Yusha Chen

APLN: A potential novel biomarker for cervical cancer

Apelin (APLN) is recently demonstrated a direct association with many malignant diseases. However, its effects on cervical cancer remain unclear. This study therefore aims to evaluate the association between APLN expression and cervical cancer using publicly available data from The Cancer Genome Atlas (TCGA). The Pearson χ2 test and Fish exact test, as well as logistic regression, were used to evaluate the relationship between clinicopathological factors in cervical cancer and the expression of APLN. Additionally, the Cox regression and Kaplan-Meier methods were conducted to analyze the Overall Survival (OS) of cervical cancer patients in TCGA. Finally, gene set enrichment analysis (GSEA) was performed to establish its biological functions. High expression of APLN in cervical cancer was significantly associated with a more advanced clinical stage (OR = 1.91 (1.21–3.05) for Stage II, Stage III, and Stage IV vs Stage I, p = 0.006). Additionally, it was associated with poor outcome after primary therapy (OR = 2.14 (1.03–4.59) for Progressive Disease (PD), Stable Disease (SD), and Partial Response (PR) vs Complete Remission (CR), p = 0.045) and high histologic grade (OR = 1.67 (1.03–2.72) for G3 and G4 vs G1 and G2, p = 0.037). Moreover, multivariate analysis showed that high expression of APLN was associated with a shorter OS. GSEA demonstrated that six KEGG pathways, including PPAR signaling, ECM-receptor interaction, focal adhesion, MAPK signaling, TGF-beta signaling, and Gap junction pathways were differentially enriched in the high expression APLN phenotype. The recent study suggests that APLN plays an important role in the progression of cervical cancer and might be a promising prognostic biomarker of the disease.

Prevalence of multiple human papillomavirus infections and association with cervical lesions among outpatients in Fujian, China: A cross‐sectional study

AbstractMultiple human papillomavirus (HPV) infections are common, but their impact on cervical lesions remains controversial. A total of 6225 female patients who underwent colposcopies/conization following abnormal cervical cancer screening results were included in the study. The final pathological diagnosis was determined by the most severe pathological grade among the cervical biopsy, endocervical curettage, and conization. Univariate and multivariate logistic regression analyses were used to investigate the association between multiple HPV infections and cervical lesions, adjusting for age, HPV genotype, gravidity and parity. In total, 33.3% (n = 2076) of the study population was infected with multiple HPV genotypes. Multiple HPV infections were more prevalent in patients younger than 25 years and older than 55 years, with the rate of multiple HPV infections at 52.8% and 44.3%, respectively. HPV16\52\18\58 are the most common HPV genotypes and usually appear as a single infection. Compared to single HR‐HPV infection, multiple HR‐HPV infections do not increase the risk of HSIL+, while single HR‐HPV coinfected with LR‐HPV seems to reduce the risk of HSIL+ (odds ratio = 0.515, confidence interval: 0.370–0.719, p < 0.001). Multiple HR‐HPV infections cannot be risk‐stratified for triage of HR‐HPV‐positive women.

A Prognostic Nomogram for Predicting Overall Survival in Patients With Small-Cell Carcinoma of the Uterine Cervix: A SEER Population-Based Study

Background: This study aimed to develop a prognostic model based on the Surveillance, Epidemiology, and End Results (SEER) database to predict the overall survival (OS) of small cell carcinoma of the uterine cervix (SmCC). Methods: Between 1975 and 2016, a total of 401 patients were included, and their comprehensive sociodemographic and clinicopathological characteristics were collected. Univariate and multivariate Cox regression models were used to screen for independent prognostic factors. The identified factors were used to conduct a nomogram for predicting the OS of SmCC. The performance of the nomogram was determined using area under the receiver operating characteristic curve (AUC), concordance index (C-index), calibration curve, and decision curve analysis (DCA) metrics. Results: The median survival time of all patients was about 24 months (95% confidence interval [95% CI] [1.50-2.17]). Age (hazard ratio [HR] = 1.693 for 45-59 vs 21-34, 95% CI [1.140-2.513], P = .009; HR = 2.836 for 60-92 vs 21-34, 95% CI [1.851-4.345], P < .001), positive nodes (HR = 2.384, 95% CI [1.437-3.955], P < .001), regional nodes number ≥12 (HR = 0.500, 95% CI [0.282-0.886], P = .018), and treatment method (HR = 0.409 for surgery vs no, 95% CI [0.267-0.628], P < .001; HR = 0.649 for chemotherapy vs no, 95% CI [0.478-0.881)], P = .006) were independent factors of OS. Young patients who had surgical resection or chemotherapy, negative lymph nodes, and regional lymph nodes ≥12 had a longer survival time. These clinical factors were utilized to construct a nomogram for predicting OS. The AUC and C-index were higher than 0.7, indicating the good discriminating ability of the nomogram. The calibrations were all around the 45-degree line, indicating excellent consistency between the prediction of the model and actual observations. The DCA plots supported the clinical utility of the nomogram. Conclusion: The constructed nomogram is expected to help predict the prognosis of SmCC and guide patient treatment.