Using the theory of coevolution to predict protein-protein interactions in non-small cell lung cancer

PPI-Miner: A Structure and Sequence Motif Co-Driven Protein-Protein Interaction Mining and Modeling Computational Method

Protein-protein interactions (PPIs) play important roles in biological processes of life, and predicting PPIs becomes a critical scientific issue of concern. Most PPIs occur through small domains or motifs (fragments), which are challenging and laborious to map by standard biochemical approaches because they generally require the cloning of several truncation mutants. Here, we present a computational method, named as PPI-Miner, to fish potential protein interacting partners utilizing protein motifs as queries. In brief, this work first developed a motif-matching algorithm designed to identify the proteins that contain sequential or structural similar motifs with the given query motif. Being aligned to the query motif, the binding mode of the discovered motif and its receptor protein will be initially determined to be used to build PPI complexes accordingly. Eventually, a PPI complex structure could be built and optimized with a designed automatic protocol. Besides discovering PPIs, PPI-Miner can also be applied to other areas, i.e., the rational design of molecular glues and protein vaccines. In this work, PPI-Miner was employed to mine the potential cereblon (CRBN) substrates from human proteome. As a result, 1,739 candidates were predicted, and 16 of them have been experimentally validated in previous studies. The source code of PPI-Miner can be obtained from the GitHub repository (https://github.com/Wang-Lin-boop/PPI-Miner), the webserver is freely available for users (https://bailab.siais.shanghaitech.edu.cn/services/ppi-miner), and the database of predicted CRBN substrates is accessible at https://bailab.siais.shanghaitech.edu.cn/services/crbn-subslib.

Tumor-associated macrophages secrete CCL2 and induce the invasive phenotype of human breast epithelial cells through upregulation of ERO1-α and MMP-9

Tumor-associated macrophages (TAMs) are major components of tumor microenvironment that promote invasion and metastasis of cancer cells. In this study, we investigated the effect of TAMs on phenotypic conversion of non-neoplastic MCF10A human breast epithelial cells using an indirect co-culture system. Co-culture with TAMs induced epithelial-to-mesenchymal transition, invasive phenotype, and MMP-9 upregulation in MCF10A cells. Comparative proteomic analysis revealed that endoplasmic reticulum oxidoreductase (ERO)1-α was increased in MCF10A cells co-cultured with TAMs compared to that in mono-cultured cells. ERO1-α was crucial for TAMs-induced invasive phenotype and MMP-9 upregulation involving transcription factors c-fos and c-Jun. Cytokine array analysis showed that levels of interleukin (IL)-6, C-X-C motif ligand (CXCL)1, C-C motif ligand (CCL)2, growth-regulated protein (GRO), IL-8, and granulocyte-macrophage colony-stimulating factor (GM-CSF) were increased in conditioned media of co-cultured cells. Among these cytokines increased in conditioned media of co-cultured cells, CCL2 was secreted from TAMs, leading to induction of ERO1-α, MMP-9 upregulation, and invasiveness in MCF10A cells. Our findings elucidated a molecular mechanism underlying the aggressive phenotypic change of non-neoplastic breast cells by co-culture with TAMs, providing useful information for prevention or treatment of recurrent breast cancer.

Biological Networks for Cancer Candidate Biomarkers Discovery

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.

A Genome-Wide Association Study of Cutaneous Squamous Cell Carcinoma among European Descendants

BACKGROUND

No GWAS on the risk of cutaneous squamous cell carcinoma (SCC) has been published. We conducted a multistage genome-wide association study (GWAS) to identify novel genetic loci for SCC.

METHODS

The study included 745 SCC cases and 12,805 controls of European descent in the discovery stage and 531 SCC cases and 551 controls of European ancestry in the replication stage. We selected 64 independent loci that showed the most significant associations with SCC in the discovery stage (linkage disequilibrium r(2) < 0.4) for replication.

RESULTS

Rs8063761 in the DEF8 gene on chromosome 16 showed the strongest association with SCC (P = 1.7 × 10(-9) in the combined set; P = 1.0 × 10(-6) in the discovery set and P = 4.1 × 10(-4) in the replication set). The variant allele of rs8063761 (T allele) was associated with a decreased expression of DEF8 (P = 1.2 × 10(-6)). Besides, we validated four other SNPs associated with SCC in the replication set, including rs9689649 in PARK2 gene (P = 2.7 × 10(-6) in combined set; P = 3.2 × 10(-5) in the discovery; and P = 0.02 in the replication), rs754626 in the SRC gene (P = 1.1 × 10(-6) in combined set; P = 1.4 × 10(-5) in the discovery and P = 0.02 in the replication), rs9643297 in ST3GAL1 gene (P = 8.2 × 10(-6) in combined set; P = 3.3 × 10(-5) in the discovery; and P = 0.04 in the replication), and rs17247181 in ERBB2IP gene (P = 4.2 × 10(-6) in combined set; P = 3.1 × 10(-5) in the discovery; and P = 0.048 in the replication).

CONCLUSION

Several genetic variants were associated with risk of SCC in a multistage GWAS of subjects of European ancestry.

IMPACT

Further studies are warranted to validate our finding and elucidate the genetic function of these variants. Cancer Epidemiol Biomarkers Prev; 25(4); 714-20. ©2016 AACR.

Molecular markers as therapeutic targets in lung cancer

Lung cancer is responsible for 29% of cancer deaths in the United States and has very low 5-year survival rates of approximately 11% in men and 15% in women. Although the early diagnosis of lung cancer may increase the survival rate with adequate treatment, advanced lung cancers are often metastasized and receive limited benefit from therapeutic regimens. As conventional treatments for lung cancer reach their limitations, researchers have attempted to discover novel drug therapies aimed at specific targets contributing to the progression of tumorigenesis. Recent advances in systems biology have enabled the molecular biology of lung carcinogenesis to be elucidated. Our understanding of the physiologic processes of tumor development provide a means to design more effective and specific drugs with less toxicity, thereby accelerating the delivery of new drug therapies to the patient's bedside.