ABSTRACT
Objective: To investigate whether the preoperative controlling nutritional status (COUNT) score is a prognostic factor of patients with high-risk factors for early-stage cervical cancer after surgery and concurrent chemoradiotherapy (CCRT). Methods: This was a retrospective study. From July 2017 to March 2021, a total of 354 patients with histologically confirmed FIGO stage IB-IIA cervical cancer undergoing surgery and postoperative CCRT were included at Hunan University of Medicine General Hospital, China. According to receiver operating characteristic (ROC) curve analysis, the patients were divided into a low CONUT score (< 3) group and a high CONUT score (≥ 3) group. Overall survival (OS) was used as the primary outcome measure and disease-free survival (DFS) as the secondary outcome measure. Results: Among the 354 patients, 239(67.5%) were included in the low CONUT score group and 115 (32.5%) in the high CONUT score group. The 3, 5 and 10-year OS rates in the low CONUT score group and high CONUT score group were respectively presenting statistically significant differences (p<0.001). The 3, 5 and 10-year DFS rates in the low CONUT score group and in the high CONUT score group were respectively with statistically significant differences (p<0.001). Multivariate Cox regression analysis showed that CONUT score, histological type, PNI and lymph node metastasis were all independent predictors for OS and DFS (all p<0.05). Conclusion: High preoperative CONUT score indicates poor prognosis of patients with high-risk factors for early-stage cervical cancer after surgery and postoperative CCRT. In clinical practice, consolidation chemotherapy is recommended for patients with high CONUT scores.
ABSTRACT
Background: M2 macrophages play an important role in cancer development. However, the underlying biological fator affecting M2 macrophages infiltration in ovarian cancer (OV) has not been elucidated. Methods: R software v 4.0.0 was used for all the analysis. The expression profile and clinical information of OV patients enrolled in this study were all downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Results: The CIBERSORT algorithm was used to quantify the M2 macrophage infiltration in OV tissue, which was found a risk factor for patients survival. Based on the limma package, a total of 196 DEGs were identified between OV patients with high and low M2 macrophage infiltration, which were defined as M2 macrophages related genes. Finally, the genes PTGFR, LILRA2 and KCNA1 were identified for prognosis model construction, which showed a great prediction efficiency in both training and validation cohorts (Training cohort, 1-year AUC = 0.661, 3-year AUC = 0.682, 8-year AUC = 0.846; Validation cohort, 1-year AUC = 0.642, 3-year AUC = 0.716, 5-year AUC = 0.741). Clinical correlation showed that the riskscore was associated with the worse clinical features. Pathway enrichment analysis showed that in high risk patients, the pathway of epithelial-mesenchymal transition (EMT), TNF-α signaling via NFKB, IL2/STAT5 signaling, apical junction, inflammatory response, KRAS signaling, myogenesis were activated. Moreover, we found that the PTGFR, LILRA2 and KCNA1 were all positively correlated with M2 macrophage infiltration and PTGFR was significantly associated with the pathway of autophagy regulation. Moreover, we found that the low risk patients might be more sensitive to cisplatin, while high risk patient might be more sensitive to axitinib, bexarotene, bortezomib, nilotinib, pazopanib. Conclusions: In this study, we identified the genes associated with M2 macrophage infiltration and developed a model that could effectively predict the prognosis of OV patients.
