N6-methyladenosine-related non-coding RNAs are potential prognostic and immunotherapeutic responsiveness biomarkers for bladder cancer

Abstract Background Bladder cancer (BC) is a commonly occurring malignant tumor of the urinary system, demonstrating high global morbidity and mortality rates. BC currently lacks widely accepted biomarkers and its predictive, preventive, and personalized medicine (PPPM) is still unsatisfactory. N6-methyladenosine (m6A) modification and non-coding RNAs (ncRNAs) have been shown to be effective prognostic and immunotherapeutic responsiveness biomarkers and contribute to PPPM for various tumors. However, their role in BC remains unclear. Methods m6A-related ncRNAs (lncRNAs and miRNAs) were identified through a comprehensive analysis of TCGA, starBase, and m6A2Target databases. Using TCGA dataset (training set), univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to develop an m6A-related ncRNA–based prognostic risk model. Kaplan-Meier analysis of overall survival (OS) and receiver operating characteristic (ROC) curves were used to verify the prognostic evaluation power of the risk model in the GSE154261 dataset (testing set) from Gene Expression Omnibus (GEO). A nomogram containing independent prognostic factors was developed. Differences in BC clinical characteristics, m6A regulators, m6A-related ncRNAs, gene expression patterns, and differentially expressed genes (DEGs)–associated molecular networks between the high- and low-risk groups in TCGA dataset were also analyzed. Additionally, the potential applicability of the risk model in the prediction of immunotherapeutic responsiveness was evaluated based on the “IMvigor210CoreBiologies” data set. Results We identified 183 m6A-related ncRNAs, of which 14 were related to OS. LASSO regression analysis was further used to develop a prognostic risk model that included 10 m6A-related ncRNAs (BAALC-AS1, MIR324, MIR191, MIR25, AC023509.1, AL021707.1, AC026362.1, GATA2-AS1, AC012065.2, and HCP5). The risk model showed an excellent prognostic evaluation performance in both TCGA and GSE154261 datasets, with ROC curve areas under the curve (AUC) of 0.62 and 0.83, respectively. A nomogram containing 3 independent prognostic factors (risk score, age, and clinical stage) was developed and was found to demonstrate high prognostic prediction accuracy (AUC = 0.83). Moreover, the risk model could also predict BC progression. A higher risk score indicated a higher pathological grade and clinical stage. We identified 1058 DEGs between the high- and low-risk groups in TCGA dataset; these DEGs were involved in 3 molecular network systems, i.e., cellular immune response, cell adhesion, and cellular biological metabolism. Furthermore, the expression levels of 8 m6A regulators and 12 m6A-related ncRNAs were significantly different between the two groups. Finally, this risk model could be used to predict immunotherapeutic responses. Conclusion Our study is the first to explore the potential application value of m6A-related ncRNAs in BC. The m6A-related ncRNA–based risk model demonstrated excellent performance in predicting prognosis and immunotherapeutic responsiveness. Based on this model, in addition to identifying high-risk patients early to provide them with focused attention and targeted prevention, we can also select beneficiaries of immunotherapy to deliver personalized medical services. Furthermore, the m6A-related ncRNAs could elucidate the molecular mechanisms of BC and lead to a new direction for the improvement of PPPM for BC.

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The Prognostic Role of m6A-Related Genes in Paediatric Neuroblastoma Patients

Computational and Mathematical Methods in Medicine ◽

10.1155/2022/8354932 ◽

2022 ◽

Vol 2022 ◽

pp. 1-13

Author(s):

Chunyang Zhang ◽

Zhaozheng Ding ◽

Hong Luo

Keyword(s):

