Tumor DNA Methylation Profiles Enable Diagnosis, Prognosis Prediction, and Screening for Cervical Cancer.

Background: DNA-methylation-based machine learning algorithms have demonstrated powerful diagnostic capabilities, and these tools are currently emerging in many fields of tumor diagnosis and patient prognosis prediction. This work aimed to identify novel DNA methylation diagnostic biomarkers for differentiating cervical cancer (CC) from normal tissues, as well as a prognostic prediction model to predict survival of CC patients. Methods: The methylation profiles with the available clinical characteristics were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) program. We first screened out the differential methylation sites in CC and normal tissues and performed multiple statistical analyses to discover DNA methylation diagnostic markers that are used to distinguish CC and normal control. Then, we developed a methylation-based survival model to improve risk stratification. Results: A diagnostic prediction panel consists of five CpG markers that could predict cervical cancer versus normal tissue with highly correct rate of 100%, and cg16428251, cg22341310, and cg23316360 which in diagnostic prediction panel all could yield high sensitivity and specificity for detection of CC and normal in six cohorts (area under curve [AUC] > 0.8), in addition to excellent performance in discriminating between CC and normal sample. The diagnostic marker panel also effectively predicted the CIN3 versus normal tissue with high accuracy in two datasets (AUC = 0.80, 0.789, respectively). Furthermore, a prognostic prediction model aggregated two CpG markers that effectively stratified the prognosis of high-risk and low-risk groups (training cohort: hazard ratio [HR] 4, 95% CI: 1.7-9.6, P = 0.0021; testing cohort: hazard ratio [HR] 1.9, 95% CI: 1.2-3.1, P = 0.0072). Conclusion: The findings of our study showed that DNA methylation markers are of great value in the diagnosis and prognosis of CC.

Combined Identification of Novel Markers for Diagnosis and Prognostic of Classic Hodgkin Lymphoma.

BACKGROUND: An effective diagnostic and prognostic marker based on the gene expression profile of classic Hodgkin lymphoma (cHL) has not yet been developed. The aim of the present study was to investigate potential markers for the diagnosis and prediction of cHL prognosis. METHODS: The gene expression profiles with all available clinical features were downloaded from the Gene Expression Omnibus (GEO) database. Then, multiple machine learning algorithms were applied to develop and validate a diagnostic signature by comparing cHL with normal control. In addition, we identified prognostic genes and built a prognostic model with them to predict the prognosis for 130 patients with cHL which were treated with first-line treatment (ABVD chemotherapy or an ABVD-like regimen). RESULTS: A diagnostic prediction signature was constructed and showed high specificity and sensitivity (training cohort: AUC=0.981,95% CI 0.933-0.998, P<0.001, validation cohort: AUC=0.955,95% CI 0.895-0.986, P<0.001). Additionally, nine prognostic genes (LAMP1, STAT1, MMP9, C1QB, ICAM1, CD274, CCL19, HCK and LILRB2) were screened and a prognostic prediction model was constructed with them, which had been confirmed effectively predicting prognosis (P<0.001). Furthermore, the results of the immune infiltration assessment indicated that the high scale of the fraction of CD8 + T cells, M1 macrophages, resting mast cells associated with an adverse outcome in cHL, and naive B cells related to prolonged survival. In addition, a nomogram that combined the prognostic prediction model and clinical characteristics is also suggested to have a good predictive value for the prognosis of patients. CONCLUSION: The new markers found in this study may be helpful for the diagnosis and prediction of the prognosis of cHL.

Comprehensive Characterization of Cachexia-Inducing Factors in Diffuse Large B-Cell Lymphoma Reveals a Molecular Subtype and a Prognosis-Related Signature.

