic genes were involved in OS development and may serve as new targets for developing therapeutic drugs.
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Comprehensive Analysis of Histone Modifications in Hepatocellular Carcinoma Reveals Different Subtypes and Key Prognostic Models. JOURNAL OF ONCOLOGY 2022; 2022:5961603. [PMID: 35957801 PMCID: PMC9359864 DOI: 10.1155/2022/5961603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/01/2022] [Accepted: 06/07/2022] [Indexed: 11/17/2022]
Abstract
Histone modification, an important epigenetic mechanism, is related to the carcinogenesis of hepatocellular carcinoma (HCC). In three datasets, we screened 88 epigenetic-dysregulated PCGs (epi-PCGs) , which were significantly associated with HCC survival and could cluster HCC into three molecular subtypes. These subtypes were associated with prognosis, immunomodulatory alterations, and response to different treatment strategies. Based on 88 epi-PCGs in the TCGA training set, a risk prediction model composed of 4 epi-PCGs was established. The model was closely related to the clinicopathological features and showed a strong predictive ability in different clinical subgroups. In addition, the risk prediction model was an independent prognostic factor for patients with HCC. The significance of epi-PCGs in HCC is revealed by our data analysis.
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Identification of Six Genes as Diagnostic Markers for Colorectal Cancer Detection by Integrating Multiple Expression Profiles. JOURNAL OF ONCOLOGY 2022; 2022:3850674. [PMID: 35909904 PMCID: PMC9337943 DOI: 10.1155/2022/3850674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/24/2022] [Accepted: 07/04/2022] [Indexed: 12/24/2022]
Abstract
Background Many studies have demonstrated the promising utility of DNA methylation and miRNA as biomarkers for colorectal cancer (CRC) early detection. However, mRNA is rarely reported. This study aimed to identify novel fecal-based mRNA signatures. Methods The differentially expressed genes (DEGs) were first determined between CRCs and matched normal samples by integrating multiple datasets. Then, Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to reduce the number of candidates of aberrantly expressed genes. Next, the potential functions were investigated for the candidate signatures and their ability to detect CRC and pan-cancers was comprehensively evaluated. Results We identified 1841 common DEGs in two independent datasets. Functional enrichment analysis revealed they were mainly related to extracellular structure, biosynthesis, and cell adhesion. The CRC classifier was established based on six genes screened by LASSO regression. Sensitivity, specificity, and area under the ROC curve (AUC) for CRC detection were 79.30%, 80.40%, and 0.85 (0.76–0.92) in the training set, and these indexes achieved 93.20%, 41.80%, and 0.73 (0.65–0.83) in the testing set. For validation set, the sensitivity, specificity, and AUC were 98.90%, 98.00%, and 0.97 (0.94–0.99). The average sensitivities exceeded 90.00% for CRCs with different clinical features. For adenomas detection, the sensitivity and specificity were 74.50% and 64.00%. Besides, the six genes obtained an average AUC of 0.855 for pan-cancer detection. Conclusion The six-gene signatures showed ability to detect CRC and pan-cancer samples, which could be served as potential diagnostic markers.
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Bioinformatics Analysis Reveals an Association between Autophagy, Prognosis, Tumor Microenvironment, and Immunotherapy in Osteosarcoma. JOURNAL OF ONCOLOGY 2022; 2022:4220331. [PMID: 35874628 PMCID: PMC9303156 DOI: 10.1155/2022/4220331] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/13/2022] [Accepted: 06/20/2022] [Indexed: 12/14/2022]
Abstract
Autophagy is a catabolic pathway involved in the regulation of bone homeostasis. We explore clinical correlation of autophagy-related key molecules to establish risk signature for predicting the prognosis, tumor microenvironment (TME), and immunotherapy response of osteosarcoma. Single cell RNA sequencing data from GSE162454 dataset distinguished malignant cells from normal cells in osteosarcoma. Autophagy-related genes (ARGs) were extracted from the established risk signature of the Molecular Signatures Database of Gene Set Enrichment Analysis (GSEA) by univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Overall survival (OS), TME score, abundance of infiltrating immune cells, and response to immune-checkpoint blockade (ICB) treatment in patients with different risks were compared based on risk score. Nine ARGs were identified and risk signature was constructed. In all osteosarcoma datasets, the OS was significantly longer in the high-risk patients than low-risk onset. Risk signature significantly stratified clinical outcomes, including OS, metastatic status, and survival status. Risk signature was an independent variable for predicting osteosarcoma OS and showed high accuracy. A nomogram based on risk signature and metastases was developed. The calibration curve confirmed the consistency in 1-year, 3-year, and 5-year predicted OS and the actual OS. The risk score was related to 6 kinds of T cells and macrophages, myeloid-derived suppressor cell, natural killer cell, immune score, and stromal score in TME. The risk signature helped in predicting patients' response to anti-PD1/anti-PD-L1 treatment. The ARGs-led risk signature has important value for survival prediction, risk stratification, tumor microenvironment, and immune response evaluation of osteosarcoma.