Gene Expression ◽

Prognostic Factors ◽

Operating Characteristic ◽

Risk Model ◽

Disease Free Survival ◽

Roc Curves ◽

Risk Groups ◽

Prognostic Role ◽

Target Database

Background and Purpose. N6-Methyladenosine (m6A) is the most abundant methylation modification form in eukaryotic mRNA. Nonetheless, the role of m6A-related genes in neuroblastoma (NB) is unclear. This study attempted to determine the prognostic role of m6A-related genes in NB patients. Methods. The gene expression data were extracted from the “Therapeutically Applicable Research to Generate Effective Treatments” (TARGET) database. The differentially expressed genes (DEGs) were identified, and the relationships between DEGs and m6A genes were explored. Then, the correlations among the m6A genes in neuroblastoma were investigated. Finally, the prognostic role of the m6A genes was studied, and the risk model was constructed. Results. 81 NB patients were extracted from the TARGET database. After comparing the gene expression between unfavorable and favorable outcome groups, 73 DEGs were identified, including 54 downregulated genes and 19 upregulated genes. In NB patients, we found that IGF2BP3, METTL14, and METTL16 are prognostic factors for disease-free survival (DFS) while IGF2BP3, METTL14, and METTL16 are prognostic factors for overall survival (OS). Besides, after the risk model construction, the OS between the two risk groups was drawn (log-rank p = 1.64 e − 08 , HR = 3.438 , 95% CI 2.24-5.278). The 1-, 3-, and 5-year time-dependent receiving operating characteristic (ROC) curves were also illustrated, and the areas under the receiver operating characteristic curves (AUCs) attained 0.75, 0.798, and 0.768, respectively. Conclusions. IGF2BP3, METTL14, and METTL16 were identified as the significant factors for DFS and OS in NB patients.

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A novel prognostic model based on epithelial-mesenchymal transition-related genes predicts patient survival in gastric cancer

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02329-9 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Wanting Song ◽

Yi Bai ◽

Jialin Zhu ◽

Fanxin Zeng ◽

Chunmeng Yang ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Risk Model ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Risk Groups ◽

The Cancer Genome Atlas ◽

Critical Function ◽

Mesenchymal Transition ◽

Patient Prognosis

Abstract Background Gastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have been identified, a precise risk model is still necessary to help doctors determine patient prognosis in GC. Methods Gene expression data and clinical information for GC were acquired from The Cancer Genome Atlas (TCGA) database and 200 EMT-related genes (ERGs) from the Molecular Signatures Database (MSigDB). Then, ERGs correlated with patient prognosis in GC were assessed by univariable and multivariable Cox regression analyses. Next, a risk score formula was established for evaluating patient outcome in GC and validated by survival and ROC curves. In addition, Kaplan-Meier curves were generated to assess the associations of the clinicopathological data with prognosis. And a cohort from the Gene Expression Omnibus (GEO) database was used for validation. Results Six EMT-related genes, including CDH6, COL5A2, ITGAV, MATN3, PLOD2, and POSTN, were identified. Based on the risk model, GC patients were assigned to the high- and low-risk groups. The results revealed that the model had good performance in predicting patient prognosis in GC. Conclusions We constructed a prognosis risk model for GC. Then, we verified the performance of the model, which may help doctors predict patient prognosis.

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Immune Characteristics-Related Typing of Colorectal Cancer and the Establishment of 14-Gene Prognostic Model

10.21203/rs.3.rs-1093264/v1 ◽

2021 ◽

Author(s):

Jianxing Ma ◽

Chen Wang

Keyword(s):

