-
Categories
-
Pharmaceutical Intermediates
-
Active Pharmaceutical Ingredients
-
Food Additives
- Industrial Coatings
- Agrochemicals
- Dyes and Pigments
- Surfactant
- Flavors and Fragrances
- Chemical Reagents
- Catalyst and Auxiliary
- Natural Products
- Inorganic Chemistry
-
Organic Chemistry
-
Biochemical Engineering
- Analytical Chemistry
-
Cosmetic Ingredient
- Water Treatment Chemical
-
Pharmaceutical Intermediates
Promotion
ECHEMI Mall
Wholesale
Weekly Price
Exhibition
News
-
Trade Service
Accurate prediction of survival in patients with acute myeloid leukemia (AML) is important
for treatment decisions.
The research team examined RNA matrix data from 1611 AML patients extracted from a common database in a training queue and three validation queues
.
The model has robust prognostic accuracy in the training and validation queues, with a five-year area of 0.
Figure 1: A prognostic TFG risk score model for AML patients is constructed
.
In multivariate analysis, the high-risk score independently predicted shorter survival, with a death-to-risk ratio in the range
of 1.
Figure 2: Prognostic potential
of the risk model.
Figure 3: Biological phenotypes associated with TFG immune risk scores
In scRNA-seq analysis, most of the genes in the signature are transcribed
in leukemia cells.
Figure 4: Single cell profiling of bone marrow cells in AML patients
Figure 5: Establishment of a joint line plot to predict OS
in newly diagnosed AML patients.
Overall, the study used data on gene expression related to T-cell function to develop a model that can accurately predict the survival
of AML patients.
Original Source:
Wang Y, Chen S, Chi P, Nie R, Gale RP, Huang H, Chen Z, Cai Y, Yan E, Zhang X, Zhong N, Liang Y.
