Non-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 overall survival of NSCLC patients. Based on microarray data from a cohort set (614 patients), seven well-known NSCLC biomarkers were used to group patients into biomarker- and biomarker+ subgroups. Then, by using a systems biology approach, prognosis relevance values (PRV) were then calculated to select eight additional novel prognostic gene biomarkers. Finally, the combined 15 biomarkers along with clinical data were then used to develop an integrative DNN via bimodal learning to predict the 5-year survival status of NSCLC patients with tremendously high accuracy (AUC: 0.8163, accuracy: 75.44%). Using the capability of deep learning, we believe that our prediction can be a promising index that helps oncologists and physicians develop personalized therapy and build the foundation of precision medicine in the future.

Epithelial–mesenchymal transition (EMT)/mesenchymal–epithelial transition (MET) processes are proposed to be a driving force of cancer metastasis. By studying metastasis in bone marrow-derived mesenchymal stem cell (BM-MSC)-driven lung cancer models, microarray time-series data analysis by systems biology approaches revealed BM-MSC-induced signaling triggers early dissemination of CD133⁺/CD83⁺ cancer stem cells (CSCs) from primary sites shortly after STAT3 activation but promotes proliferation towards secondary sites. The switch from migration to proliferation was regulated by BM-MSC-secreted LIF and activated LIFR/p-ERK/pS727-STAT3 signaling to promote early disseminated cancer cells MET and premetastatic niche formation. Then, tumor-tropic BM-MSCs circulated to primary sites and triggered CD151⁺/CD38⁺ cells acquiring EMT-associated CSC properties through IL6R/pY705-STAT3 signaling to promote tumor initiation and were also attracted by and migrated towards the premetastatic niche. In summary, STAT3 phosphorylation at tyrosine 705 and serine 727 differentially regulates the EMT–MET switch within the distinct molecular subtypes of CSCs to complete the metastatic process.

Deep learning models have been shown to be capable of extracting high-level representations from the increasing amount of customer-level data generated via fast-growing financial activities. In financial data, dynamic features that evolve with time are commonly observed. However, such time dependencies are often ignored in classical classification models. In this study, we propose to learn a Recurrent Neural Network (RNN) feature extractor with GRU on credit card payment history to leverage the time dependencies embedded in these dynamic features. Input sequences are first preprocessed by this feature extractor. The extracted dynamic features along with the static features are then utilized to train an enhanced RNN model (RNN-RF) to predict credit card client defaults. Numerical experiments confirmed that the enhanced RNN predictor indeed provides the best performance in both lift index (0.659) and AUC (0.782) compared to the other benchmark models. The proposed model allows us to effectively combine static and dynamic features to provide superior predictive performance for financial data.

Regeneration is an important biological process for the restoration of organ mass, structure, and function after damage, and involves complex bio-physiological mechanisms including cell differentiation and immune responses. We constructed four regenerative protein-protein interaction (PPI) networks using dynamic models and AIC (Akaike’s Information Criterion), based on time-course microarray data from the regeneration of four zebrafish organs: heart, cerebellum, fin, and retina. We extracted core and organ-specific proteins, and proposed a recalled-blastema-like formation model to uncover regeneration strategies in zebrafish. It was observed that the core proteins were involved in TGF-β signaling for each step in the recalled-blastema-like formation model and TGF-β signaling may be vital for regeneration. Integrins, FGF, and PDGF accelerate hemostasis during heart injury, while Bdnf shields retinal neurons from secondary damage and augments survival during the injury response. Wnt signaling mediates the growth and differentiation of cerebellum and fin neural stem cells, potentially providing a signal to trigger differentiation. Through our analysis of all four zebrafish regenerative PPI networks, we provide insights that uncover the underlying strategies of zebrafish organ regeneration.

Accurate cancer patient prognosis stratification is essential for oncologists to recommend proper treatment plans. Deep learning models are capable of providing good prediction power for such stratification. The main challenge is that only a limited number of labeled patients are available for cancer prognosis. To overcome this, we proposed Wasserstein Generative Adversarial Network-based Deep Adversarial Data Augmentation (wDADA) that leverages generative adversarial networks to perform data augmentation and assist in model training. We used the proposed framework to train our model for predicting disease-specific survival (DSS) of breast cancer patients from the METABRIC dataset. We found that wDADA achieved 0.6726± 0.0278, 0.7538±0.0328, and 0.6507 ±0.0248 in terms of accuracy, AUC, and concordance index in predicting 5-year DSS, respectively, which is comparable to our previously proposed Bimodal model (accuracy: 0.6889±0.0159; AUC: 0.7546± 0.0183; concordance index: 0.6542±0.0120), which needs careful calibration and extensive search on pre-trained network architectures. The flexibility of the proposed wDADA allows us to incorporate it with ensemble learning and semi-supervised learning to further improve performance. Our results indicate that it is possible to utilize generative adversarial networks to train deep models in medical applications, wherein only limited data are available.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer mortality. Cancer stem cells (CSCs) are responsible for the maintenance, metastasis, and relapse of various tumors. The effects of CSCs on the tumorigenesis of HCC are still not fully understood, however. We have recently established two new rat HCC cell lines HTC and TW-1, which we isolated from diethylnitrosamine-induced rat liver cancer. Results showed that TW-1 expressed the genetic markers of CSCs, including CD133, GSTP1, CD44, CD90, and EpCAM. Moreover, TW-1 showed higher tolerance to sorafenib than HTC did. In addition, tumorigenesis and metastasis were observed in nude mice and wild-type rats with TW-1 xenografts. Finally, we combined highly expressed genes in TW-1/HTC with well-known biomarkers from recent HCC studies to predict HCC-related biomarkers and able to identify HCC with AUCs > 0.9 after machine learning. These results indicated that TW-1 was a novel rat CSC line, and the mice or rat models we established with TW-1 has great potential on HCC studies in the future.