BACKGROUND: Cachexia is defined as an involuntary decrease in body weight, which can increase the risk of death in cancer patients and reduce the quality of life. Cachexia-inducing factors (CIFs) have been reported in colorectal cancer and pancreatic adenocarcinoma, but their value in diffuse large B-cell lymphoma (DLBCL) requires further genetic research. METHODS: We used gene expression data from Gene Expression Omnibus to evaluate the expression landscape of 25 known CIFs in DLBCL patients and compared them with normal lymphoma tissues from two cohorts [GSE56315 (n = 88) and GSE12195 (n = 136)]. The mutational status of CIFs were also evaluated in The Cancer Genome Atlas database. Based on the expression profiles of 25 CIFs, a single exploratory dataset which was merged by the datasets of GSE10846 (n = 420) and GSE31312 (n = 498) were divided into two molecular subtypes by using the method of consensus clustering. Immune microenvironment between different subtypes were assessed via single-sample gene set enrichment analysis and the CIBERSORT algorithm. The treatment response of commonly used chemotherapeutic drugs was predicted and gene set variation analysis was utilized to reveal the divergence in activated pathways for distinct subtypes. A risk signature was derived by univariate Cox regression and LASSO regression in the merged dataset (n = 882), and two independent cohorts [GSE87371 (n = 221) and GSE32918 (n = 244)] were used for validation, respectively. RESULTS: Clustering analysis with CIFs further divided the cases into two molecular subtypes (cluster A and cluster B) associated with distinct prognosis, immunological landscape, chemosensitivity, and biological process. A risk-prognostic signature based on CCL2, CSF2, IL15, IL17A, IL4, TGFA, and TNFSF10 for DLBCL was developed, and significant differences in overall survival analysis were found between the low- and high-risk groups in the training dataset and another two independent validation datasets. Multivariate regression showed that the risk signature was an independently prognostic factor in contrast to other clinical characteristics. CONCLUSION: This study demonstrated that CIFs further contribute to the observed heterogeneity of DLBCL, and molecular classification and a risk signature based on CIFs are both promising tools for prognostic stratification, which may provide important clues for precision medicine and tumor-targeted therapy.

Prognostic and predictive value of a mRNA signature in peripheral T-cell lymphomas: A mRNA expression analysis.

Current international prognostic index is widely questioned on the risk stratification of peripheral T-cell lymphoma and does not accurately predict the outcome for patients. We postulated that multiple mRNAs could combine into a model to improve risk stratification and helping clinicians make treatment decisions. In this study, the gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to screening genes in selected module which most closely related to PTCLs, and then built a mRNA signature using a LASSO Cox regression model and validated the prognostic accuracy of it. Finally, a nomogram was constructed and the performance was assessed. A total of 799 WGCNA-selected mRNAs in black module were identified, and a mRNA signature which based on DOCK2, GSTM1, H2AFY, KCNAB2, LAPTM5 and SYK for PTCLs was developed. Significantly statistical difference can be seen in overall survival of PTCLs between low-risk group and high-risk group (training set:hazard ratio [HR] 4.3, 95% CI 2.4-7.4, P < .0001; internal testing set:hazard ratio [HR] 2.4, 95% CI 1.2-4.8, P < .01; external testing set:hazard ratio [HR] 2.3, 95% CI 1.10-4.7, P = .02). Furthermore, multivariate regression demonstrated that the signature was an independently prognostic factor. Moreover, the nomogram which combined the mRNA signature and multiple clinical factors suggesting that predicted survival probability agreed well with the actual survival probability. The signature is a reliable prognostic tool for patients with PTCLs, and it has the potential for clinicians to implement personalized therapeutic regimen for patients with PTCLs.

Prognostic Significance of ß-Catenin Expression in Osteosarcoma: A Meta-Analysis.

Background: ß-catenin plays a crucial role in the progression of osteosarcoma. However, the clinical significance of ß-catenin over-expression in osteosarcoma still remains unclear. Thus, we performed a meta-analysis of studies that evaluated the impact of ß-catenin on metastasis and overall survival (OS) in osteosarcoma. Methods: We searched PubMed, The Cochrane Library, Embase, Springer, Science Direct, OVID, Weipu, Wanfang and China National Knowledge Internet (CNKI) databases from their start year up to Aug.2019. Individual hazard ratios (HRs) and 95% confidence intervals (CIs) were extracted and pooled HRs with 95% CIs or odd ratio (OR) were used to evaluate the relationships between ß-catenin over-expression and metastasis and overall survival in osteosarcoma. Results: Eight related studies involving 521 patients were qualified for this meta-analysis. Results showed that over-expression of ß-catenin was significantly correlated with metastasis (OR = 3.31, 95% CI = 2.08-5.24, P < 0.001) and overall survival (HR = 2.32, 95% CI = 1.48-363, P = 0.02). Conclusion: The meta-analysis revealed that over-expression of ß-catenin might be associated with distant metastasis and overall survival in osteosarcoma, which reminds that ß-catenin acts as a prognostic biomarker and it can guide the clinical therapy in osteosarcoma patients.

