Gene Co-Expression Networks Restructured Gene Fusion in Rhabdomyosarcoma Cancers

Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis

Background

Thyroid eye disease (TED) is the most common orbital pathology that occurs in up to 50% of patients with Graves’ disease. Herein, we aimed at discovering the possible hub genes and pathways involved in TED based on bioinformatical approaches.

Results

The GSE105149 and GSE58331 datasets were downloaded from the Gene Expression Omnibus (GEO) database and merged for identifying TED-associated modules by weighted gene coexpression network analysis (WGCNA) and local maximal quasi-clique merger (lmQCM) analysis. EdgeR was run to screen differentially expressed genes (DEGs). Transcription factor (TF), microRNA (miR) and drug prediction analyses were performed using ToppGene suite. Function enrichment analysis was used to investigate the biological function of genes. Protein–protein interaction (PPI) analysis was performed based on the intersection between the list of genes obtained by WGCNA, lmQCM and DEGs, and hub genes were identified using the MCODE plugin. Based on the overlap of 497 genes retrieved from the different approaches, a robust TED coexpression network was constructed and 11 genes (ATP6V1A, PTGES3, PSMD12, PSMA4, METAP2, DNAJA1, PSMA1, UBQLN1, CCT2, VBP1 and NAA50) were identified as hub genes. Key TFs regulating genes in the TED-associated coexpression network, including NFRKB, ZNF711, ZNF407 and MORC2, and miRs including hsa-miR-144, hsa-miR-3662, hsa-miR-12136 and hsa-miR-3646, were identified. Genes in the coexpression network were enriched in the biological processes including proteasomal protein catabolic process and proteasome-mediated ubiquitin-dependent protein catabolic process and the pathways of endocytosis and ubiquitin-mediated proteolysis. Drugs perturbing genes in the coexpression network were also predicted and included enzyme inhibitors, chlorodiphenyl and finasteride.

Conclusions

For the first time, TED-associated coexpression network was constructed and key genes and their functions, as well as TFs, miRs and drugs, were predicted. The results of the present work may be relevant in the treatment and diagnosis of TED and may boost molecular studies regarding TED.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-022-00412-0.

TPSC: a module detection method based on topology potential and spectral clustering in weighted networks and its application in gene co-expression module discovery

BACKGROUND

Gene co-expression networks are widely studied in the biomedical field, with algorithms such as WGCNA and lmQCM having been developed to detect co-expressed modules. However, these algorithms have limitations such as insufficient granularity and unbalanced module size, which prevent full acquisition of knowledge from data mining. In addition, it is difficult to incorporate prior knowledge in current co-expression module detection algorithms.

RESULTS

In this paper, we propose a novel module detection algorithm based on topology potential and spectral clustering algorithm to detect co-expressed modules in gene co-expression networks. By testing on TCGA data, our novel method can provide more complete coverage of genes, more balanced module size and finer granularity than current methods in detecting modules with significant overall survival difference. In addition, the proposed algorithm can identify modules by incorporating prior knowledge.

CONCLUSION

In summary, we developed a method to obtain as much as possible information from networks with increased input coverage and the ability to detect more size-balanced and granular modules. In addition, our method can integrate data from different sources. Our proposed method performs better than current methods with complete coverage of input genes and finer granularity. Moreover, this method is designed not only for gene co-expression networks but can also be applied to any general fully connected weighted network.

Clinical features and prognosis of paediatric rhabdomyosarcoma with bone marrow metastasis: a single Centre experiences in China

BACKGROUND

The aim of this study was to summarize the clinical characteristics, therapeutic effects and prognosis of patients with rhabdomyosarcoma (RMS) and bone marrow metastasis, improve the understanding of this disease.

METHOD

This was a single-institution retrospective study involving the children with RMS, who presented with bone marrow metastasis at initial presentation to our hospital between 1st, Jan, 2006 and 31st, Dec,2019. Follow-up concluded on 31st, Dec, 2020 and the clinical data were collected and analysed.

RESULT

Between 1st Jan 2006 and 31st Dec 2019, 13 eligible patients presented to our hospital, including 10 males and 3 females, these eligible patients accounted for 4.5% of all RMS patients. The median age at onset was 5.6 years (range 1.7-14 years). The patients not only had unfavourable primary sites, but also had multiple metastases. The bone marrow aspirate samples of the patients comprised 8-95% blast-like cells. Nine of 13 patients were misdiagnosed with haematological malignancies or other solid tumours. With respect to histology, four of 13 children were classified as embryonal RMS and nine as alveolar RMS. Eleven patients underwent PAX-FOXO1 fusion testing; eight had the POX- FOXO1 fusion gene. Immunohistochemically(IHC) analysis revealed that the tumour cells were positive for Desmin, Vimentin, Myo-D1 and Myogenin. More importantly, the patients had extremely poor prognoses, the median EFS was 12.0 months (range 3-28.3 months) and the median OS was 27.0 months (range6-46.2 months).

CONCLUSION

This study demonstrates that children with RMS and bone marrow metastasis usually exhibit atypical primary sites and multiple metastases, with presentation mimicking haematological malignancies or other solid tumors at initial presentation. Pathology and IHC analysis combined with POX-FOXO1 fusion gene detections can effectively confirm the diagnosis. These patients are more likely to relapse or progress during early treatment and are prone to intracranial metastasis. While multidisciplinary therapy combined with Temozolomide may prevent it, further prospective research is required to evaluate the therapeutic effects.

Innovating Computational Biology and Intelligent Medicine: ICIBM 2019 Special Issue

The International Association for Intelligent Biology and Medicine (IAIBM) is a nonprofit organization that promotes intelligent biology and medical science. It hosts an annual International Conference on Intelligent Biology and Medicine (ICIBM), which was established in 2012. The ICIBM 2019 was held from 9 to 11 June 2019 in Columbus, Ohio, USA. Out of the 105 original research manuscripts submitted to the conference, 18 were selected for publication in a Special Issue in Genes. The topics of the selected manuscripts cover a wide range of current topics in biomedical research including cancer informatics, transcriptomic, computational algorithms, visualization and tools, deep learning, and microbiome research. In this editorial, we briefly introduce each of the manuscripts and discuss their contribution to the advance of science and technology.

Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations

BACKGROUND

Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients.

METHODS

In this study, a broad analysis was performed on TCGA cancers using a variety of Deep Learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances.

RESULTS

All models show competitive results across 12 cancer types. The last hidden layers of the Deep Learning approaches are lower dimensional representations of the input data that can be used for feature reduction and visualization. Furthermore, the prognosis performances reveal a negative correlation between model accuracy, overall survival time statistics, and tumor mutation burden (TMB), suggesting an association among overall survival time, TMB, and prognosis prediction accuracy.

CONCLUSIONS

Deep Learning based algorithms demonstrate superior performances than traditional machine learning based models. The cancer prognosis results measured in concordance index are indistinguishable across models while are highly variable across cancers. These findings shedding some light into the relationships between patient characteristics and survival learnability on a pan-cancer level.