Rictor promotes cell migration and actin polymerization through regulating ABLIM1 phosphorylation in Hepatocellular Carcinoma

Downregulation of ABLIM3 confers to the metastasis of neuroblastoma via regulating the cell adhesion molecules pathway

Neuroblastoma (NB) is the most prevalent extracranial solid tumor in pediatric patients, and its treatment failure often associated with metastasis. In this study, LASSO, SVM-RFE, and random forest tree algorithms, was used to identify the pivotal gene involved in NB metastasis. NB cell lines (SK-N-AS and SK-N-BE2), in conjunction with NB tissue were used for further study. ABLIM3 was identified as the hub gene and can be an independent prognostic factor for patients with NB. The immunohistochemical analysis revealed that ABLIM3 is negatively correlated with the metastasis of NB. Patients with low expression of ABLIM3 had a poor prognosis. High ABLIM3 expression correlated with APC co-stimulation and Type1 IFN response, and TIDE analysis indicated that patients with low ABLIM3 expression exhibited enhanced responses to immunotherapy. Downregulation of ABLIM3 by shRNA transfection increased the migration and invasion ability of NB cells. Gene Set Enrichment Analysis (GSEA) revealed that genes associated with ABLIM3 were primarily enriched in the cell adhesion molecules (CAMs) pathway. RT-qPCR and western blot analyses demonstrated that downregulation of ABLIM3 led to decreased expression of ITGA3, ITGA8, and KRT19, the key components of CAMs. This study indicated that ABLIM3 can be an independent prognostic factor for NB patients, and CAMs may mediate the effect of ABLIM3 on the metastasis of NB, suggesting that ABLIM3 is a potential therapeutic target for NB metastasis, which provides a novel strategy for future research and treatment strategies for NB patients.

The identification and functional analysis of CircRNAs in endometrial receptivity of mice with polycystic ovary

The disorders of endometrial receptivity and ovulatory dysfunction are both significant causes of infertility in patients with polycystic ovary syndrome (PCOS). In this study, we investigated the expression profile and functional implications of circular RNAs (circRNAs) in the endometrial receptivity of PCOS-affected mice. Twenty-four female C57BL/6 mice were divided into PCOS and normal control groups. The PCOS group received subcutaneous DHEA treatment, while the control group remained untreated. Gene chip technology was utilized to analyze circRNA expression in endometrial tissues on the fourth day of gestation with subsequent bioinformatics analyses into circRNA functions. Furthermore, endometrial epithelial cells were used to determine represented circRNA functions. Results showed that the PCOS group exhibited 205 differentially expressed circRNAs, with 147 upregulated and 58 downregulated ones. qRT-PCR confirmed differential expression of circRNAs, including circRNA_38548, circRNA_001686, circRNA_38550, and circRNA_27938. Predicted target genes and a circRNA-miRNA-mRNA regulatory network were constructed. Additionally, four circRNAs (circRNA_38548, circRNA_38550, and circRNA_001686) were identified to contribute to abnormal endometrial receptivity by regulating genes such as Lifr, FOXK1, FOXO1, HOXA10, through interactions with miRNAs. Further research is warranted to elucidate the underlying mechanisms involving these circRNAs.

Roles of Rictor alterations in gastrointestinal tumors (Review)

Gastrointestinal tumors account for five of the top 10 causes of mortality from all cancers (colorectal, liver, stomach, esophageal and pancreatic cancer). Mammalian target of rapamycin (mTOR) signaling is commonly dysregulated in various human cancers. As a core component of the mTOR complex 2 (mTORC2), Rictor is a key effector molecule of the PI3K/Akt pathway. A high alteration rate of Rictor has been observed in gastrointestinal tumors, and such Rictor alterations are often associated with resistance to chemotherapy and related adverse clinical outcomes. However, the exact roles of Rictor in gastrointestinal tumors remain elusive. The aim of the present study was to critically discuss the following: i) Mutation and biological characteristics of Rictor in tumors with a detailed overview of Rictor in cell proliferation, angiogenesis, apoptosis, autophagy and drug resistance; ii) the role of Rictor in tumors of the digestive system, particularly colorectal, hepatobiliary, gastric, esophageal and pancreatic cancer and cholangiocarcinoma; and iii) the current status and prospects of targeted therapy for Rictor by inhibiting Akt activation. Despite the growing realization of the importance of Rictor/mTORC2 in cancer, the underlying mechanistic details remain poorly understood; this needs to change in order for the development of efficient targeted therapies and re‑sensitization of therapy‑resistant cancers to be made possible.

