Integrative Omics Analysis Reveals Post-Transcriptionally Enhanced Protective Host Response in Colorectal Cancers with Microsatellite Instability

Microsatellite instability (MSI) is a frequent and clinically relevant molecular phenotype in colorectal cancer. MSI cancers have favorable survival compared with microsatellite stable cancers (MSS), possibly due to the pronounced tumor-infiltrating lymphocytes observed in MSI cancers. Consistent with the strong immune response that MSI cancers trigger in the host, previous transcriptome expression studies have identified mRNA signatures characteristic of immune response in MSI cancers. However, proteomics features of MSI cancers and the extent to which the mRNA signatures are reflected at the protein level remain largely unknown. Here, we performed a comprehensive comparison of global proteomics profiles between MSI and MSS colorectal cancers in The Cancer Genome Atlas (TCGA) cohort. We found that protein signatures of MSI are also associated with increased immunogenicity. To reliably quantify post-transcription regulation in MSI cancers, we developed a resampling-based regression method by integrative modeling of transcriptomics and proteomics data sets. Compared with the popular simple method, which detects post-transcriptional regulation by either identifying genes differentially expressed at the mRNA level but not at the protein level or vice versa, our method provided a quantitative, more sensitive, and accurate way to identify genes subject to differential post-transcriptional regulation. With this method, we demonstrated that post-transcriptional regulation, coordinating protein expression with key players, initiates de novo and enhances protective host response in MSI cancers

Data_Sheet_1_Characteristics of cognitive function in patients with cerebellar infarction and its association with lesion location.docx

Objective: This study aimed to explore the characteristics of cognitive function in patients with cerebellar infarction and its association with lesion location.Methods: Forty-five patients with isolated cerebellar infarction were collected in the Department of Neurology, Beijing Tiantan Hospital. Thirty healthy controls were recruited matched by age and education. Global cognitive function was evaluated by using Addenbrooke’s Cognitive Examination version III (ACE-III). An extensive neuropsychological assessment battery was also tested to evaluate the characteristics of each cognitive domain. 3D slicer software was used to draw the lesion, and evaluate the lesions’ volume, side, and location. Group analysis was used to compare the differences in cognitive performance between patients and healthy controls, and patients with left and right cerebellar hemisphere infarction. Spearman analysis was used to explore the correlation between cognitive function and lesion volume. We also subdivided each patient’s lesions according to the cerebellar atlas to identify the specific cerebellar location related to cognitive decline.Results: Patients with cerebellar infarction had a lower ACE-III score compared with the healthy group (87.9 ± 6.2 vs. 93.7 ± 2.9, p Conclusion: We identified that cerebellar involvement in cognition, especially in attention processing and executive function. Cerebellar right-sided lateralization of cognition and functional topography were also revealed in the current study.</p

Construction of drug network using side effects.

<p>Construction of drug network using side effects.</p

Construction of Drug Network Based on Side Effects and Its Application for Drug Repositioning

<div><p>Drugs with similar side-effect profiles may share similar therapeutic properties through related mechanisms of action. In this study, a drug-drug network was constructed based on the similarities between their clinical side effects. The indications of a drug may be inferred by the enriched FDA-approved functions of its neighbouring drugs in the network. We systematically screened new indications for 1234 drugs with more than 2 network neighbours, 36.87% of the drugs achieved a performance score of <b>N</b>ormalized <b>D</b>iscounted <b>C</b>umulative <b>G</b>ain in the top <b>5</b> positions (NDCG@5)≥0.7, which means most of the known FDA-approved indications were well predicted at the top 5 positions. In particular, drugs for diabetes, obesity, laxatives and antimycobacterials had extremely high performance with more than 80% of them achieving NDCG@5≥0.7. Additionally, by manually checking the predicted 1858 drug-indication pairs with <b>E</b>xpression <b>A</b>nalysis <b>S</b>ystematic <b>E</b>xplorer (EASE) score≤10⁻⁵ (EASE score is a rigorously modified Fisher exact test p value), we found that 80.73% of such pairs could be verified by preclinical/clinical studies or scientific literature. Furthermore, our method could be extended to predict drugs not covered in the network. We took 98 external drugs not covered in the network as the test sample set. Based on our similarity criteria using side effects, we identified 41 drugs with significant similarities to other drugs in the network. Among them, 36.59% of the drugs achieved NDCG@5≥0.7. In all of the 106 drug-indication pairs with an EASE score≤0.05, 50.94% of them are supported by FDA approval or preclinical/clinical studies. In summary, our method which is based on the indications enriched by network neighbors may provide new clues for drug repositioning using side effects.</p></div

Predicted drug-indication pairs of SIDER drugs.

<p>Predicted drug-indication pairs of SIDER drugs.</p

The trends of covered drugs and drug-drug pairs that share the same terms in the MeSH hierarchy.

<p>The trends of covered drugs and drug-drug pairs that share the same terms in the MeSH hierarchy.</p

Sub-network of Dynastat.

<p>Each node represents a drug. Drugs approved for pain management are marked in yellow. Drugs approved for rheumatoid arthritis therapy are marked in purple.</p

Top 10 ATC therapeutic categories with NDCG@5≥0.7.

<p>Top 10 ATC therapeutic categories with NDCG@5≥0.7.</p

The similarity of 98 SIDER drugs in test sample set with the drugs in the network.

<p>The similarity of 98 SIDER drugs in test sample set with the drugs in the network.</p

The 2×2 contingency table of <i>drug A</i> and indication <i>i</i>.

<p>n: The number of <i>drug A</i>'s neighbors which are approved for indication <i>i</i> (n≥2);</p><p>N: The number of <i>drug A</i>'s neighbors;</p><p>r: the number of drugs in the network which are approved for indication <i>i</i>;</p><p>d: The number of drugs in the network.</p