Identification of ovarian cancer associated genes using an integrated approach in a Boolean framework

Identification of lung adenocarcinoma subtypes based on mitochondrial energy metabolism-related genes

BACKGROUND

Identifying subtypes of lung adenocarcinoma (LUAD) patients based on mitochondrial energy metabolism and immunotherapy sensitivity is essential for precision cancer treatment.

METHODS

LUAD subtypes were identified using unsupervised consensus clustering, and results were subjected to immune and tumor mutation analyses. DEGs between subtypes were identified by differential analysis. Functional enrichment and PPI network analyses were conducted. Patients were classified into high and low expression groups based on the expression of the top 10 hub genes, and survival analysis was performed. Drugs sensitive to feature genes were screened based on the correlation between hub gene expression and drug IC50 value. qRT-PCR and western blot were used for gene expression detection, and CCK-8 and flow cytometry were for cell viability and apoptosis analysis.

RESULTS

Cluster-1 had significantly higher overall survival and a higher degree of immunoinfiltration and immunophenotypic score, but a lower TIDE score, DEPTH score, and TMB. Enrichment analysis showed that pathways and functions of DEGs between two clusters were mainly related to the interaction of receptor ligands with intracellular proteases. High expression of hub genes corresponded to lower patient survival rates. The predicted drugs with high sensitivity to feature genes were CDK1: Ribavirin (0.476), CCNB2: Hydroxyurea (0.474), Chelerythrine (0.470), and KIF11: Ribavirin (0.471). KIF11 and CCNB2 were highly expressed in LUAD cells and promoted cell viability and inhibited cell apoptosis.

CONCLUSION

This study identified two subtypes of LUAD, with cluster-1 being more suitable for immunotherapy. These results provided a reference for the development of precision immunotherapy for LUAD patients.

BubR1 controls starvation-induced lipolysis via IMD signaling pathway in Drosophila

Lipolysis, the key process releasing fat acids to generate energy in adipose tissues, correlates with starvation resistance. Nevertheless, its detail mechanisms remain elusive. BubR1, an essential mitotic regulator, ensures proper chromosome alignment and segregation during mitosis, but its physiological functions are largely unknown. Here, we use Drosophila adult fat body, the major lipid storage organ, to study the functions of BubR1 in lipolysis. We show that both whole body- and fat body-specific BubR1 depletions increase lipid degradation and shorten the lifespan under fasting but not feeding. Relish, the conserved regulator of IMD signaling pathway, acts as the downstream target of BubR1 to control the expression level of Bmm and modulate the lipolysis upon fasting. Thus, our study reveals new functions of BubR1 in starvation-induced lipolysis and provides new insights into the molecular mechanisms of lipolysis mediated by IMD signaling pathway.

BUB1 drives the occurrence and development of bladder cancer by mediating the STAT3 signaling pathway

Background

The incidence of bladder urothelial carcinoma (UC), a common malignancy of the urinary tract, is approximately three times higher in men than in women. High expression of the mitotic kinase BUB1 is associated with the occurrence and development of several cancers, although the relationship between BUB1 and bladder tumorigenesis remains unclear.

Methods

Using a microarray approach, we found increased BUB1 expression in human BCa. The association between BUB1 and STAT3 phosphorylation was determined through molecular and cell biological methods. We evaluated the impact of pharmacologic inhibition of BUB1 kinase activity on proliferation and BCa progression in vitro and in vivo.

Results

In this study, we found that BUB1 expression was increased in human bladder cancer (BCa). We further identified through a series of molecular and cell biological approaches that BUB1 interacted directly with STAT3 and mediated the phosphorylation of STAT3 at Ser727. In addition, the findings that pharmacologic inhibition of BUB1 kinase activity significantly suppressed BCa cell proliferation and the progression of bladder cancer in vitro and in vivo were further verified. Finally, we found that the BUB1/STAT3 complex promoted the transcription of STAT3 target genes and that depletion of BUB1 and mutation of the BUB1 kinase domain abrogated this transcriptional activity, further highlighting the critical role of kinase activity in the activation of STAT3 target genes. A pharmacological inhibitor of BUB1 (2OH-BNPP1) was able to significantly inhibit the growth of BCa cell xenografts.

Conclusion

This study showed that the BUB1 kinase drives the progression and proliferation of BCa by regulating the transcriptional activation of STAT3 signaling and may be an attractive candidate for therapeutic targeting in BCa.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-021-02179-z.

Crystal structure of the SH3 domain of human Lyn non-receptor tyrosine kinase

Lyn kinase (Lck/Yes related novel protein tyrosine kinase) belongs to the family of Src-related non-receptor tyrosine kinases. Consistent with physiological roles in cell growth and proliferation, aberrant function of Lyn is associated with various forms of cancer, including leukemia, breast cancer and melanoma. Here, we determine a 1.3 Å resolution crystal structure of the polyproline-binding SH3 regulatory domain of human Lyn kinase, which adopts a five-stranded β-barrel fold. Mapping of cancer-associated point mutations onto this structure reveals that these amino acid substitutions are distributed throughout the SH3 domain and may affect Lyn kinase function distinctly.

