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Biology subjects

Hsu, T.-C.

Publications and source records attributed to Hsu, T.-C..

2 recordsLinked to original sources

Hematopoietic Transfer of the Anti-Cancer and Lifespan-Extending Capabilities of A Genetically Engineered Blood System

A causal relationship exists among the aging process, organ decay and dis-function, and the occurrence of various diseases including cancer. A genetically engineered mouse model, termed Klf1K74R/K74R or Klf1(K74R), carrying mutation on the well-conserved sumoylation site of the hematopoietic transcription factor KLF1/ EKLF has been generated that possesses extended lifespan and healthy characteristics including cancer resistance. We show that the healthy longevity characteristics of the Klf1(K74R) mice, as exemplified by their higher anti- cancer capability, are likely gender-, age- and genetic background-independent. Significantly, the anti-cancer capability, in particular that against melanoma as well as hepatocellular carcinoma, and lifespan-extending property of Klf1(K74R) mice could be transferred to wild-type mice via transplantation of their bone marrow mononuclear cells at young age of the latter. Furthermore, NK(K74R) cells carry higher in vitro cancer cell-killing ability than wild type NK cells. Targeted/global gene expression profiling analysis has identified changes of the expression of specific proteins, including the immune checkpoint factors PDCD and CD274, and cellular pathways in the leukocytes of the Klf1(K74R) that are in the directions of anti-cancer and/or anti-aging. This study demonstrates the feasibility of developing a transferable hematopoietic/ blood system for long-term anti-cancer and, potentially, for anti-aging.

cancer biology↗

Predicting the prognosis of non-small cell lung cancer by integrating microarray and clinical data with deep learning

AO_SCPLOWBSTRACTC_SCPLOWNon-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide. Accurate prognostic stratification of NSCLC can become an important clinical reference when designing therapeutic strategies for cancer patients. With this clinical application in mind, we developed a deep neural network (DNN) combining heterogeneous data sources of gene expression and clinical data to accurately predict the prognosis of NSCLC patients. Based on microarray data from a cohort set (614 patients), seven well-known NSCLC markers were used to group patients into marker- and marker+ subgroups. Using a systems biology approach, prognosis relevance values (PRV) were then calculated to select eight additional novel prognostic gene markers. Gene markers along with clinical data were then used to develop an integrative DNN via bimodal learning to predict the 5-year survival rate of NSCLC patients with tremendously high accuracy (AUC: 0.8163, accuracy: 75.44%), which is superior to all other existing methods based on AUC. Using the capability of deep learning, we believe that our predicted cancer prognosis can be a promising index helping oncologists and physicians develop personalized therapy and build the foundation of precision medicine in the future.

bioinformatics↗