Colorectal Cancer ◽

Risk Model ◽

Cox Regression ◽

Nonnegative Matrix ◽

Test Group ◽

Progression Free Survival ◽

Risk Groups ◽

The Cancer Genome Atlas ◽

Lasso Regression ◽

Immune Related Genes

Abstract This study is to establish NMF (nonnegative matrix factorization) typing related to the tumor microenvironment (TME) of colorectal cancer (CRC) and to construct a gene model related to prognosis to be able to more accurately estimate the prognosis of CRC patients. NMF algorithm was used to classify samples merged clinical data of differentially expressed genes (DEGs) of TCGA that are related to the TME shared in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and survival differences between subtype groups were compared. By using createData Partition command, TCGA database samples were randomly divided into train group and test group. Then the univariate Cox analysis, Lasso regression and multivariate Cox regression models were used to obtain risk model formula, which is used to score the samples in the train group, test group and GEO database, and to divide the samples of each group into high-risk and low-risk groups, according to the median score of the train group. After that, the model was validated. Patients with CRC were divided into 2, 3, 5 subtypes respectively. The comparison of patients with overall survival (OS) and progression-free survival (PFS) showed that the method of typing with the rank set to 5 was the most statistically significant (p=0.007, p<0.001, respectively). Moreover, the model constructed containing 14 immune-related genes (PPARGC1A, CXCL11, PCOLCE2, GABRD, TRAF5, FOXD1, NXPH4, ALPK3, KCNJ11, NPR1, F2RL2, CD36, CCNF, DUSP14) can be used as an independent prognostic factor, which is superior to some previous models in terms of patient prognosis. The 5-type typing of CRC patients and the 14 immune-related genes model constructed by us can accurately estimate the prognosis of patients with CRC.

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A Three Protein-Coding Gene Prognostic Model Predicts Overall Survival in Bladder Cancer Patients

BioMed Research International ◽

10.1155/2020/7272960 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Xiang-hui Ning ◽

Yuan-yuan Qi ◽

Fang-xin Wang ◽

Song-chao Li ◽

Zhan-kui Jia ◽

...

Keyword(s):

Bladder Cancer ◽

Zinc Finger Protein ◽

Risk Model ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Lasso Regression ◽

Protein Coding ◽

Protein Coding Genes ◽

Differentially Methylated Genes

Bladder cancer (BLCA) is the most common urinary tract tumor and is the 11th most malignant cancer worldwide. With the development of in-depth multisystem sequencing, an increasing number of prognostic molecular markers have been identified. In this study, we focused on the role of protein-coding gene methylation in the prognosis of BLCA. We downloaded BLCA clinical and methylation data from The Cancer Genome Atlas (TCGA) database and used this information to identify differentially methylated genes and construct a survival model using lasso regression. We assessed 365 cases, with complete information regarding survival status, survival time longer than 30 days, age, gender, and tumor characteristics (grade, stage, T, M, N), in our study. We identified 353 differentially methylated genes, including 50 hypomethylated genes and 303 hypermethylated genes. After annotation, a total of 227 genes were differentially expressed. Of these, 165 were protein-coding genes. Three genes (zinc finger protein 382 (ZNF382), galanin receptor 1 (GALR1), and structural maintenance of chromosomes flexible hinge domain containing 1 (SMCHD1)) were selected for the final risk model. Patients with higher-risk scores represent poorer survival than patients with lower-risk scores in the training set ( HR = 2.37 , 95% CI 1.43-3.94, p = 0.001 ), in the testing group ( HR = 1.85 , 95% CI 1.16-2.94, p = 0.01 ), and in the total cohort ( HR = 2.06 , 95% CI 1.46-2.90, p < 0.001 ). Further univariate and multivariate analyses using the Cox regression method were conducted in these three groups, respectively. All the results indicated that risk score was an independent risk factor for BLCA. Our study screened the different methylation protein-coding genes in the BLCA tissues and constructed a robust risk model for predicting the outcome of BLCA patients. Moreover, these three genes may function in the mechanism of development and progression of BLCA, which should be fully clarified in the future.

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Venous Thromboembolism Risk Model Predicts Early Progression and Overall Mortality in Cancer Patients Receiving Chemotherapy

Blood ◽

10.1182/blood.v112.11.172.172 ◽

2008 ◽

Vol 112 (11) ◽

pp. 172-172 ◽

Cited By ~ 6

Author(s):

Nicole M. Kuderer ◽

Alok A. Khorana ◽

Charles W. Francis ◽

Eva Culakova ◽

Thomas L. Ortel ◽

...

