We hypothesize that instead of an individual gene, two functionally imbalanced groups of genes in lung cancer cells determine the fate of the tumor cells, which ultimately Atropine sulfate determines patient’s survival time. Accurate identification of the Yin and Yang genes in tumor development can be used to develop a prognostic signature. Pathway and interaction network analyses of these 74 genes allowed selecting two main networks that are related to tumor morphology and DNA replication. These networks participate in the canonical Molecular Mechanisms of Cancer pathway. These networks contain 31 genes whose gene symbol names matched the Affymetrix U95 AV2 probe set identifiers. We selected these 31 genes as Yin gene candidates. The 108 downregulated genes constituted two main networks related to maintenance and cellular development processes. The RAR Activation pathway and the Hepatic Stellate Cell Activation pathway invoked by Yang genes exert a wide variety of effects on tissue homeostasis, cell proliferation, differentiation, and apoptosis. There is evidence that lung tissue harbors Hepatic Stellate-like cells which are vitamin-Astoring lung cells. We retrieved the focus genes from the networks that involved cell maintenance and cellular development process resulting in two gene groups. These two groups were combined, resulting in 32 unique genes in total. We defined these 32 genes as Yang gene candidates for signature development. In this study, we developed a new survival prediction signature called YMR for lung cancer. The YMR value of individual patients can provide valuable biomarker information relevant to lung cancer prognosis and therapeutic decision-making. In a clinical setting, the ideal prediction model should be applicable to any single patient by providing an informative risk score for that patient. The major shortcoming of all previous prediction models is that the signature gene-expression values of new samples have to be comparable to those of the training sample data in terms of data preprocessing, analysis platform, and data normalization. For example, Shedden K et al normalized the entire training and testing data sets together. This is not practical for clinical use. Additionally, global normalization may remove some inter-site differences. Even though using a small number of genes by qRT-PCR would be more practical, qRT-PCR data also needs to be normalized before the same models can be applied. We propose that YMR not only simplifies the modeling but also avoids data normalization preprocess since the ratio of each patient is comparable. The YMR is computed from the same individuals; therefore, it works for a single patient sample. YMR works for different data analysis platforms and different data preprocess methods. Further, lung cancer prognosis with the YMR could be improved by optimizing the Yin and Yang gene lists and the number of genes in the YMR calculation. With the advent of microarray technology, groups of differentially expressed genes were chosen between normal tissue samples and cancer samples. To our knowledge, there is no Ginsenoside-Ro report that selected the DEGs between normal and cancer samples for cancer prognostic signature development. Rather, previous publications selected genes between patients of long and short survival time or genes that correlate to survival time. In those publications, Cox regression analysis of all genes against the survival time of all patients resulted in a proportional hazard rate for each gene. The top gene in the list, pre-clustered genes, or metagenes were used as signature genes.