ABLIM1, a novel ubiquitin E3 ligase, promotes growth and metastasis of colorectal cancer through targeting IĸBα ubiquitination and activating NF-ĸB signaling

Actin-binding LIM protein 1 (ABLIM1), a member of the LIM-domain protein family, has been reported as a suppressor in several tumors whereas its role in colorectal cancer (CRC) remains unknown. In this study, we find that ABLIM1 is up-regulated in CRC patients and high levels of ABLIM1 predict short disease-free survival time. Knock-down of ABLIM1 in CRC cell lines by lenti-virus leads to inhibited cell proliferation, migration, and invasion capabilities in vitro and impaired growth of tumor xenografts and liver metastasis lesions in vivo, while ABLIM1 overexpression accelerates tumor growth and invasion in vitro. Mechanistically, we uncover that ABLIM1 activates the NF-ĸB/CCL-20 signaling through modulating IĸBα ubiquitination and proteasomal-mediated degradation. Further co-immunoprecipitation, in vivo and in vitro ubiquitination assays reveal ABLIM1 as a novel ubiquitin E3 ligase binding to IĸBα. Interestingly, The E3 ligase catalysis activity of ABLIM1 depends on its 402-778aa rather than its LIM domains and its interaction with IĸBα relies on the HP domain. Our findings delineate the oncogenic role of ABLIM1 in CRC progression and reveal it as a novel E3 ligase targeting IĸBα, providing new insights into the regulation of NF-ĸB signaling in tumors.

The mTORC2 signaling network: targets and cross-talks

The mechanistic target of rapamycin, mTOR, controls cell metabolism in response to growth signals and stress stimuli. The cellular functions of mTOR are mediated by two distinct protein complexes, mTOR complex 1 (mTORC1) and mTORC2. Rapamycin and its analogs are currently used in the clinic to treat a variety of diseases and have been instrumental in delineating the functions of its direct target, mTORC1. Despite the lack of a specific mTORC2 inhibitor, genetic studies that disrupt mTORC2 expression unravel the functions of this more elusive mTOR complex. Like mTORC1 which responds to growth signals, mTORC2 is also activated by anabolic signals but is additionally triggered by stress. mTORC2 mediates signals from growth factor receptors and G-protein coupled receptors. How stress conditions such as nutrient limitation modulate mTORC2 activation to allow metabolic reprogramming and ensure cell survival remains poorly understood. A variety of downstream effectors of mTORC2 have been identified but the most well-characterized mTORC2 substrates include Akt, PKC, and SGK, which are members of the AGC protein kinase family. Here, we review how mTORC2 is regulated by cellular stimuli including how compartmentalization and modulation of complex components affect mTORC2 signaling. We elaborate on how phosphorylation of its substrates, particularly the AGC kinases, mediates its diverse functions in growth, proliferation, survival, and differentiation. We discuss other signaling and metabolic components that cross-talk with mTORC2 and the cellular output of these signals. Lastly, we consider how to more effectively target the mTORC2 pathway to treat diseases that have deregulated mTOR signaling.

Transcriptomic analysis of esophageal cancer reveals hub genes and networks involved in cancer progression

Esophageal carcinoma (ESCA) has a 5-year survival rate of fewer than 20%. The study aimed to identify new predictive biomarkers for ESCA through transcriptomics meta-analysis to address the problems of ineffective cancer therapy, lack of efficient diagnostic tools, and costly screening and contribute to developing more efficient cancer screening and treatments by identifying new marker genes. Nine GEO datasets of three kinds of esophageal carcinoma were analyzed, and 20 differentially expressed genes were detected in carcinogenic pathways. Network analysis revealed four hub genes, namely RAR Related Orphan Receptor A (RORA), lysine acetyltransferase 2B (KAT2B), Cell Division Cycle 25B (CDC25B), and Epithelial Cell Transforming 2 (ECT2). Overexpression of RORA, KAT2B, and ECT2 was identified with a bad prognosis. These hub genes modulate immune cell infiltration. These hub genes modulate immune cell infiltration. Although this research needs lab confirmation, we found interesting biomarkers in ESCA that may aid in diagnosis and treatment.