Keyword(s):

Overall Survival ◽

Prognostic Factors ◽

Venous Thromboembolism ◽

Disease Progression ◽

Cancer Patients ◽

Risk Score ◽

Risk Model ◽

Risk Groups ◽

Group I ◽

All Cause Mortality

Abstract Background: Venous Thromboembolism (VTE) is a common complication of cancer and is strongly associated with early all-cause mortality during the course of cancer chemotherapy (Kuderer et al. ASCO 2008). A clinical model for predicting the risk of VTE in cancer patients initiating chemotherapy has been recently developed and validated (Khorana et al. Blood 2008). Risk of VTE in low (group I), intermediate (group II) and high risk patients (group III) was 0.8%, 1.8% and 7.1%, respectively. The aim of current study is to evaluate the ability of the VTE risk model to predict disease progression and early all-cause mortality. Methods: A prospective study of 4,458 adult cancer patients with solid tumors or malignant lymphoma initiating a new chemotherapy regimen was conducted between 2002 and 2006 at 115 randomly selected practice sites throughout the USA. Demographic, clinical and treatment-related information was captured prospectively at baseline and during the first four cycles of chemotherapy, including rates of documented VTE, disease recurrence and deaths from all causes. Progression-free survival (PFS) and overall survival (OS) within 4 months of starting chemotherapy were estimated by the method of Kaplan-Meier and adjusted hazard ratios (HR ± 95% CI) were estimated by a Cox regression model, incorporating VTE as a time-dependent covariate. Results: Patient age ranged from 18–97 with a mean of 60 years. VTE occurred in 3% of patients by 4 months with a median of 38 days following initiation of chemotherapy. The HR for VTE occurrence among risk score groups II and III, compared to group I, were 3.07 [1.39–6.77] and 11.73 [5.22–16.37], (P<0.0001) respectively. Within 4 months, disease progression occurred in 298 patients and 137 patients died. Death or disease progression was reported in 7%, 18% and 28% of risk score groups I, II and III, respectively. HR for reduced PFS among risk groups II and III compared to group I were 2.77 [1.97–3.87] and 4.27 [2.90–6.27], respectively (P<0.0001). Death from all causes within 4 months of treatment initiation was reported in 1.2%, 5.9% and 12.7% patients for risk groups I, II and III. HR estimates for mortality among groups II and III were 3.56 [1.91–6.66] and 6.89 [3.50–13.57], respectively (P<0.0001). In multivariate analysis, the risk score and VTE occurrence were both significant independent predictors for early mortality and reduced PFS after adjusting for major prognostic factors including: age, stage, cancer type, ECOG performance status, Charlson comorbidity index, body mass index, relative dose intensity, and year of enrollment. Conclusions: VTE is strongly associated with increased early all-cause mortality during the course of cancer chemotherapy. A recently validated risk score is not only predictive of VTE occurrence, but also of progression-free and overall survival demonstrating a strong association with prognostic factors for disease progression and mortality.

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Risk group stratification in patients with micropapillary bladder cancer treated with radical cystectomy and/or neoadjuvant chemotherapy.

Journal of Clinical Oncology ◽

10.1200/jco.2015.33.7_suppl.302 ◽

2015 ◽

Vol 33 (7_suppl) ◽

pp. 302-302

Author(s):

Stephen Bentley Williams ◽

Mario Fernandez ◽

Daniel Levi Willis ◽

Rebecca Slack ◽

Arlene O. Siefker-Radtke ◽

...

