Background and aimPrevious studies have suggested that lymph node metastasis (LNM) in early-stage cervical cancer (CESC) may affect the prognosis of patients and the outcomes of subsequent adjuvant therapy. However, research focused on miRNA expression in early-stage CESC patients with LNM remains limited. Therefore, it is necessary to identify prognostic miRNAs and determine their molecular mechanisms.MethodsWe evaluated the differentially expressed genes in early-stage CESC patients with LNM compared to patients without LNM and evaluated the prognostic significance of these differentially expressed genes by analyzing a public dataset from The Cancer Genome Atlas. Potential molecular mechanisms were investigated by gene ontology, the Kyoto Encyclopedia of Genes and Genomes, and protein–protein interaction network analyses.ResultsAccording to the The Cancer Genome Atlas data, hsa-miR-508, hsa-miR-509-2, and hsa-miR-526b expression levels were significantly lower in early-stage CESC patients with LNM than in patients without LNM. A multivariate analysis suggested that three miRNAs were prognostic factors for CESC (P<0.05). The target genes were identified to be involved in the MAPK, cAMP, PI3K/Akt, mTOR, and estrogen cancer signaling pathways. Protein–protein interaction network analysis showed that TP53, MMP1, NOTCH1, SMAD4, and NFKB1 were the most significant hub proteins.ConclusionOur results indicate that hsa-miR-508, hsa-miR-509-2, and hsa-miR-526b may be potential diagnostic biomarkers for early-stage CESC with LNM, and serve as prognostic predictors for patients with CESC.

BackgroundColorectal cancer (CRC) is a common malignant tumor of the digestive tract with a poor prognosis. Cancer stem cells (CSCs) affect disease outcomes and treatment responses in CRC. We developed a circular RNA (circRNA) regulatory stemness-related gene pair (CRSRGP) signature to predict CRC patient prognosis and treatment effects.MethodsThe circRNA, miRNA, and mRNA expression profiles and clinical information of CRC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CRSRGPs were established based on stemness-related genes in the competing endogenous RNA (ceRNA) network. A CRSRGP signature was generated using the least absolute shrinkage and selection operator (Lasso) and Cox regression analysis of TCGA training set. The prognosis was predicted by generating a nomogram integrating the CRSRGP signature and clinicopathologic features. The model was validated in an external validation set (GSE17536). The antitumor drug sensitivity and immunotherapy responses of CRC patients in the high-risk group (HRG) and low-risk group (LRG) were evaluated by the pRRophetic algorithm and immune checkpoint analysis.ResultsWe established an 18-CRSRGP signature to predict the prognosis and treatment responses of CRC patients. In the training and external validation sets, risk scores were used to categorize CRC patients into the HRG and LRG. The Kaplan–Meier analysis showed a poor prognosis for patients in the HRG and that subgroups with different clinical characteristics had significantly different prognoses. A multivariate Cox analysis revealed that the CRSRGP signature was an independent prognostic factor. The nomogram integrating clinical features and the CRSRGP signature efficiently predicted CRC patient prognosis, outperformed the current TNM staging system, and had improved practical clinical value. Anticancer drug sensitivity predictions revealed that the tumors of patients in the HRG were more sensitive to pazopanib, sunitinib, gemcitabine, lapatinib, and cyclopamine. Analysis of immune checkpoint markers demonstrated that patients in the HRG were more likely to benefit from immunotherapy.ConclusionAn efficient, reliable tool for evaluating CRC patient prognosis and treatment response was established based on the 18-CRSRGP signature and nomogram.