Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions

Characterization of a prognostic model for lung squamous cell carcinoma based on eight stemness index-related genes

Background

Cancer stem cells (CSCs) are implicated in cancer progression, chemoresistance, and poor prognosis; thus, they may be promising therapeutic targets. In this study, we aimed to investigate the prognostic application of differentially expressed CSC-related genes in lung squamous cell carcinoma (LUSC).

Methods

The mRNA stemness index (mRNAsi)-related differentially expressed genes (DEGs) in tumors were identified and further categorized by LASSO Cox regression analysis and 1,000-fold cross-validation, followed by the construction of a prognostic score model for risk stratification. The fractions of tumor-infiltrating immune cells and immune checkpoint genes were analyzed in different risk groups.

Results

We found 404 mRNAsi-related DEGs in LUSC, 77 of which were significantly associated with overall survival. An eight-gene prognostic signature (PPP1R27, TLX2, ANKLE1, TIGD3, AMH, KCNK3, FLRT3, and PPBP) was identified and used to construct a risk score model. The TCGA set was dichotomized into two risk groups that differed significantly (p = 0.00057) in terms of overall survival time (1, 3, 5-year AUC = 0.830, 0.749, and 0.749, respectively). The model performed well in two independent GEO datasets (p = 0.029, 0.033; 1-year AUC = 0747, 0.783; 3-year AUC = 0.746, 0.737; 5-year AUC = 0.706, 0.723). Low-risk patients had markedly increased numbers of CD8+ T cells and M1 macrophages and downregulated immune checkpoint genes compared to the corresponding values in high-risk patients (p < 0.05).

Conclusion

A stemness-related prognostic model based on eight prognostic genes in LUSC was developed and validated. The results of this study would have prognostic and therapeutic implications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-02011-0.

Community structure informs species geographic distributions

Understanding what determines species’ geographic distributions is crucial for assessing global change threats to biodiversity. Measuring limits on distributions is usually, and necessarily, done with data at large geographic extents and coarse spatial resolution. However, survival of individuals is determined by processes that happen at small spatial scales. The relative abundance of coexisting species (i.e. ‘community structure’) reflects assembly processes occurring at small scales, and are often available for relatively extensive areas, so could be useful for explaining species distributions. We demonstrate that Bayesian Network Inference (BNI) can overcome several challenges to including community structure into studies of species distributions, despite having been little used to date. We hypothesized that the relative abundance of coexisting species can improve predictions of species distributions. In 1570 assemblages of 68 Mediterranean woody plant species we used BNI to incorporate community structure into Species Distribution Models (SDMs), alongside environmental information. Information on species associations improved SDM predictions of community structure and species distributions moderately, though for some habitat specialists the deviance explained increased by up to 15%. We demonstrate that most species associations (95%) were positive and occurred between species with ecologically similar traits. This suggests that SDM improvement could be because species co-occurrences are a proxy for local ecological processes. Our study shows that Bayesian Networks, when interpreted carefully, can be used to include local conditions into measurements of species’ large-scale distributions, and this information can improve the predictions of species distributions.

In utero exposure of rats to high-fat diets perturbs gene expression profiles and cancer susceptibility of prepubertal mammary glands

Human studies suggest that high-fat diets (HFDs) increase the risk of breast cancer. The 7,12-dimethylbenz[a]anthracene (DMBA)-induced mammary carcinogenesis rat model is commonly used to evaluate the effects of lifestyle factors such as HFD on mammary tumor risk. Past studies focused primarily on the effects of continuous maternal exposure on the risk of offspring at the end of puberty (PND50). We assessed the effects of prenatal HFD exposure on cancer susceptibility in prepubertal mammary glands and identified key gene networks associated with such disruption. During pregnancy, dams were fed AIN-93G-based diets with isocaloric high olive oil, butterfat or safflower oil. The control group received AIN-93G. Female offspring were treated with DMBA on PND21. However, a significant increase in tumor volume and a trend of shortened tumor latency were observed in rats with HFD exposure against the controls (P=.048 and P=.067, respectively). Large-volume tumors harbored carcinoma in situ. Transcriptome profiling identified 43 differentially expressed genes in the mammary glands of the HFBUTTER group as compared with control. Rapid hormone signaling was the most dysregulated pathway. The diet also induced aberrant expression of Dnmt3a, Mbd1 and Mbd3, consistent with potential epigenetic disruption. Collectively, these findings provide the first evidence supporting susceptibility of prepubertal mammary glands to DMBA-induced tumorigenesis that can be modulated by dietary fat that involves aberrant gene expression and likely epigenetic dysregulation.