Actin-Binding LIM 1 (ABLIM1) Inhibits Glioblastoma Progression and Serves as a Novel Prognostic Biomarker

Background

Glioma is the most prevalent malignant brain tumor in adult humans, and glioblastoma (GBM) is the most malignant type. The actin-binding LIM 1 (ABLIM1) protein can modulate actin polymerization, which is essential for the cell proliferation and migration. We aim to investigate ABLIM1 expression, function, and clinical significance in GBM.

Methods

The ABLIM1 mRNA level was extracted from the TCGA and GTEx online databases. The ABLIM1 protein expression level was explored using immunohistochemistry staining in a GBM cohort enrolled in our hospital (n = 104). The patient survival and prognostic factors were determined using the Kaplan-Meier method and multivariate Cox hazard proportional analysis, respectively. Two human GBM cell lines, U87 and U251 cells, were utilized for ABLIM1 overexpression and cell proliferation analyses. A subcutaneous xenograft model was generated using nude mice to validate the tumor-related effect of ABLIM1 in vivo.

Results

ABLIM1 exhibited a significantly lower mRNA level in GBM than in other glioma or normal brain tissues. Higher ABLIM1 protein level was correlated with smaller GBM tumor size and better cancer-specific survival (CSS). Multivariate analysis identified ABLIM1 as a novel independent prognostic factor for GBM prognosis. ABLIM1 overexpression significantly inhibits U87 and U251 cell proliferation and colony formation. Consistently, ABLIM1 exerted tumor-suppressing functions in mice models.

Conclusion

ABLIM1 plays antitumor roles in GBM progression and could be served as a novel biomarker to help predict GBM prognosis.

Novel deep learning-based solution for identification of prognostic subgroups in liver cancer (Hepatocellular carcinoma)

Background

Liver cancer (Hepatocellular carcinoma; HCC) prevalence is increasing and with poor clinical outcome expected it means greater understanding of HCC aetiology is urgently required. This study explored a deep learning solution to detect biologically important features that distinguish prognostic subgroups. A novel architecture of an Artificial Neural Network (ANN) trained with a customised objective function (L_RSC) was developed. The ANN should discover new data representations, to detect patient subgroups that are biologically homogenous (clustering loss) and similar in survival (survival loss) while removing noise from the data (reconstruction loss). The model was applied to TCGA-HCC multi-omics data and benchmarked against baseline models that only use a reconstruction objective function (BCE, MSE) for learning. With the baseline models, the new features are then filtered based on survival information and used for clustering patients. Different variants of the customised objective function, incorporating only reconstruction and clustering losses (L_RC); and reconstruction and survival losses (L_RS) were also evaluated. Robust features consistently detected were compared between models and validated in TCGA and LIRI-JP HCC cohorts.

Results

The combined loss (L_RSC) discovered highly significant prognostic subgroups (P-value = 1.55E−77) with more accurate sample assignment (Silhouette scores: 0.59–0.7) compared to baseline models (0.18–0.3). All L_RSC bottleneck features (N = 100) were significant for survival, compared to only 11–21 for baseline models. Prognostic subgroups were not explained by disease grade or risk factors. Instead L_RSC identified robust features including 377 mRNAs, many of which were novel (61.27%) compared to those identified by the other losses. Some 75 mRNAs were prognostic in TCGA, while 29 were prognostic in LIRI-JP also. L_RSC also identified 15 robust miRNAs including two novel (hsa-let-7g; hsa-mir-550a-1) and 328 methylation features with 71% being prognostic. Gene-enrichment and Functional Annotation Analysis identified seven pathways differentiating prognostic clusters.

Conclusions

Combining cluster and survival metrics with the reconstruction objective function facilitated superior prognostic subgroup identification. The hybrid model identified more homogeneous clusters that consequently were more biologically meaningful. The novel and prognostic robust features extracted provide additional information to improve our understanding of a complex disease to help reveal its aetiology. Moreover, the gene features identified may have clinical applications as therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04454-4.