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Zuo J, Liu C, Ni H, Yu Z. WDR34 affects PI3K/Akt and Wnt/β-catenin pathways to regulates malignant biological behaviors of glioma cells. J Neurooncol 2022; 156:281-293. [PMID: 34981299 DOI: 10.1007/s11060-021-03932-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 12/20/2021] [Indexed: 10/19/2022]
Abstract
PURPOSE Glioma is the most prevalent primary intracranial tumor globally. WDR34, a member of the WDR superfamily with five WD40 repeats, is involved in the pathogenesis of several tumors. However, the role of WDR34 in glioma progression is unknown. METHODS The expression and prognostic significance of WDR34 in glioma patients were analyzed using GEPIA. WDR34 expression was detected by qRT-PCR. Western blot was employed to determine the expression of Ki67, proliferating cell nuclear antigen (PCNA), matrix metallopeptidase (MMP)2, MMP9, phosphatase and tensin homolog, protein kinase B (Akt), phosphorylated Akt, β-catenin, and c-Myc. CCK-8, BrdU incorporation assay, Transwell invasion assay, flow cytometry analysis, and measurement of caspase-3 and caspase-9 activities were conducted to examine the effects of WDR34 knockdown on glioma cells. RESULTS WDR34 was upregulated in glioma, which predicted a poor prognosis in glioma patients. WDR34 knockdown inhibited cell proliferation and reduced the expression of Ki67 and PCNA in glioma cells. WDR34 knockdown repressed the invasive ability of glioma cells by decreasing MMP-2 and MMP-9 expression. WDR34 knockdown increased the apoptotic rate and caspase-3 and caspase-9 activities in glioma cells. The PI3K/Akt and Wnt/β-catenin pathways were inhibited after WDR34 knockdown in glioma cells. Moreover, overexpression of Akt or β-catenin reversed the function of WDR34 knockdown on proliferation, invasion, and apoptosis. WDR34 knockdown reduced tumor growth in vivo. CONCLUSIONS WDR34 knockdown inhibited malignant biological behaviors of glioma cells by inactivating the PI3K/Akt and Wnt/β-catenin signaling cascades.
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Affiliation(s)
- Jiandong Zuo
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou, 215006, People's Republic of China
| | - Chun Liu
- Department of Neurosurgery, Lianshui People's Hospital Affiliated to Kangda College of Nanjing Medical University, Huai'an, 210009, People's Republic of China
| | - Hongzao Ni
- Department of Neurosurgery, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an Second People's Hospital, Huai'an, 223002, People's Republic of China
| | - Zhengquan Yu
- Department of Neurosurgery, The First Affiliated Hospital of Soochow University, 188 Shizi Street, Suzhou, 215006, People's Republic of China.