Platelet-lymphocyte ratio as a potential prognostic factor in gynecologic cancers: a meta-analysis.

PURPOSE: Cancer-related inflammation plays an important role in tumor development and progression. Platelet-lymphocyte ratio (PLR) has been studied as a biomarker for prognosis in gynecologic cancers. But, the results of previous studies were controversial, so we performed this meta-analysis. METHODS: We searched the scientific database of PubMed, Embase, Web of Science, Wanfang, and China National Knowledge Infrastructure (CNKI) using free text and MeSH keywords. Crude HR (hazard ratio) with 95% confidence interval was used to evaluate the risk association between PLR and overall survival (OS) or progression-free survival (PFS) in gynecologic neoplasms. RESULTS: There totally 23 studies, including 6869 patients who were eligible, most of which are published after 2015 or later. PLR greater than the cut-off was associated with poorer survival prognosis in ovarian cancer [OS: HR 1.80 (95% CI 1.37-2.37), p = 0.000; PFS: HR 1.63 (95% CI 1.38-1.91), p = 0.000] and cervical cancer [OS: HR 1.36 (95% CI 1.10-1.68), p = 0.005; PFS: HR 1.40 (95% CI 1.16-1.70), p = 0.002], but not in endometrial cancer [OS: HR 1.95 (95% CI 0.65-5.84), p = 0.234]. CONCLUSIONS: The current meta-analysis revealed that pretreatment PLR was a simple, promising prognostic indicator for OS and PFS in ovarian and cervical cancers. But, its significance of prognosis did not agree with endometrial neoplasm. However, due to the limited number of original studies, future large-scale studies with more well-designed, high-quality studies are still needed.

Identification of key genes and pathways associated with classical Hodgkin lymphoma by bioinformatics analysis.

The current study aimed to explore the mechanisms associated with classic Hodgkin lymphoma (cHL) to identify novel diagnostic and therapeutic targets. The GES12453 microarray dataset was downloaded from the Gene Expression Omnibus database; the differentially expressed genes (DEGs) between cHL samples and normal B cell samples by were identified using the limma package. Gene ontology (GO) and pathway enrichment analysis of DEGs gene were performed. Furthermore, construction and analysis of protein­protein interaction (PPI) network was performed, and co­expression modules of DEGs were produced. A total of 450 DEGs were identified, comprising 216 upregulated and 234 downregulated genes in cHL compared with normal B cell samples. The DEGs were enriched in biological processes associated with immune response. The upregulated genes were mainly associated with the pathway of transcriptional misregulation in cancer, while downregulated genes were associated with B cell receptor signaling. PPI network analysis demonstrated that IL6 had the highest connectivity degree. Interleukin­6 (IL6) and signal transducer and activator of transcription 1 (STAT1) were demonstrated to be involved with the response to cytokine GO term in co­expression module 1. Spleen tyrosine kinase (SYK), B­cell linker protein (BLNK), CD79B, phospholipase C γ2 (PLCG2) were enriched in the B cell receptor signaling pathway in module 2. Matrix metallopeptidase 9 (MMP9), protein tyrosine phosphatase receptor type C had the highest connectivity degrees in module 3 and module 4, respectively. The results suggested that DEGs, including IL6, STAT1, MMP9, SYK, BLNK, PLCG2 and CD79B, and the pathways of B cell receptor signaling, Epstein­Barr virus infection and transcriptional misregulation in cancer have strong potential to be useful as targets for diagnosis or treatment of cHL.