Bioinformatics Analysis Reveals Centromere Protein K Can Serve as Potential Prognostic Biomarker and Therapeutic Target for Non-small Cell Lung Cancer

Prediction of lung cancer metastasis by gene expression

Tumor metastasis is the main cause of death in cancer patients. Early prediction of tumor metastasis can allow for timely intervention. At present, research on tumor metastasis mainly focuses on manual diagnosis by imaging or diagnosis by computational methods. With the deterioration of the tumor, gene expression levels in blood change greatly. It is feasible to measure the transcripts of key genes to predict whether cancer will metastasize. Therefore, in this paper, we obtained gene expression data from 226 patients from TCGA. These data included 239,322 transcripts. Background screening and LASSO analysis were used to select 31 transcripts as features. Finally, a deep neural network (DNN) was used to determine whether or not lung cancer would metastasize. We compared our methods with several other methods and found that our method achieved the best precision. In addition, in a previous study, we identified 7 genes that play a vital role in lung cancer. We added those gene transcripts into the DNN and found that the AUC and AUPR of the model were increased.

The mechanism and biomarker function of Cavin-2 in lung ischemia-reperfusion injury

BACKGROUND

Lung Ischemia Reperfusion injury(LIRI) is one of the most predominant complications of ischemic lung disease. Cavin-2 emerged as a regulator of a variety of cellular processes, including endocytosis, lipid homeostasis, signal transduction and tumorigenesis, but the function of Cavin-2 in LIRI is unknown. The purpose of this study was to determine the predictive potential of Cavin-2 in protecting lung ischemia-reperfusion injury and its corresponding mechanisms.

METHODS

We found the strong relationship between Cavin-2 and multiple immune-related genes by deep learning method. To reveal the mechanism of Cavin-2 in LIRI, the LIRI SD rat model was constructed to detect the expression of Cavin-2 in the lung tissue of SD rats after LIRI, and the expression of Cavin-2 in lung cell lines was also detected. The expression of IL-6, IL-10 and MDA in cells after Cavin-2 over-expression or knockdown was examined under hypoxic conditions. The expression levels of p-AKT, p-STAT3 and p-ERK1/2 were measured in over-expressing Cavin-2 cells under hypoxic-ischemia conditions, and then the corresponding blockers of AKT, STAT3 and ERK1/2 were given to verify, whether they play a protective role in LIRI.

RESULTS

After hypoxia, the expression of Cavin-2 in rat lung tissues was significantly increased, and the cellular activity and IL-10 in Cavin-2 over-expressing cells were significantly higher than that of the control group, while IL-6 and MDA were significantly lower than that of the control group, while the above results were reversed in Cavin-2 knockdown cells; Meanwhile, the phosphorylation levels of AKT, STAT3, and ERK1/2 were significantly increased in Cavin-2 over-expression cells after hypoxia. When AKT, STAT3, and ERK1/2 specific blockers were given, they lost their protective effect against LIRI.

CONCLUSIONS

Cavin-2 shows biomarker potential in protecting lung from ischemia-reperfusion injury through the survivor activating factor enhancement (SAFE) and reperfusion injury salvage kinase (RISK) pathway.

KRAS is a prognostic biomarker associated with diagnosis and treatment in multiple cancers

KRAS encodes K-Ras proteins, which take part in the MAPK pathway. The expression level of KRAS is high in tumor patients. Our study compared KRAS expression levels between 33 kinds of tumor tissues. Additionally, we studied the association of KRAS expression levels with diagnostic and prognostic values, clinicopathological features, and tumor immunity. We established 22 immune-infiltrating cell expression datasets to calculate immune and stromal scores to evaluate the tumor microenvironment. KRAS genes, immune check-point genes and interacting genes were selected to construct the PPI network. We selected 79 immune checkpoint genes and interacting related genes to calculate the correlation. Based on the 33 tumor expression datasets, we conducted GSEA (genome set enrichment analysis) to show the KRAS and other co-expressed genes associated with cancers. KRAS may be a reliable prognostic biomarker in the diagnosis of cancer patients and has the potential to be included in cancer-targeted drugs.

MoRF-FUNCpred: Molecular Recognition Feature Function Prediction Based on Multi-Label Learning and Ensemble Learning

Intrinsically disordered regions (IDRs) without stable structure are important for protein structures and functions. Some IDRs can be combined with molecular fragments to make itself completed the transition from disordered to ordered, which are called molecular recognition features (MoRFs). There are five main functions of MoRFs: molecular recognition assembler (MoR_assembler), molecular recognition chaperone (MoR_chaperone), molecular recognition display sites (MoR_display_sites), molecular recognition effector (MoR_effector), and molecular recognition scavenger (MoR_scavenger). Researches on functions of molecular recognition features are important for pharmaceutical and disease pathogenesis. However, the existing computational methods can only predict the MoRFs in proteins, failing to distinguish their different functions. In this paper, we treat MoRF function prediction as a multi-label learning task and solve it with the Binary Relevance (BR) strategy. Finally, we use Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF) as basic models to construct MoRF-FUNCpred through ensemble learning. Experimental results show that MoRF-FUNCpred performs well for MoRF function prediction. To the best knowledge of ours, MoRF-FUNCpred is the first predictor for predicting the functions of MoRFs. Availability and Implementation: The stand alone package of MoRF-FUNCpred can be accessed from https://github.com/LiangYu-Xidian/MoRF-FUNCpred.

An epistasis and heterogeneity analysis method based on maximum correlation and maximum consistence criteria

Tumor heterogeneity significantly increases the difficulty of tumor treatment. The same drugs and treatment methods have different effects on different tumor subtypes. Therefore, tumor heterogeneity is one of the main sources of poor prognosis, recurrence and metastasis. At present, there have been some computational methods to study tumor heterogeneity from the level of genome, transcriptome, and histology, but these methods still have certain limitations. In this study, we proposed an epistasis and heterogeneity analysis method based on genomic single nucleotide polymorphism (SNP) data. First of all, a maximum correlation and maximum consistence criteria was designed based on Bayesian network score K2 and information entropy for evaluating genomic epistasis. As the number of SNPs increases, the epistasis combination space increases sharply, resulting in a combination explosion phenomenon. Therefore, we next use an improved genetic algorithm to search the SNP epistatic combination space for identifying potential feasible epistasis solutions. Multiple epistasis solutions represent different pathogenic gene combinations, which may lead to different tumor subtypes, that is, heterogeneity. Finally, the XGBoost classifier is trained with feature SNPs selected that constitute multiple sets of epistatic solutions to verify that considering tumor heterogeneity is beneficial to improve the accuracy of tumor subtype prediction. In order to demonstrate the effectiveness of our method, the power of multiple epistatic recognition and the accuracy of tumor subtype classification measures are evaluated. Extensive simulation results show that our method has better power and prediction accuracy than previous methods.