Identification of a novel lysyl oxidase-like 2 alternative splicing isoform, LOXL2 Δe13, in esophageal squamous cell carcinoma.

Lysyl oxidase-like 2 (LOXL2) participates in every stage of cancer progression and promotes invasion and metastasis. In this study, we identified a novel alternative splicing isoform of LOXL2, namely LOXL2 Δe13, which lacked exon 13. Deletion of exon 13 caused an open reading frame shift and produced a truncated protein. LOXL2 Δe13 was expressed ubiquitously in cell lines and tissues and was mainly localized to the cytoplasm. Although it showed impaired deamination enzymatic activity compared with full-length LOXL2, LOXL2 Δe13 promoted the cell mobility and invasion of esophageal squamous cell carcinoma (ESCC) cells to greater degrees. In further research on the mechanisms, gene expression profiling and signaling pathway analysis revealed that LOXL2 Δe13 induced the expression of MAPK8 without affecting the FAK, AKT, and ERK signaling pathways. RNAi-mediated knockdown of MAPK8 could block the cell migration promoted by LOXL2De13, but it had little effect on that of full-length LOXL2. Our data suggest that LOXL2 Δe13 modulates the effects of cancer cell migration and invasion through a different mechanism from that of full-length LOXL2 and that it may play a very important role in tumor carcinogenesis and progression.

Exploration of potential roles of a new LOXL2 splicing variant using network knowledge in esophageal squamous cell carcinoma.

LOXL2 (lysyl oxidase-like 2), an enzyme that catalyzes oxidative deamination of lysine residue, is upregulated in esophageal squamous cell carcinoma (ESCC). A LOXL2 splice variant LOXL2-e13 and its wild type were overexpressed in ESCC cells followed by microarray analyses. In this study, we explored the potential role and molecular mechanism of LOXL2-e13 based on known protein-protein interactions (PPIs), following microarray analysis of KYSE150 ESCC cells overexpressing a LOXL2 splice variant, denoted by LOXL2-e13, or its wild-type counterpart. The differentially expressed genes (DEGs) of LOXL2-WT and LOXL2-e13 were applied to generate individual PPI subnetworks in which hundreds of DEGs interacted with thousands of other proteins. These two DEG groups were annotated by Functional Annotation Chart analysis in the DAVID bioinformatics database and compared. These results found many specific annotations indicating the potential specific role or mechanism for LOXL2-e13. The DEGs of LOXL2-e13, comparing to its wild type, were prioritized by the Random Walk with Restart algorithm. Several tumor-related genes such as ERO1L, ITGA3, and MAPK8 were found closest to LOXL2-e13. These results provide helpful information for subsequent experimental identification of the specific biological roles and molecular mechanisms of LOXL2-e13. Our study also provides a work flow to identify potential roles of splice variants with large scale data.

Identification of multiple risk factors for colorectal cancer relapse after laparoscopic radical resection.

BACKGROUND: Colorectal cancer (CRC) is a common life-threatening disease that often requires surgical intervention, such as laparoscopic radical resection. However, despite successful surgeries, some patients experience disease relapse. Identifying the risk factors for CRC relapse can help guide clinical interventions and improve patient outcomes. AIM: To determine the risk factors that may lead to CRC relapse after laparoscopic radical resection. METHODS: We performed a retrospective analysis using the baseline data of 140 patients with CRC admitted to our hospital between January 2018 and January 2020. All included participants were followed up until death or for 3 years. The baseline data and laboratory indicators were compared between the patients who experienced relapse and those who did not experienced relapse. RESULTS: Among the 140 patients with CRC, 30 experienced relapse within 3 years after laparoscopic radical resection and 110 did not experience relapse. The relapse group had a higher frequency of rectal tumors with low differentiation and lymphatic vessel invasion than that of the non-relapse group. The expression of serum markers and the prognostic nutritional index were lower, whereas the neutrophil-to-lymphocyte ratio, expression of cytokeratin 19 fragment antigen 21-1, vascular endothelial growth factor, and Chitinase-3-like protein 1 were significantly higher in the relapse group than those in the non-relapse group. The groups did not differ significantly based on other parameters. Logistic regression analysis revealed that all the above significantly altered factors were independent risk factors for CRC relapse. CONCLUSION: We identified multiple risk factors for CRC relapse following surgery, which can be considered for the clinical monitoring of patients to reduce disease recurrence and improve patient survival.

Predicting lymph node metastasis in colorectal cancer: An analysis of influencing factors to develop a risk model.

