ROBO2 hampers malignant biological behavior and predicts a better prognosis in pancreatic adenocarcinoma

Subtype-WGME enables whole-genome-wide multi-omics cancer subtyping

We present an innovative strategy for integrating whole-genome-wide multi-omics data, which facilitates adaptive amalgamation by leveraging hidden layer features derived from high-dimensional omics data through a multi-task encoder. Empirical evaluations on eight benchmark cancer datasets substantiated that our proposed framework outstripped the comparative algorithms in cancer subtyping, delivering superior subtyping outcomes. Building upon these subtyping results, we establish a robust pipeline for identifying whole-genome-wide biomarkers, unearthing 195 significant biomarkers. Furthermore, we conduct an exhaustive analysis to assess the importance of each omic and non-coding region features at the whole-genome-wide level during cancer subtyping. Our investigation shows that both omics and non-coding region features substantially impact cancer development and survival prognosis. This study emphasizes the potential and practical implications of integrating genome-wide data in cancer research, demonstrating the potency of comprehensive genomic characterization. Additionally, our findings offer insightful perspectives for multi-omics analysis employing deep learning methodologies.

Downregulation of Roundabout guidance receptor 2 suppresses hepatocellular carcinoma progression by interacting with Y-box binding protein 1

Roundabout guidance receptor 2 (Robo2) is closely related to malignant tumors such as pancreatic cancer and liver fibrosis, but there is no relevant research on the role of Robo2 in HCC. The study will further explore the function and mechanism of Robo2 and its downstream target genes in HCC. Firstly, Robo2 protein levels in human HCC tissues and paired adjacent normal liver tissues were detected. Then we established HepG2 and Huh7 hepatoma cell lines with knock-down Robo2 by transfection with lentiviral vectors, and examined the occurrence of EMT, proliferation and apoptosis abilities in HCC cells by western blot, flow cytometry, wound healing assay and TUNEL staining. Then we verified the interaction between Robo2 and its target gene by Co-IP and immunofluorescence co-staining, and further explored the mechanism of Robo2 and YB-1 by rescue study. The protein expression level of Robo2 in HCC was considerably higher than that in the normal liver tissues. After successfully constructing hepatoma cells with knock-down Robo2, it was confirmed that down-regulated Robo2 suppressed EMT and proliferation of hepatoma cells, and accelerated the cell apoptosis. High-throughput sequencing and validation experiments verified that YB-1 was the downstream target gene of Robo2, and over-expression of YB-1 could reverse the apoptosis induced by Robo2 down-regulation and its inhibitory effect on EMT and proliferation. Robo2 deficiency inhibits EMT and proliferation of hepatoma cells and augments the cell apoptosis by regulating YB-1, thus inhibits the occurrence of HCC and provides a new strategy for the treatment of HCC.

SALL4 advances the proliferation and tumor cell stemness of colon cancer cells through the transcription and regulation of ROBO2

SALL4 is a transcription factor highly expressed in diverse cancers and is implicated in the development of cancer. SALL4 has been implied to play a cancer-promoting role in colon cancer (CC), but the molecular mechanism remains unclear. Chromatin immunoprecipitation assay and dual-luciferase assay were conducted to verify the binding relationship of SALL4 and ROBO2. qRT-PCR detected the mRNA expression levels of SALL4 and ROBO2, and the flow cytometry analyzed the cell cycle distribution. Western blot examined SALL4 expression, and cell cycle/cell stemness-related proteins. The impact of SALL4 and ROBO2 on the proliferation capacity of cells and tumor cell stemness was elucidated by MTT, colony formation, and sphere-forming assays. SALL4 and ROBO2 were up-regulated in CC, and SALL4 could activate the transcription of ROBO2. Down-regulated SALL4 was able to significantly restrain the proliferation capacity of CC cells and arrest the cell cycle in G0/G1 phase by repressing the expression of cyclin B, cyclin E, and cyclin D1. Besides, the rescue assay results indicated that up-regulated ROBO2 could reverse the repressive impact of down-regulated SALL4 on the proliferation of CC cells and accelerate the progression of the cell cycle, thus promoting the sphere-forming of tumor stem cells. SALL4 advanced the proliferation of CC and cell stemness through direct activation of ROBO2 expression, implied the novel mechanism of SALL4 in CC, and pointed out that SALL4/ROBO2 axis was likely to be a potential target for clinical treatment of CC.

ROBO3s: a novel ROBO3 short isoform promoting breast cancer aggressiveness

Basal-like breast cancer (BLBC) is a highly aggressive breast cancer subtype frequently associated with poor prognosis. Due to the scarcity of targeted treatment options, conventional cytotoxic chemotherapies frequently remain the standard of care. Unfortunately, their efficacy is limited as BLBC malignancies rapidly develop resistant phenotypes. Using transcriptomic and proteomic approaches in human and murine BLBC cells, we aimed to elucidate the molecular mechanisms underlying the acquisition of aggressive and chemotherapy-resistant phenotypes in these mammary tumors. Specifically, we identified and characterized a novel short isoform of Roundabout Guidance Receptor 3 (ROBO3s), upregulated in BLBC in response to chemotherapy and encoding for a protein variant lacking the transmembrane domain. We established an important role for the ROBO3s isoform, mediating cancer stem cell properties by stimulating the Hippo-YAP signaling pathway, and thus driving resistance of BLBC cells to cytotoxic drugs. By uncovering the conservation of ROBO3s expression across multiple cancer types, as well as its association with reduced BLBC-patient survival, we emphasize its potential as a prognostic marker and identify a novel attractive target for anti-cancer drug development.

A 70‑RNA model based on SVR and RFE for predicting the pancreatic cancer clinical prognosis

Researches on the prognosis of pancreatic cancer is of great significance to improve the patient treatment effect and survival. Current researches mainly focus on the prediction of the survival status and the determination of prognostic markers. Each patient has its own characteristics, there is no report about the prediction of survival time. However, accurate prediction of survival time is critical for personalized medicine. In this paper, a hybrid algorithm of Support Vector Regression (SVR) and Recursive Feature Elimination (RFE) was used to construct a quantitative prediction model of Overall Survival (OS) for pancreatic cancer patients, 70 RNAs related to OS were determined, including 33 mRNAs, 28 lncRNAs, and 9 miRNAs. The results of 10-fold cross-validation (R2 is 0.9693) and the generalization ability (R2 is 0.9666) showed that the model has reliable predictive performance and these 70 RNAs are important factors influencing the OS of pancreatic cancer patients. To further study the relationship between RNA-RNA interaction and the survival, competitive endogenous RNA (ceRNA) regulation network was constructed. Degree centrality, betweenness centrality and closeness centrality of nodes in the ceRNA network showed that hsa-mir-570, hsa-mir-944, hsa-mir-6506, hsa-mir-3136, MMP16, PLGLB2, HPGD, FUT1, MFSD2A, SULT1E1, SLC13A5, ZNF488, F2RL2, TNFRSF8, TNFSF11, FHDC1, ISLR2 and THSD7B are hub nodes, which are key RNAs closely determining the OS of pancreatic cancer patients.