Colorectal cancer stands as a leading cause of cancer-related morbidity and mortality globally,with liver metastases being a significant determinant of patient prognosis.Conventional diagnostic methods,includ-ing imaging studies and biomarker testing,frequently exhibit inadequate sensitivity and specificity,underscoring the necessity for more advanced technologies.Recent advancements in genomics,transcriptomics,proteomics,me-tabolomics,and epigenomics have revolutionized our understanding of the biological mechanisms driving colorectal cancer.These methodologies enable comprehensive analyses of genetic mutations,gene expression profiles,protein modifications,and metabolic reprogramming,all of which are pivotal to the metastatic process.This article high-lights the advanced capabilities of artificial intelligence(AI)technologies in processing complex multi-omics data,thereby enhancing diagnostic accuracy and supporting personalized treatment strategies.It also addresses the challenges AI encounters in multi-omics analyses,such as ensuring data quality,improving model interpretability,and facilitating clinical translation.Additionally,it explores the potential integration of emerging technologies like single-cell sequencing and spatial omics into large-scale,multicenter studies to further enhance the clinical utility of these tools.

Colorectal cancer stands as a leading cause of cancer-related morbidity and mortality globally,with liver metastases being a significant determinant of patient prognosis.Conventional diagnostic methods,includ-ing imaging studies and biomarker testing,frequently exhibit inadequate sensitivity and specificity,underscoring the necessity for more advanced technologies.Recent advancements in genomics,transcriptomics,proteomics,me-tabolomics,and epigenomics have revolutionized our understanding of the biological mechanisms driving colorectal cancer.These methodologies enable comprehensive analyses of genetic mutations,gene expression profiles,protein modifications,and metabolic reprogramming,all of which are pivotal to the metastatic process.This article high-lights the advanced capabilities of artificial intelligence(AI)technologies in processing complex multi-omics data,thereby enhancing diagnostic accuracy and supporting personalized treatment strategies.It also addresses the challenges AI encounters in multi-omics analyses,such as ensuring data quality,improving model interpretability,and facilitating clinical translation.Additionally,it explores the potential integration of emerging technologies like single-cell sequencing and spatial omics into large-scale,multicenter studies to further enhance the clinical utility of these tools.

Background:Whether the ABO blood group is associated with the survival of patients with laryngeal cancer remains unknown. The purpose of this study was to investigate the association between the ABO blood group and clinico?pathologic characteristics of patients with laryngeal cancer and assess whether the ABO blood group was associated with prognosis. Methods:We analyzed the records of 1260 patients with laryngeal cancer who underwent curative treatment at Sun Yat?sen University Cancer Center between January 1993 and December 2009. The Chi?square test was used to assess the relationship between the ABO blood group and clinicopathologic characteristics. The Kaplan–Meier method was used to estimate 3?, 5?, and 10?year overall survival (OS) rates. The Cox proportional hazards model was used in univariate and multivariate analyses of OS. Results:No signiifcant association was found between the ABO blood group and clinicopathologic characteristics except for primary tumor site. The median OS for patients with blood groups A, B, AB, and O were 87.0, 80.0, 90.0, and 72.5months, respectively. The 3?, 5?, and 10?year OS rates were 82.4%, 76.0%, and 67.5% for patients with blood group A; 77.4%, 69.8%, and 58.4% for patients with blood group B; 82.2%, 73.1%, and 65.6% for patients with blood group AB; and 71.7%, 66.4%, and 55.5% for patients with blood group O, respectively. Univariate and multivariate analyses showed that the ABO blood group had signiifcant effects on prognosis in patients with laryngeal cancer. Conclusions:The ABO blood group is associated with survival in patients with laryngeal cancer. Patients with blood group O had signiifcantly shorter OS than patients with other ABO blood groups.

ObjectiveTo examine the relationship between HIF-1αt and VEGF expression and the clinicopathological characteristics in hypopharyngeal squamous cell carcinoma. Methods The expression status of HIF-1α and VEGF were examined by immunohistochemistric method (IHC) in 62 tumor tissue and 19 paracancerous normal tissue.The relationships between the expression of HIF-1α and VEGF and clinicopathological characteristic were analyzed. Results HIF-1αt and VEGF expression were higher in hypopharyngeal carcinoma tissues than those in paracancerous normal tissues (66. 1％:26. 3％ ;x2 =18. 02,P ＜0. 05 ;67. 7％ : 31.6％ ; x2 =19.22, P ＜ 0. 05 ). The expression intensity of HIF-1 α was related to T stage, N stage and TNM stage ( x2 =4. 23,5.83,9.94,all P ＜0. 05). The expression intensity of VEGF was related to metastasis, T stage, N stage and TNM stage (x2 =5.62,7. 38,15.75,4. 29 ,all P ＜0. 05 ). There was minus relationship between overall survival and expression level of HIF-1 α and/or VEGF (x2 =29. 25, P＜0.01; x2 =24.88, P＜ 0.01 ).On multivariate analysis,HIF-1α expression and T stage were independent prognostic factors for overall survival (x2 =4.80,5.74, all P＜0. 05).ConclusionsHIF-1α and VEGF may be considered as a parameter in evaluation of progression, metastasis and prognosis of hypopharyngeal carcinoma and also may be a direction of molecular target therapy.

Objective To analyse the prognosis of 117 newly diagnosed nasopharyngeal carcinoma (NPC) patients underwent intensity modulated radiotherapy (IMRT). Methods From Jan to Nov 2005, 117 NPC patients who were treated by IMRT were enrolled. There were 81 males and 36 females with a median age of 42 years (range 18-76 years). According to Chinese Fuzhou Staging system(1992), 11 cases were Stage I , 15 Stage Ⅱ, 54 Stage Ⅲ and 37 Stage ⅣA. IMRT was carried out with Peacock plan. The prescription dose to the gross target volume(GTVnx) of nasopharyngeal tumor was 68 Gy, that of positive neck lymph nodes (GTVnd) was 60-66 Gy, clinical target volume 1 (CTV1) was 60 Gy, and CTV2 was 54 Gy. Results After a median follow-up time of 48 months (range 10.5-59.5 months), the 3-and 5-year overall survival (OS) rates were 95.7 % and 89.7 %, the disease-free survival (DFS) rates were 91.5 % and 87.2%, and the local-regional control rates were 94.0 % and 91.5 %. Univariate analysis showed the KPS, stage, Fuzhou clinical stage, status of blood platelet before treatment and uric acid after treatment were correlated with OS rate. T stage was the only independent factor of prognosis in the COX stepwise regression model. Conclusion Radical IMRT significantly prolongs the survival of NPC patients. T stage is the only independent prognostic factor for NPC patients.