Keyword(s):

Risk Factors ◽

Bladder Cancer ◽

Neoadjuvant Chemotherapy ◽

Urothelial Carcinoma ◽

Radical Cystectomy ◽

Clinical Stage ◽

Recursive Partitioning ◽

Risk Groups ◽

Preoperative Risk Factors ◽

Traditional Risk Factors

302 Background: Micropapillary bladder cancer (MPBC) is an aggressive variant of urothelial carcinoma. We have previously published clinical risk stratification groups for patients with conventional urothelial carcinoma and sought to identify if these were valid in patients with this variant histology. Methods: An IRB approved review of 1910 patients in our radical cystectomy database revealed 106 patients with preoperative diagnosis of ≤cT4aN0M0 MPBC between December 1992 and January 2012 who underwent upfront radical cystectomy (RC, n = 74) or neoadjuvant chemotherapy (NAC) followed by RC (n = 32). To determine whether patients with MPBC can be risk stratified using traditional risk factors, a recursive partitioning analysis (RPA) was performed. Results: In multivariate analyses, hydronephrosis (HR=3.1; p=0.01), and extent of MPBC at transurethral resection (TUR) (HR=1.9; p=0.04) were associated with shortened OS. In the reduced model, clinical stage also achieved significance (HR=2.8; p=0.03). Results were similar for DSS: hydronephrosis (HR=2.4, p=0.03), extent of MPBC (HR=2.1, p=0.03) and clinical stage (HR=4.7, p=0.02). Using the RPA analysis, following risk groups were identified according to OS or DSS: 1) cT1 disease with no hydronephrosis; 2) cT2 or higher with no hydronephrosis; or 3) hydronephrosis (with any cT stage). These groups corresponded to a low, intermediate and high-risk groups with 5-year OS and DSS rates of 85% and 91%, 50% and 57% and 16% and 17%, (p<0.001), respectively. We found these risk groups to hold true in those treated with NAC or upfront RC; those who received NAC trended towards better outcomes. Conclusions: In patients with MPBC, preoperative risk factors can help stratify patients into different risk groups similar to what is seen in patients with conventional UC. Presence of hydronephrosis is an especially ominous sign.

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Reprint: Peculiarities of expression of apoptosis markers in the tissues of primary fallopian tubes carcinoma

Biomedical Research and Clinical Reviews ◽

10.31579/2692-9406/084 ◽

2021 ◽

Vol 5 (1) ◽

pp. 01-01

Author(s):

Franklin Unawunwa ◽

Natalia Hyriavenko ◽

Anna Korobchanska ◽

Mykola Lyndin ◽

Vladyslav Sikora

Keyword(s):

Prognostic Factors ◽

Malignant Tumors ◽

Clinical Stage ◽

Immunohistochemical Analysis ◽

Risk Groups ◽

Fallopian Tubes ◽

P53 Expression ◽

Independent Prognostic Marker ◽

Oncological Diseases ◽

Apoptosis Markers

Aim: immunohistochemical analysis of apoptosis markers in the tissue of PFTC. Introduction: Primary fallopian tubes carcinoma is a rare case among oncological diseases of female genital organs, but the mortality rate is rather high. Nowadays, the prognostic factors of this neoplasia are not fully determined. The data on the p53 and bcl2 proteins expression and their use as prognostic factors in patients with malignant tumors of many locations are contradictory. Methods: the study was conducted on 66 samples of fallopian tubes tumor tissue. To study the apoptosis peculiarities of tumor cells the mouse monoclonal antibodies for bcl-2 (clone 100/D5) and p53 (clone SP5) were used. Mathematic calculations were done using Microsoft Excel 2010 with AtteStat 12.0.5. Results: The high expression of p53 was found in patients of all clinical stages. Mutations of p53 increased with spreading of the neoplastic process. Strong correlation of p53 presence in tumor samples and clinical stage of the disease was determined (r=0.77). In contrast to the abovementioned protein the study of bcl-2 showed the moderate negative correlation between this protein and the stage of the disease (r=−0.54). Analysis of the dependence of p53 expression with the presence or absence of lymph nodes metastasis showed a direct correlation between the indicators (r=0.25). Thus the level of p53 expression in patients with N1 was 80.6±2.7% compared with the N0 group (29.7±3.6%). The stage of neoplasia differentiation is in moderate direct correlation with p53 expression (r=0.58) and in inverse with – bcl-2 (r=−0.64). Conclusion: Expression of p53 depends on neoplasia spreading and stage of tumor differentiation. The expression of p53 is an independent prognostic marker for N-status and helps to classify the patients into “risk” groups.

