PDLIM1 interacts with HK2 to promote gastric cancer progression through enhancing the Warburg effect via Wnt/ß-catenin signaling.

PDZ and LIM domain protein 1 (PDLIM1) is a cytoskeletal protein and is associated with the malignant pathological features of several tumors. However, the prognostic value of PDLIM1 and the molecular mechanisms by which it is involved in the metabolism and progression in gastric cancer (GC) are still unclear. The GEPIA database was used to predict the expression and prognosis of PDLIM1 in GC. qRT-PCR and western blot assays were applied to detect the mRNA and protein expression in GC tissues and cells. Loss- and gain-of-function experiments were performed to evaluate the biological role of PDLIM1 in GC cells. The Warburg effect was detected by a battery of glycolytic indicators. The interaction of PDLIM1 and hexokinase 2 (HK2) was determined by a co-immunoprecipitation assay. Furthermore, the modulatory effects of PDLIM1 and HK2 on Wnt/ß-catenin signaling were assessed. The results showed that PDLIM1 expression was upregulated in GC tissues and cells and was associated with a poor prognosis for GC patients. PDLIM1 inhibition reduced GC cell proliferation, migration and invasion and promoted cell apoptosis. In the glucose deprivation (GLU-D) condition, the PDLIM1 level was reduced and PDLIM1 overexpression led to an increase in glycolysis. Besides, mechanistic investigation showed that PDLIM1 interacted with HK2 to mediate biological behaviors and the glycolysis of GC through Wnt/ß-catenin signaling under glucose deprivation. In conclusion, PDLIM1 interacts with HK2 to promote gastric cancer progression by enhancing the Warburg effect via Wnt/ß-catenin signaling.

PERK activation by CCT020312 chemosensitizes colorectal cancer through inducing apoptosis regulated by ER stress.

Endoplasmic reticulum (ER) stress is a significant mechanism for chemoresistance to colorectal cancer (CRC) treatment. The RNA-like endoplasmic reticulum kinase (PERK) is critical for ER stress induction. In the present study, we attempted to explore whether PERK activator CCT020312 (CCT) could be effective for CRC treatment, and reveal the underlying mechanisms. We first found that CCT dose- and time-dependently reduced CRC cell proliferation. Importantly, it markedly improved the chemosensitivity of CRC cells that were drug-sensitive or -resistant to taxol treatment, as evidenced by the significantly decreased cell viability. Moreover, CCT at the non-toxic concentration exhibited obviously synergistic effects with taxol to induce apoptosis and cell cycle arrest in G2/M phase in vitro. In addition, we showed that CCT alone considerably induced ER stress in CRC cells through a dose- and time-dependent fashion. Meanwhile, CCT combined with taxol caused significant ER stress through improving phosphorylated PERK, eukaryotic translation initiation factor 2α (eIF2ɑ), C/EBP homologous protein (CHOP) and glucose-regulated protein 78 (GRP78). More studies showed that the interaction between PERK and GRP78 was a potential target for CCT to perform its regulatory events. Intriguingly, PERK knockdown markedly abolished the regulatory role of CCT and taxol cotreatments in cell proliferation suppression and apoptosis induction, indicating the importance of PERK for CCT to perform its anti-cancer bioactivity. Our in vivo experiments confirmed that CCT plus taxol dramatically reduced tumor growth in CRC xenografts. Together, all these results suggested that promoting PERK activation by CCT may be an effective therapeutic strategy to improve CRC to taxol treatment.

Prognostic significance of periostin in colorectal cancer.

Accumulating evidence suggests that periostin is frequently upregulated in tissue injury, inflammation, fibrosis and tumor progression. Periostin expression in cancer cells can promote metastatic potential of colorectal cancer (CRC) via activating PI3K/Akt signaling pathway. Moreover, periostin is observed mainly in tumor stroma and cytoplasm of cancer cells, which may facilitate aggressiveness of CRC. In this review, we summarize information regarding periostin to emphasize its role as a prognostic marker of CRC.

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.

Metabolomic Comparison of Patients With Colorectal Cancer at Different Anticancer Treatment Stages.

BACKGROUND: The difficulties of early diagnosis of colorectal cancer (CRC) result in a high mortality rate. The ability to predict the response of a patient to surgical resection or chemotherapy may be of great value for clinicians when planning CRC treatments. Metabolomics is an emerging tool for biomarker discovery in cancer research. Previous reports have indicated that the metabolic profile of individuals can be significantly altered between CRC patients and healthy controls. However, metabolic changes in CRC patients at different treatment stages have not been explored. METHODS: To this end, we performed nuclear magnetic resonance (NMR)-based metabolomic analysis to determine metabolite aberrations in CRC patients before and after surgical resection or chemotherapy. In general, a total of 106 urine samples from four clinical groups, namely, healthy volunteers (n = 31), presurgery CRC patients (n = 25), postsurgery CRC patients (n = 25), and postchemotherapy CRC patients (n = 25), were collected and subjected to further analysis. RESULTS: In the present study, we identified five candidate metabolites, namely, N-phenylacetylglycine, succinate, 4-hydroxyphenylacetate, acetate, and arabinose, in CRC patients compared with healthy individuals, three of which were reported for the first time. Furthermore, approximately ten metabolites were uniquely identified at each stage of CRC treatment, serving as good candidates for biomarker panel selection. CONCLUSION: In summary, these potential metabolite candidates may provide promising early diagnostic and monitoring approaches for CRC patients at different anticancer treatment stages.

Implications of the Receptor Tyrosine Kinase Axl in Gastric Cancer Progression.

BACKGROUND: Gastric cancer (GC) is an aggressive malignancy with high lethality. Systematic chemotherapy is the main therapeutic strategy for advanced GC patients. The overexpression of Axl is associated with poor prognosis and regulates tumor growth and metastasis in many types of cancer. However, the role of Axl in GC progression remains elusive. MATERIALS AND METHODS: Western blot and quantitative real-time PCR assay (RT-PCR) assays were used to detect the expression of Gas6, Axl, ZEB1 and epithelial-mesenchymal transition (EMT)-related markers in GC cells. Cell proliferation was determined by EdU cell proliferation assay and CCK-8 assay. Transwell invasion assay was performed to explore the effect of Axl and ZEB1 on cell invasion. Tumor xenografts and lung metastasis models were conducted to examine the effect of Axl on the growth and lung metastasis of GC cells. RESULTS: In our study, we found that high levels of Gas6 and Axl expression were associated with reduced overall survival (OS) in GC patients and the expression of Gas6 and Axl was upregulated in GC cell lines. Ectopic expression of Axl induced EMT and promoted GC cell invasion and proliferation. The knockdown of Axl inhibited EMT and suppressed the proliferation and invasion of GC cell. In vivo study showed that inhibition of Axl impaired tumor growth and lung metastasis of GC cells. Mechanistic investigations revealed that Axl promoted EMT, invasion, and proliferation via upregulating ZEB1 expression in GC cells. CONCLUSION: Our results demonstrated that the Gas6/Axl/ZEB1 signaling pathway regulated EMT, invasion, and proliferation in GC cells and might represent a potential therapeutic target for GC treatment.