BACKGROUND: Colorectal cancer (CRC) is a significant global health issue, and lymph node metastasis (LNM) is a crucial prognostic factor. Accurate prediction of LNM is essential for developing individualized treatment strategies for patients with CRC. However, the prediction of LNM is challenging and depends on various factors such as tumor histology, clinicopathological features, and molecular characteristics. The most reliable method to detect LNM is the histopathological examination of surgically resected specimens; however, this method is invasive, time-consuming, and subject to sampling errors and interobserver variability. AIM: To analyze influencing factors and develop and validate a risk prediction model for LNM in CRC based on a large patient queue. METHODS: This study retrospectively analyzed 300 patients who underwent CRC surgery at two Peking University Shenzhen hospitals between January and December 2021. A deep learning approach was used to extract features potentially associated with LNM from primary tumor histological images while a logistic regression model was employed to predict LNM in CRC using machine-learning-derived features and clinicopathological variables as predictors. RESULTS: The prediction model constructed for LNM in CRC was based on a logistic regression framework that incorporated machine learning-extracted features and clinicopathological variables. The model achieved high accuracy (0.86), sensitivity (0.81), specificity (0.87), positive predictive value (0.66), negative predictive value (0.94), area under the curve for the receiver operating characteristic (0.91), and a low Brier score (0.10). The model showed good agreement between the observed and predicted probabilities of LNM across a range of risk thresholds, indicating good calibration and clinical utility. CONCLUSION: The present study successfully developed and validated a potent and effective risk-prediction model for LNM in patients with CRC. This model utilizes machine-learning-derived features extracted from primary tumor histology and clinicopathological variables, demonstrating superior performance and clinical applicability compared to existing models. The study provides new insights into the potential of deep learning to extract valuable information from tumor histology, in turn, improving the prediction of LNM in CRC and facilitate risk stratification and decision-making in clinical practice.

A truncated splice variant of human lysyl oxidase-like 2 promotes migration and invasion in esophageal squamous cell carcinoma.

Lysyl oxidase-like 2 (LOXL2) is a member of the lysyl oxidase family, which plays an important role in extracellular matrix protein biosynthesis and tumor progression. In the present study, we identified a novel splice variant, LOXL2Δ72, which encodes a peptide having the same N- and C-termini as wild-type LOXL2 (LOXL2WT), but lacks 72 nucleotides encoding 24 amino acids. LOXL2Δ72 had dramatically reduced enzymatic activity, and was no longer secreted. However, LOXL2Δ72 promoted greater cell migration and invasion than LOXL2WT. Furthermore, a dual luciferase reporter assay indicated that LOXL2Δ72 activates distinct signal transduction pathways compared to LOXL2WT, consistent with cDNA microarray data showing different expression levels of cell migration- and invasion-related genes induced following over-expression of each LOXL2 isoform. In particular, LOXL2Δ72 distinctly promoted esophageal squamous cell carcinoma (ESCC) cell migration via up-regulating the C-C motif chemokine ligand 28 (CCL28). Our results suggest that the new LOXL2 splice variant contributes to tumor progression by novel molecular mechanisms different from LOXL2WT.

Protein-protein interaction network analyses for elucidating the roles of LOXL2-delta72 in esophageal squamous cell carcinoma.

Lysyl oxidase-like 2 (LOXL2), a member of the lysyl oxidase (LOX) family, is a copper-dependent enzyme that catalyzes oxidative deamination of lysine residues on protein substrates. LOXL2 was found to be overexpressed in esophageal squamous cell carcinoma (ESCC) in our previous research. We later identified a LOXL2 splicing variant LOXL2-delta72 and we overexpressed LOXL2-delta72 and its wild type counterpart in ESCC cells following microarray analyses. First, the differentially expressed genes (DEGs) of LOXL2 and LOXL2-delta72 compared to empty plasmid were applied to generate protein-protein interaction (PPI) sub-networks. Comparison of these two sub-networks showed hundreds of different proteins. To reveal the potential specific roles of LOXL2- delta72 compared to its wild type, the DEGs of LOXL2-delta72 vs LOXL2 were also applied to construct a PPI sub-network which was annotated by Gene Ontology. The functional annotation map indicated the third PPI sub-network involved hundreds of GO terms, such as "cell cycle arrest", "G1/S transition of mitotic cell cycle", "interphase", "cell-matrix adhesion" and "cell-substrate adhesion", as well as significant "immunity" related terms, such as "innate immune response", "regulation of defense response" and "Toll signaling pathway". These results provide important clues for experimental identification of the specific biological roles and molecular mechanisms of LOXL2-delta72. This study also provided a work flow to test the different roles of a splicing variant with high-throughput data.