Targeting autophagy sensitises lung cancer cells to Src family kinase inhibitors

Lung cancer is the main cancer killer in both men and women, mostly due to the rapid development of drug resistant metastatic disease. Here, we evaluate the potential involvement of SRC family kinases (SFK) in lung cancer biology and assess the possible benefits of their inhibition as a therapeutic approach. We demonstrated that various SRC family members, including LYN and LCK, normally expressed solely in hematopoietic cells and neural tissues, are overexpressed and activated in a panel of SCLC and NSCLC cell lines. This was clinically relevant as LYN and FYN are also overexpressed in lung cancer clinical specimens. Moreover, LYN overexpression correlated with decreased patient survival on univariate and multivariate analysis. Dasatinib (BMS-354825), a SRC/ABL inhibitor, effectively blocked SFK activation at nanomolar concentrations which correlated with a significant decrease in cell numbers of multiple lung cancer cell lines. This effect was matched by a decrease in DNA synthesis, but only moderate induction of apoptosis. Indeed, dasatinib as well as PP2, another SFK inhibitor, strongly induced autophagy that likely prevented apoptosis. However, inhibition of this autophagic response induced robust apoptosis and sensitised lung cancer cells to dasatinib in vitro and in vivo. Our results provide an explanation for why dasatinib failed in NSCLC clinical trials. Furthermore, our data suggest that combining SFK inhibitors with autophagy inhibitors could provide a novel therapeutic approach in this disease.

Modeling of Cancer Classifier to Predict Site of Origin

Cancer classification based on site of origin is very significant research issue for prediction and treatment of cancer. This paper is addressing the problem of cancer classification for Homo Sapiens genes composed of amino acid chain. Cancer gene network is realized by equivalent electrical circuits based on hydrophilic/ hydrophobic property of amino acid and a classifier is modeled to determine the cancer origin. The phase value, peak gain value and shape of Nyquist curve of network model are investigated to characterize different types of cancer gene origins. The model achieves 81.09% of classification accuracy and proves to be more sensitive and simple, since it shows 69% better performance compare to the existing nucleotide based method. The proposed classifier successfully predicts the site of origin of 93 cancer gene samples.

MiR-195 suppresses non-small cell lung cancer by targeting CHEK1

MiR-195 suppresses tumor growth and is associated with better survival outcomes in several malignancies including non-small cell lung cancer (NSCLC). Our previous study showed high miR-195 plasma levels associated with favorable overall survival of non-smoking women with lung adenocarcinoma. To further elucidate role of miR-195 in NSCLC, we conducted in vitro experiment as well as clinical studies in a cohort of 299 NSCLC samples. We demonstrated that miR-195 expression was lower in tumor tissues and was associated with poor survival outcome. Overexpression of miR-195 suppressed tumor cell growth, migration and invasion. We discovered that CHEK1 was a direct target of miR-195, which decreased CHEK1 expression in lung cancer cells. High expression of CHEK1 in lung tumors was associated with poor overall survival. Our results suggest that miR-195 suppresses NSCLC and predicts lung cancer prognosis.

BUB1 mRNA is significantly co-expressed with AURKA and AURKB mRNA in advanced-stage ovarian serous carcinoma

The objective of this study was to investigate the expression and clinical role of the spindle checkpoint kinase budding uninhibited by benzimidazole 1 (Bub1) in primary and metastatic advanced-stage ovarian serous carcinoma. BUB1 mRNA expression was analyzed in 178 tumors (88 effusions, 38 primary carcinomas, and 52 solid metastases) from 144 patients with advanced-stage disease using quantitative real-time polymerase chain reaction (PCR). Bub1 protein expression by Western blotting was studied in 63 carcinomas (30 effusions and 33 solid lesions). BUB1 mRNA expression at different anatomic sites was studied for association with clinicopathologic parameters, including chemotherapy resistance and survival. BUB1 mRNA was universally expressed in serous carcinomas, irrespective of anatomic site. BUB1 mRNA levels were uniformly low in six ovarian surface epithelium specimens analyzed for comparative purposes. Bub1 protein was expressed in 22/30 effusions and 28/33 solid lesions. BUB1 mRNA expression was significantly higher in chemo-naïve primary carcinomas and solid metastases compared to specimens obtained following neoadjuvant chemotherapy (p < 0.001) and was unrelated to chemotherapy exposure in effusions nor to chemoresponse or survival at any anatomic site. BUB1 mRNA levels in both effusions and solid lesions were strongly related to the mRNA levels of AURKA and AURKB previously studied in this cohort (p < 0.001 for both). Bub1 is widely expressed in primary and metastatic OC, suggesting a biological role in this cancer. BUB1 mRNA levels are lower following chemotherapy exposure in solid lesions, though its presence is unrelated to clinical behavior including response to chemotherapy and survival. BUB1 is co-expressed with AURKA and AURKB suggesting biological relationship between these spindle cell components.

Network analysis: a new approach to study endocrine disorders

Systems biology is the study of the interactions that occur between the components of individual cells - including genes, proteins, transcription factors, small molecules, and metabolites, and their relationships to complex physiological and pathological processes. The application of systems biology to medicine promises rapid advances in both our understanding of disease and the development of novel treatment options. Network biology has emerged as the primary tool for studying systems biology as it utilises the mathematical analysis of the relationships between connected objects in a biological system and allows the integration of varied 'omic' datasets (including genomics, metabolomics, proteomics, etc.). Analysis of network biology generates interactome models to infer and assess function; to understand mechanisms, and to prioritise candidates for further investigation. This review provides an overview of network methods used to support this research and an insight into current applications of network analysis applied to endocrinology. A wide spectrum of endocrine disorders are included ranging from congenital hyperinsulinism in infancy, through childhood developmental and growth disorders, to the development of metabolic diseases in early and late adulthood, such as obesity and obesity-related pathologies. In addition to providing a deeper understanding of diseases processes, network biology is also central to the development of personalised treatment strategies which will integrate pharmacogenomics with systems biology of the individual.