ObjectiveCombined with long non-coding RNA (lncRNA) to find a regression model that can be used to predict the survival rate of patients with colon cancer before operation.MethodsThe clinical information and gene expression information of patients with colon cancer were downloaded by using TCGA database. The differentially expressed lncRNAs in tumor and paracancerous tissues were screened out, and then combined with the clinical information of patients to construct Cox proportional hazard regression model.ResultsA total of 26 kinds of lncRNAs with statistical difference in gene expression between paracancerous tissues and tumor tissues were selected (P<0.05). Through repeated screening and comparison of prediction efficiency, the prediction model was finally selected, which was constructed by patients’ age, M stage, N stage, and three kinds of lncRNAs (ZFAS1, SNHG25, and SNHG7) gene expression level: age [HR=4.00, 95%CI: (1.48, 10.84), P=0.006], M stage [HR=3.96, 95%CI: (2.23, 7.04), P<0.001], N stage [HR=1.87, 95%CI: (1.24, 2.84), P=0.003], ZFAS1 gene expression level [HR=0.60, 95%CI: (0.41, 0.86), P=0.006], SNHG25 gene expression level [HR=0.85, 95%CI: (0.73, 1.00), P=0.045], and SNHG7 gene expression level [HR=2.32, 95%CI: (1.53, 3.52), P<0.001] were all independent risk factors for postoperative survival of patients with colon cancer. The area under the ROC curves for predicting 1, 3, and 5-year overall survival were 0.802, 0.828, and 0.771, respectiely, which had a good prediction ability.ConclusionThe predictive model constructed by the combination of ZFAS1, SNHG25, SNHG7 genes expression level with M stage, N stage, and age can better predict the overall survival rate of patients before operation, which can effectively guide clinical decision-making and choose the most suitable treatment method for patients.
[Abstract]High-grade histologic subtypes of lung adenocarcinoma, such as micropapillary and solid patterns, are characterized by high invasiveness, increased risk of recurrence, and poor prognosis. Early preoperative identification of these subtypes is crucial for achieving individualized treatment and improving clinical outcomes. This review summarizes the clinical features, imaging manifestations, molecular mechanisms, and diagnostic advances related to these aggressive patterns. Studies have shown that micropapillary and solid subtypes are more common in male smokers, often present as solid nodules, and demonstrate strong predictive value in FDG-PET metabolic parameters and CT-based radiomics models. At the molecular level, EGFR mutations are more frequently observed in micropapillary types, whereas solid subtypes are often associated with high PD-L1 expression and TP53 mutations, indicating distinct therapeutic strategies for targeted and immunotherapies. In addition, serum markers such as CEA and CYFRA21-1, along with inflammatory indices like NLR and SII, may serve as auxiliary tools for subtype identification. Histologic subtypes of lung adenocarcinoma are evolving from descriptive classifications into critical determinants of treatment decisions and precision management. Clinicians should incorporate comprehensive histologic evaluation into individualized therapeutic planning. Multimodal integration technologies, combined with artificial intelligence algorithms, are advancing the accurate preoperative prediction and management of high-risk subtypes, thereby facilitating early diagnosis and stratified treatment of lung adenocarcinoma.