Gastrointestinal cancer is one of the most malignant tumors with high morbidity and mortality, especially colorectal cancer, which has become the second leading cause of cancer-related deaths worldwide. Targeted drug treatment and precise endoscopic resection can significantly improve the overall survival rate and greatly extend the life span. Promising biomedical applications of hydrogels would represent hopeful therapeutic alternatives for patients with different kinds of diseases, particularly providing precise therapy for cancer patients. Although the intersection field of material science and biomedical science has made tremendous advances, major challenges remain. In this review, the application of hydrogel-based technology in cancer precision medicine is the focus of attention, which is the development trend of multidisciplinary cooperation in the future. First, we provide the current clinical landscape of hydrogel applications, and then we highlight precision oncology, including personalized drug treatment and accurate endoscopic intervention. Finally, we discuss major challenges for their clinical translation that have not yet been overcome and future perspectives on cancer precision medicine.
Acute myeloid leukemia (AML) is a malignant hematological malignancy with a poor prognosis. Risk stratification of patients with AML is mainly based on the characteristics of cytogenetics and molecular genetics; however, patients with favorable genetics may have a poor prognosis. Here, we focused on the activity changes of immunologic and hallmark gene sets in the AML population. Based on the enrichment score of gene sets by gene set variation analysis (GSVA), we identified three AML subtypes by the nonnegative matrix factorization (NMF) algorithm in the TCGA cohort. AML patients in subgroup 1 had worse overall survival (OS) than subgroups 2 and 3 (
P
<
0.001
). The median overall survival (mOS) of subgroups 1–3 was 0.4, 2.2, and 1.7 years, respectively. Clinical characteristics, including age and FAB classification, were significantly different among each subgroup. Using the least absolute shrinkage and selection operator (LASSO) regression method, we discovered three prognostic gene sets and established the final prognostic model based on them. Patients in the high-risk group had significantly shorter OS than those in the low-risk group in the TCGA cohort (
P
<
0.001
) with mOS of 2.2 and 0.7 years in the low- and high-risk groups, respectively. The results were further validated in the GSE146173 and GSE12417 cohorts. We further identified the key genes of prognostic gene sets using a protein-protein interaction network. In conclusion, the study established and validated a novel prognostic model for risk stratification in AML, which provides a new perspective for accurate prognosis assessment.
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