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Liu X, Wu Y, Zhang Y, Bu D, Wu C, Lu S, Huang Z, Song Y, Zhao Y, Guo F, Ye P, Fu C, Shen L, Zhang J, Wang H, Duan X, Wu J. High Throughput Transcriptome Data Analysis and Computational Verification Reveal Immunotherapy Biomarkers of Compound Kushen Injection for Treating Triple-Negative Breast Cancer. Front Oncol 2021; 11:747300. [PMID: 34604090 PMCID: PMC8484800 DOI: 10.3389/fonc.2021.747300] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 08/30/2021] [Indexed: 12/11/2022] Open
Abstract
Background Although notable therapeutic and prognostic benefits of compound kushen injection (CKI) have been found when it was used alone or in combination with chemotherapy or radiotherapy for triple-negative breast cancer (TNBC) treatment, the effects of CKI on TNBC microenvironment remain largely unclear. This study aims to construct and validate a predictive immunotherapy signature of CKI on TNBC. Methods The UPLC-Q-TOF-MS technology was firstly used to investigate major constituents of CKI. RNA sequencing data of CKI-perturbed TNBC cells were analyzed to detect differential expression genes (DEGs), and the GSVA algorithm was applied to explore significantly changed pathways regulated by CKI. Additionally, the ssGSEA algorithm was used to quantify immune cell abundance in TNBC patients, and these patients were classified into distinct immune infiltration subgroups by unsupervised clustering. Then, prognosis-related genes were screened from DEGs among these subgroups and were further overlapped with the DEGs regulated by CKI. Finally, a predictive immunotherapy signature of CKI on TNBC was constructed based on the LASSO regression algorithm to predict mortality risks of TNBC patients, and the signature was also validated in another TNBC cohort. Results Twenty-three chemical components in CKI were identified by UPLC-Q-TOF-MS analysis. A total of 3692 DEGs were detected in CKI-treated versus control groups, and CKI significantly activated biological processes associated with activation of T, natural killer and natural killer T cells. Three immune cell infiltration subgroups with 1593 DEGs were identified in TNBC patients. Then, two genes that can be down-regulated by CKI with hazard ratio (HR) > 1 and 26 genes that can be up-regulated by CKI with HR < 1 were selected as key immune- and prognosis-related genes regulated by CKI. Lastly, a five-gene prognostic signature comprising two risky genes (MARVELD2 and DYNC2I2) that can be down-regulated by CKI and three protective genes (RASSF2, FERMT3 and RASSF5) that can be up-regulated by CKI was developed, and it showed a good performance in both training and test sets. Conclusions This study proposes a predictive immunotherapy signature of CKI on TNBC, which would provide more evidence for survival prediction and treatment guidance in TNBC as well as a paradigm for exploring immunotherapy biomarkers in compound medicines.
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Affiliation(s)
- Xinkui Liu
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China
| | - Yang Wu
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Yingying Zhang
- Department of Vascular Neurosurgery, New Era Stroke Care and Research Institute, The People's Liberation Army (PLA) Rocket Force Characteristic Medical Center, Beijing, China
| | - Dechao Bu
- Pervasive Computing Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Chao Wu
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China
| | - Shan Lu
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China
| | - Zhihong Huang
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China
| | - Yurong Song
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China
| | - Yi Zhao
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.,School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China
| | - Fengying Guo
- School of Management, Beijing University of Chinese Medicine, Beijing, China
| | - Peizhi Ye
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Changgeng Fu
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Liangliang Shen
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China
| | - Jingyuan Zhang
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China
| | - Haojia Wang
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China
| | - Xianchun Duan
- Department of Pharmacy, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, China
| | - Jiarui Wu
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing, China
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A novel DNA methylation-based model that effectively predicts prognosis in hepatocellular carcinoma. Biosci Rep 2021; 41:227938. [PMID: 33634306 PMCID: PMC7955104 DOI: 10.1042/bsr20203945] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 02/22/2021] [Accepted: 02/25/2021] [Indexed: 12/15/2022] Open
Abstract
PURPOSE To build a novel predictive model for hepatocellular carcinoma (HCC) patients based on DNA methylation data. METHODS Four independent DNA methylation datasets for HCC were used to screen for common differentially methylated genes (CDMGs). Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to explore the biological roles of CDMGs in HCC. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were performed to identify survival-related CDMGs (SR-CDMGs) and to build a predictive model. The importance of this model was assessed using Cox regression analysis, propensity score-matched (PSM) analysis and stratification analysis. A validation group from the Cancer Genome Atlas (TCGA) was constructed to further validate the model. RESULTS Four SR-CDMGs were identified and used to build the predictive model. The risk score of this model was calculated as follows: risk score = (0.01489826 × methylation level of WDR69) + (0.15868618 × methylation level of HOXB4) + (0.16674959 × methylation level of CDKL2) + (0.16689301 × methylation level of HOXA10). Kaplan-Meier analysis demonstrated that patients in the low-risk group had a significantly longer overall survival (OS; log-rank P-value =0.00071). The Cox model multivariate analysis and PSM analysis identified the risk score as an independent prognostic factor (P<0.05). Stratified analysis results further confirmed this model performed well. By analyzing the validation group, the results of receiver operating characteristic (ROC) curve analysis and survival analysis further validated this model. CONCLUSION Our DNA methylation-based prognosis predictive model is effective and reliable in predicting prognosis for patients with HCC.
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