Weijie Tian’s research while affiliated with Henan Provincial People’s Hospital and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (2)


The relationship of age and the cancer specific mortality of CC fit by univariate Cox regression with RCS analyses. The Cox regression indicated a nonlinear relationship between age and the risk of death(p < 0.001), with the lowest risk of mortality at age = 35.58 years. When the age was older than 35.58 years, HR was positively correlated with age, whereas negative correlations between age and HR were identified when age was less than 35.58 years. When age was older than 60 years, the risk of death increased substantial with increasing age. Shaded areas represent 95% CI.
Kaplan-Meier curves of OS (a) and CSS (b) for 3 groups of CC patients.
Forest plots for OS (a) and CSS (b) of young CC patients based on multivariate Cox regression analysis of the training cohort.
Nomograms for predicting OS (a) and CSS (b) of young CC patients.
Calibration plots of the models for predicting 3-, and 5-year OS and CSS of the development cohort (a–d) and 3-, and 5-year OS of external validation cohort (e, f).

+1

Construction and validation of prognostic models for young cervical cancer patients: age stratification based on restricted cubic splines
  • Article
  • Full-text available

November 2024

·

3 Reads

Yuan Gong

·

Feifei Gou

·

Qingfeng Qin

·

[...]

·

Dan Zi

Cervical cancer (CC) ranks as the second highest cause of morbidity and mortality among young women; however, there are currently no age-specific definitions for young cervical cancer or prognostic models tailored to this demographic. Data on CC diagnosed between 2000 and 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Age stratification is based on the relationship between age and cancer-specific mortality, as demonstrated by restricted cubic spline analyses (RCS). Cox proportional hazards regression analyses were employed to identify independent prognostic factors in the young CC group. Two novel nomograms for this population were developed and validated using an external validation cohort obtained from a local hospital database, evaluated with concordance index (C-index) and calibration plots. Receiver operating characteristic (ROC) curves were utilized to compare the accuracy of the established models against the International Federation of Gynaecology and Obstetrics (FIGO) staging system (2018). A total of 27,658 patients from the SEER database were classified into three age groups (<36 years, 36-60 years, >60 years) based on RCS analyses, with 4,990, 16,922, and 5,746 patients in each group, respectively. The independent prognostic factors identified for young CC included stage, tumour size, grade, histologic type, and surgical intervention. The results of the C-index and calibration in both the training and validation sets confirmed that the two nomograms can accurately predict the occurrence and prognosis of young CC patients. The area under the curve (AUC) values indicated that these models demonstrated higher efficacy in predicting overall survival (OS) compared to the FIGO staging system (2018). These models could potentially serve as effective tools for clinicians to estimate the prognosis of young CC patients. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-81644-z.

Download

Fig. 1 Analysis of the workflow of this study
Fig. 7 The Nomogram for predicting overall survival of CC patients. A The Nomogram integrating the signature risk score with the clinical characteristics for predicting OS. B The calibration curve for the Nomogram in TCGA cohort for predicting 3-year overall survival. C The calibration curve for the Nomogram in TCGA cohort for predicting 5-year overall survival
Fig. 8 Principal components analysis (PCA) and gene set enrichment analysis (GSEA). A PCA plot showing high-risk group and low-risk groups based on the immune-related gene sets. B PCA plot showing high-risk group and low-risk group based on the whole protein-coding gene sets. C, D GSEA implied remarkable enrichment of immune-related phenotype in the low-risk group;
Fig. 9 Differential chemotherapeutic responses of 6 drugs in low-and high-risk CC patients (A-F)
Immune-related LncRNAs scores predicts chemotherapeutic responses and prognosis in cervical cancer patients

April 2024

·

6 Reads

Discover Oncology

Background Long non-coding RNAs (LncRNAs) regulating the immune microenvironment of cancer is a hot spot. But little is known about the influence of the immune-related lncRNA (IRlncRs) on the chemotherapeutic responses and prognosis of cervical cancer (CC) patients. The purpose of the study was to identify an immune-related lncRNAs (IRlncRs)-based model for the prospective prediction of clinical outcomes in CC patients. Methods CC patients’ relevant data was acquired from The Cancer Genome Atlas (TCGA). Correlation analysis and Cox regression analyses were applied. A risk score formula was formulated. Prognostic factors were combined into a nomogram, while sensitivity for chemotherapy drugs was analyzed using the OncoPredict algorithm. Results Eight optimal IRlncRs(ATP2A1-AS1, LINC01943, AL158166.1, LINC00963, AC009065.8, LIPE-AS1, AC105277.1, AC098613.1.) were incorporated in the IRlncRs model. The overall survival (OS) of the high-risk group of the model was inferior to those in the low-risk group. Further analysis demonstrated this eight-IRlncRs model as a useful prognostic marker. The Nomogram had a concordance index of survival prediction of 0.763(95% CI 0.746–0.780) and more robust predictive accuracy. Furthermore, patients in the low-risk group were found to be more sensitive to chemotherapy, including Paclitaxel, Rapamycin, Epirubicin, Vincristine, Docetaxel and Vinorelbine. Conclusions An eight-IRlncRs-based prediction model was identified that has the potential to be an important tool to predict chemotherapeutic responses and prognosis for CC patients.