Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme

Developing a multiomics data-based mathematical model to predict colorectal cancer recurrence and metastasis

BACKGROUND

Colorectal cancer is the fourth most deadly cancer, with a high mortality rate and a high probability of recurrence and metastasis. Since continuous examinations and disease monitoring for patients after surgery are currently difficult to perform, it is necessary for us to develop a predictive model for colorectal cancer metastasis and recurrence to improve the survival rate of patients.

RESULTS

Previous studies mostly used only clinical or radiological data, which are not sufficient to explain the in-depth mechanism of colorectal cancer recurrence and metastasis. Therefore, this study proposes such a multiomics data-based predictive model for the recurrence and metastasis of colorectal cancer. LR, SVM, Naïve-bayes and ensemble learning models are used to build this predictive model.

CONCLUSIONS

The experimental results indicate that our proposed multiomics data-based ensemble learning model effectively predicts the recurrence and metastasis of colorectal cancer.

Key genes altered in glioblastoma based on bioinformatics (Review)

Glioblastoma multiforme (GBM) is an aggressive brain tumor with poor prognosis. Recent advancements in bioinformatics have contributed to uncovering the genetic alterations that underlie the development and progression of GBM. Analysis of extensive genomic data led to the identification of significant pathways involved in GBM, such as the PI3K/AKT/mTOR and Ras/Raf/MEK/ERK signaling pathways, alongside key genes such as EGFR, TP53 and TERT. These findings have enhanced our understanding of GBM biology and led to the identification of new therapeutic targets. Bioinformatics has become an indispensable tool in pinpointing the genetic modifications that drive GBM, paving the way for innovative treatment strategies. This approach not only aids in comprehending the complexities of GBM but also holds promise for improving outcomes in patients suffering from this devastating disease. The ongoing integration of bioinformatics in GBM research continues to be vital for advancing therapeutic options.

LINC01094 Affects Glioma Cell Proliferation Through miR-204-3p

This study intends to assess LINC01094′s role in glioma cells. LINC01094 level in glioma tissue was detected by RT qPCR. After transfection of LINC01094 overexpression plasmid, LINC01094 siRNA, and si-LINC01094 and miR-204-3p inhibitor, cell proliferation was evaluated by MTT and cell invasion and migration was assessed by transwell and scratch test. LINC01094 expression in glioma tissues was significantly increased. Overexpression of LINC01094 can significantly promote cell proliferation, which was significantly inhibited after knockdown of LINC01094. In addition, silence of LINC01094 can upregulate miR-204-3p and inhibit cell proliferation and promote apoptosis induced by overexpression of LINC01094. In conclusion, LINC01094 promotes glioma cell proliferation through miR-204-3p.

An integrated platform for Brucella with knowledge graph technology: From genomic analysis to epidemiological projection

Motivation:Brucella, the causative agent of brucellosis, is a global zoonotic pathogen that threatens both veterinary and human health. The main sources of brucellosis are farm animals. Importantly, the bacteria can be used for biological warfare purposes, requiring source tracking and routine surveillance in an integrated manner. Additionally, brucellosis is classified among group B infectious diseases in China and has been reported in 31 Chinese provinces to varying degrees in urban areas. From a national biosecurity perspective, research on brucellosis surveillance has garnered considerable attention and requires an integrated platform to provide researchers with easy access to genomic analysis and provide policymakers with an improved understanding of both reported patients and detected cases for the purpose of precision public health interventions.

Results: For the first time in China, we have developed a comprehensive information platform for Brucella based on dynamic visualization of the incidence (reported patients) and prevalence (detected cases) of brucellosis in mainland China. Especially, our study establishes a knowledge graph for the literature sources of Brucella data so that it can be expanded, queried, and analyzed. When similar “epidemiological comprehensive platforms” are established in the distant future, we can use knowledge graph to share its information. Additionally, we propose a software package for genomic sequence analysis. This platform provides a specialized, dynamic, and visual point-and-click interface for studying brucellosis in mainland China and improving the exploration of Brucella in the fields of bioinformatics and disease prevention for both human and veterinary medicine.

Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research

It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.

LINC00511 knockdown suppresses glioma cell malignant progression through miR-15a-5p/AEBP1 axis

BACKGROUND

A strong relationship between long intergenic non-protein coding RNA 511 (LINC00511) and glioma has been previously reported but the mechanism of LINC00511 in glioma is yet to be determined. This study examined the mechanism of LINC00511 in glioma.

METHODS

The expression of LINC00511 in glioma was determined by bioinformatics analysis and real-time quantitative PCR (RT-qPCR) analysis. The target relationship between genes was predicted by starBase, TargetScan, and was verified by dual-luciferase. Subsequently, siRNA targeting LINC00511 (siLINC00511) and miR-15a-5p mimic were transfected into glioma cells to examine the effect on biological characteristics using cell counting kit-8, clone formation, flow cytometry, wound-healing, and transwell. MiR-15a-5p inhibitor and AEBP1 were used for in vitro rescue experiments, and tumorigenesis assay and immunohistochemical assays were performed for in vivo experiments. Epithelial-mesenchymal transition (EMT) and p65 phosphorylation were examined by Western blot.

RESULTS

LINC00511 was predicted and verified to be up-regulated in glioma. SiLINC00511 suppressed cell viability, proliferation, migration and invasion, accelerated apoptosis of glioma cells. Mechanically, siLINC00511 promoted E-cadherin expression but suppressed N-cadherin and Snail expressions. MiR-15a-5p bound to LINC00511, and miR-15a-5p inhibitor partially reversed the effect and regulation of siLINC00511 on glioma cells. AEBP1, a target gene of miR-15a-5p, could activate p65 phosphorylation to promote EMT protein expression and partially reverse the inhibitory effect of miR-15a-5p mimic on the malignant phenotype of glioma cells. SiLINC00511 inhibited tumor growth, down-regulated miR-15a-5p expression and up-regulated AEBP1 and Ki67 expressions in vivo.

CONCLUSION

LINC00511 knockdown inhibits glioma cell progression via miR-15a-5p/AEBP1 axis.