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Construction and Validation of an Autophagy-Related Prognostic Model for Osteosarcoma Patients

Journal of Oncology ◽

10.1155/2021/9943465 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Hu Qian ◽

Ting Lei ◽

Pengfei Lei ◽

Yihe Hu

Keyword(s):

Prognostic Value ◽

Risk Model ◽

Cox Regression ◽

Bone Development ◽

Treatment Strategies ◽

Risk Groups ◽

Low Risk ◽

Lasso Regression ◽

Prognostic Accuracy ◽

Selection Operator

While the prognostic value of autophagy-related genes (ARGs) in OS patients remains scarcely known, increasing evidence is indicating that autophagy is closely associated with the development and progression of osteosarcoma (OS). Therefore, we explored the prognostic value of ARGs in OS patients and illuminate associated mechanisms in this study. When the OS patients in the training/validation cohort were stratified into high- and low-risk groups according to the risk model established using least absolute shrinkage and selection operator (LASSO) regression analysis, we observed that patients in the low-risk group possessed better prognosis ( P < 0.0001 ). Univariate/Multivariate COX regression and subgroup analysis demonstrated that the ARGs-based risk model was an independent survival indicator for OS patients. The nomogram incorporating the risk model and clinical features exhibited excellent prognostic accuracy. GO, KEGG, and GSVA analyses collectively indicated that bone development-associated pathway mediated the contribution of ARGs to the malignance of OS. Immune infiltration analysis suggested the potential pivotal role of macrophage in OS. In summary, the risk model based on 12 ARGs possessed potent capacity in predicting the prognosis of OS patients. Our work may assist clinicians to map out more reasonable treatment strategies and facilitate individual-targeted therapy in osteosarcoma.

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Development and Validation of a Risk Assessment Model for Early Infection during the First 3 Months in Patients with Newly Diagnosed Multiple Myeloma

Blood ◽

10.1182/blood-2020-141735 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 29-30

Author(s):

Yufeng Shang ◽

Weida Wang ◽

Minghui Liu ◽

Xiaoqin Chen ◽

Zhongjun Xia ◽

...

Keyword(s):

Multiple Myeloma ◽

High Risk ◽

Model Building ◽

Risk Model ◽

Conflicts Of Interest ◽

Treatment Strategies ◽

Risk Groups ◽

Assessment Model ◽

Early Infection ◽

Lasso Regression

PurposeEarly infection was an important cause of mortality in patients with multiple myeloma (MM). The study aimed to assess factors affecting early infection and identify patients with high risk developing infection. MethodsDuring January 2010 to June 2019, patients with MM were analyzed, retrospectively. The data was divided into training and independent validation cohort. The least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction, feature selection, and model building. ResultsOf 745 confirmed MM patients, 540 eligible cases were included in final analyses. In total, 165 patients (30.6%) suffered infections, while 110 patients (20.4%) occurred early infections during the first 3 months after diagnosis. Bacteria and the respiratory tract were the most common pathogen and localization of infection, respectively. In training cohort, PS≥2, HGB<100g/L, β2MG≥6.0mg/L and GLB≥80g/L were identified associated with early infections by LASSO regression. Based on the four factors, an early infection risk model of MM (IRMM) was established to define high- and low-risk groups, which showed significantly different rates of infection (35.3% vs. 9.4%,P<0.001, HR=4.381 [95% CI, 2.802-7.221]). IRMM displayed good discrimination (AUC=0.756) and calibration (P=0.94). ConclusionWe determined risk factors for early infection and established a predictive model to help clinicians identify patients with high-risk infection. It can help clinicians to determine whether to adjust monitoring and treatment strategies, or apply prophylactic interventions to high-risk patients. Disclosures No relevant conflicts of interest to declare.

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