A complex disease generally results not from malfunction of individual molecules but from dysfunction of the relevant system or network, which dynamically changes with time and conditions. Thus, estimating a condition-specific network from a single sample is crucial to elucidating the molecular mechanisms of complex diseases at the system level. However, there is currently no effective way to construct such an individual-specific network by expression profiling of a single sample because of the requirement of multiple samples for computing correlations. We developed here with a statistical method, i.e. a sample-specific network (SSN) method, which allows us to construct individual-specific networks based on molecular expressions of a single sample. Using this method, we can characterize various human diseases at a network level. In particular, such SSNs can lead to the identification of individual-specific disease modules as well as driver genes, even without gene sequencing information. Extensive analysis by using the Cancer Genome Atlas data not only demonstrated the effectiveness of the method, but also found new individual-specific driver genes and network patterns for various types of cancer. Biological experiments on drug resistance further validated one important advantage of our method over the traditional methods, i.e. we can even identify such drug resistance genes that actually have no clear differential expression between samples with and without the resistance, due to the additional network information.

Hypoxia occurs naturally at high-altitudes and pathologically in hypoxic solid tumors. Here, we report that genes involved in various human cancers evolved rapidly in Tibetans and six Tibetan domestic mammals compared to reciprocal lowlanders. Furthermore, m⁶A modified mRNA binding protein YTHDF1, one of evolutionary positively selected genes for high-altitude adaptation is amplified in various cancers, including non-small cell lung cancer (NSCLC). We show that YTHDF1 deficiency inhibits NSCLC cell proliferation and xenograft tumor formation through regulating the translational efficiency of CDK2, CDK4, and cyclin D1, and that YTHDF1 depletion restrains de novo lung adenocarcinomas (ADC) progression. However, we observe that YTHDF1 high expression correlates with better clinical outcome, with its depletion rendering cancerous cells resistant to cisplatin (DDP) treatment. Mechanistic studies identified the Keap1-Nrf2-AKR1C1 axis as the downstream mediator of YTHDF1. Together, these findings highlight the critical role of YTHDF1 in both hypoxia adaptation and pathogenesis of NSCLC.

Lung cancer is one of the most devastating diseases worldwide with high incidence and mortality. Hippo (Hpo) pathway is a conserved regulator of organ size in both Drosophila and mammals. Emerging evidence has suggested the significance of Hpo pathway in cancer development. In this study, we identify VGLL4 as a novel tumor suppressor in lung carcinogenesis through negatively regulating the formation of YAP-TEAD complex, the core component of Hpo pathway. Our data show that VGLL4 is frequently observed to be lowly expressed in both mouse and human lung cancer specimens. Ectopic expression of VGLL4 significantly suppresses the growth of lung cancer cells in vitro. More importantly, VGLL4 significantly inhibits lung cancer progression in de novo mouse model. We further find that VGLL4 inhibits the activity of the YAP-TEAD transcriptional complex. Our data show that VGLL4 directly competes with YAP in binding to TEADs and executes its growth-inhibitory function through two TDU domains. Collectively, our study demonstrates that VGLL4 is a novel tumor suppressor for lung cancer through negatively regulating the YAP-TEAD complex formation and thus the Hpo pathway.

Alterations of gut microbiota have been implicated in multiple diseases including cancer. However, the gut microbiota spectrum in lung cancer remains largely unknown. Here we profiled the gut microbiota composition in a discovery cohort containing 42 early-stage lung cancer patients and 65 healthy individuals through the 16S ribosomal RNA (rRNA) gene sequencing analysis. We found that lung cancer patients displayed a significant shift of microbiota composition in contrast to the healthy populations. To identify the optimal microbiota signature for noninvasive diagnosis purpose, we took advantage of Support-Vector Machine (SVM) and found that the predictive model with 13 operational taxonomic unit (OTU)-based biomarkers achieved a high accuracy in lung cancer prediction (area under curve, AUC = 97.6%). This signature performed reasonably well in the validation cohort (AUC = 76.4%), which contained 34 lung cancer patients and 40 healthy individuals. To facilitate potential clinical practice, we further constructed a ‘patient discrimination index’ (PDI), which largely retained the prediction efficiency in both the discovery cohort (AUC = 92.4%) and the validation cohort (AUC = 67.7%). Together, our study uncovered the microbiota spectrum of lung cancer patients and established the specific gut microbial signature for the potential prediction of the early-stage lung cancer.