Background Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a large number of genes using the fully connected dense deep neural network layers, which are hard to interpret. On the other hand, cancer signaling pathways are important and interpretable concepts that define the signaling cascades regulating cancer development and drug resistance. Thus, it is important to investigate potential associations between patient survival and individual signaling pathways, which can help domain experts to understand deep learning models making specific predictions. Results In this exploratory study, we proposed to investigate the relevance and influence of a set of core cancer signaling pathways in the survival analysis of cancer patients. Specifically, we built a simplified and partially biologically meaningful deep neural network, DeepSigSurvNet, for survival prediction. In the model, the gene expression and copy number data of 1967 genes from 46 major signaling pathways were integrated in the model. We applied the model to four types of cancer and investigated the influence of the 46 signaling pathways in the cancers. Interestingly, the interpretable analysis identified the distinct patterns of these signaling pathways, which are helpful in understanding the relevance of signaling pathways in terms of their application to the prediction of cancer patients’ survival time. These highly relevant signaling pathways, when combined with other essential signaling pathways inhibitors, can be novel targets for drug and drug combination prediction to improve cancer patients’ survival time. Conclusion The proposed DeepSigSurvNet model can facilitate the understanding of the implications of signaling pathways on cancer patients’ survival by integrating multi-omics data and clinical factors.

The risk of Alzheimer's disease (AD) in women is about 2 times greater than in men. The estrogen hypothesis is being accepted as the essential sex factor causing the sex difference in AD. Also, the recent meta-analysis using large-scale medical records data indicated estrogen replacement therapy. However, the underlying molecular targets and mechanisms explaining this sex difference in AD disease development remain unclear. In this study, we identified that estrogen treatment can strongly inhibition of neuro-inflammation signaling targets, using the systems pharmacology model; and identified ESR1/ESR2 (the receptors of estrogen) are topologically close to the neuroinflammation biomarker genes using signaling network analysis. Moreover, the estrogen level in women decreased to an extremely lower level than in men after age 55. Pooling together the multiple pieces of evidence, it is concluded that the loss of estrogen unleashing neuro-inflammation increases the women's risk of Alzheimer's disease. These analysis results provide novel supporting evidence explaining the potential mechanism of the anti-neuroinflammation role of estrogen causing the sex difference of AD. Medications boosting the direct downstream signaling of ESR1/ESR2, or inhibiting upstream signaling targets of neuroinflammation, like JAK2 inhibitors, on the signaling network can be potentially effective or synergistic combined with estrogen for AD prevention and treatment.