Effects of T2DM on postoperative outcome of patients with colorectal cancer: a study on the relationship between blood glucose control and survival rate.

To investigate the impact of type 2 diabetes (T2DM) on the prognosis of colorectal cancer (CRC). The data of 312 patients with CRC treated in the First Affiliated Hospital of Huzhou University from 2012 to 2018 were analyzed retrospectively. The patients were divided into a comorbidity group (n = 62) and a non-comorbidity group (n = 250) according to the presence of T2DM. The baseline data of the two groups were balanced by 1:2 propensity score matching (PSM). Kaplan-Meier analysis and Log-rank test were employed to compare the 5-year overall survival (OS) rates of patients. Cox regression model and inverse probability of treatment weighting (IPTW) were utilized to assess the influence of T2DM on 5-year OS of patients. Based on the results of Cox regression, a nomogram model of T2DM on 5-year OS of patients was constructed. A total of 62 patients in the comorbidity group and 124 patients in the non-comorbidity group were matched using PSM. The 5-year OS rate was lower in the comorbidity group than in the non-comorbidity group (82.23% VS 90.32%, P = 0.038). Subgroup analysis showed that the 5-year overall survival rate was higher in the good blood glucose control group than in the poor blood glucose control group (97.14% VS 62.96%, P<0.01). Multivariate Cox regression showed that the 5-year mortality risk in the comorbidity group was 2.641 times higher than that in the non-comorbidity group (P = 0.026). IPTW analysis showed that the 5-year risk of death in the comorbidity group was 2.458 times that of the non-comorbidity group (P = 0.019). The results showed that poor blood glucose control, BMI≥25 kg/m2, low differentiation, III/IV stage, and postoperative infection were independent factors affecting the 5-year overall survival rate of CRC patients (P<0.05). The ROC curve showed that the AUCs of the constructed model in predicting the 5-year OS in the training set and the testing set were 0.784 and 0.776, respectively. T2DM is identified as a risk factor for reduced 5-year survival among CRC patients, necessitating increased attention for this subgroup, particularly those with poor blood glucose control.

Deep learning-assisted low-cost autofluorescence microscopy for rapid slide-free imaging with virtual histological staining.

Slide-free imaging techniques have shown great promise in improving the histological workflow. For example, computational high-throughput autofluorescence microscopy by pattern illumination (CHAMP) has achieved high resolution with a long depth of field, which, however, requires a costly ultraviolet laser. Here, simply using a low-cost light-emitting diode (LED), we propose a deep learning-assisted framework of enhanced widefield microscopy, termed EW-LED, to generate results similar to CHAMP (the learning target). Comparing EW-LED and CHAMP, EW-LED reduces the cost by 85×, shortening the image acquisition time and computation time by 36× and 17×, respectively. This framework can be applied to other imaging modalities, enhancing widefield images for better virtual histology.

Exceeding the limit for microscopic image translation with a deep learning-based unified framework.

Deep learning algorithms have been widely used in microscopic image translation. The corresponding data-driven models can be trained by supervised or unsupervised learning depending on the availability of paired data. However, general cases are where the data are only roughly paired such that supervised learning could be invalid due to data unalignment, and unsupervised learning would be less ideal as the roughly paired information is not utilized. In this work, we propose a unified framework (U-Frame) that unifies supervised and unsupervised learning by introducing a tolerance size that can be adjusted automatically according to the degree of data misalignment. Together with the implementation of a global sampling rule, we demonstrate that U-Frame consistently outperforms both supervised and unsupervised learning in all levels of data misalignments (even for perfectly aligned image pairs) in a myriad of image translation applications, including pseudo-optical sectioning, virtual histological staining (with clinical evaluations for cancer diagnosis), improvement of signal-to-noise ratio or resolution, and prediction of fluorescent labels, potentially serving as new standard for image translation.

Lung Cancer Diagnosis on Virtual Histologically Stained Tissue Using Weakly Supervised Learning.

Lung adenocarcinoma (LUAD) is the most common primary lung cancer and accounts for 40% of all lung cancer cases. The current gold standard for lung cancer analysis is based on the pathologists' interpretation of hematoxylin and eosin (H&E)-stained tissue slices viewed under a brightfield microscope or a digital slide scanner. Computational pathology using deep learning has been proposed to detect lung cancer on histology images. However, the histological staining workflow to acquire the H&E-stained images and the subsequent cancer diagnosis procedures are labor-intensive and time-consuming with tedious sample preparation steps and repetitive manual interpretation, respectively. In this work, we propose a weakly supervised learning method for LUAD classification on label-free tissue slices with virtual histological staining. The autofluorescence images of label-free tissue with histopathological information can be converted into virtual H&E-stained images by a weakly supervised deep generative model. For the downstream LUAD classification task, we trained the attention-based multiple-instance learning model with different settings on the open-source LUAD H&E-stained whole-slide images (WSIs) dataset from the Cancer Genome Atlas (TCGA). The model was validated on the 150 H&E-stained WSIs collected from patients in Queen Mary Hospital and Prince of Wales Hospital with an average area under the curve (AUC) of 0.961. The model also achieved an average AUC of 0.973 on 58 virtual H&E-stained WSIs, comparable to the results on 58 standard H&E-stained WSIs with an average AUC of 0.977. The attention heatmaps of virtual H&E-stained WSIs and ground-truth H&E-stained WSIs can indicate tumor regions of LUAD tissue slices. In conclusion, the proposed diagnostic workflow on virtual H&E-stained WSIs of label-free tissue is a rapid, cost effective, and interpretable approach to assist clinicians in postoperative pathological examinations. The method could serve as a blueprint for other label-free imaging modalities and disease contexts.

Improving prognosis and assessing adjuvant chemotherapy benefit in locally advanced rectal cancer with deep learning for MRI: A retrospective, multi-cohort study.

PURPOSE: Adjuvant therapy is recommended to minimize the risk of distant metastasis (DM) and local recurrence (LR) in patients with locally advanced rectal cancer (LARC). However, its role is controversial. We aimed to develop a pretreatment MRI-based deep learning model to predict LR, DM, and overall survival (OS) over 5 years after surgery and to identify patients benefitting from adjuvant chemotherapy (AC). MATERIALS AND METHODS: The multi-survival tasks network (MuST) model was developed in a primary cohort (n = 308) and validated using two external cohorts (n = 247, 245). An AC decision tree integrating the MuST-DM score, perineural invasion (PNI), and preoperative carbohydrate antigen 19-9 (CA19-9) was constructed to assess chemotherapy benefits and aid personalized treatment of patients. We also quantified the prognostic improvement of the decision tree. RESULTS: The MuST network demonstrated high prognostic accuracy in the primary and two external cohorts for the prediction of three different survival tasks. Within the stratified analysis and decision tree, patients with CA19-9 levels > 37 U/mL and high MuST-DM scores exhibited favorable chemotherapy efficacy. Similar results were observed in PNI-positive patients with low MuST-DM scores. PNI-negative patients with low MuST-DM scores exhibited poor chemotherapy efficacy. Based on the decision tree, 14 additional patients benefiting from AC and 391 patients who received over-treatment were identified in this retrospective study. CONCLUSION: The MuST model accurately and non-invasively predicted OS, DM, and LR. A specific and direct tool linking chemotherapy decisions and benefit quantification has also been provided.

Function and mechanism of MCM8 in the development and progression of colorectal cancer.

Colorectal cancer (CRC) has become a global health problem which has almost highest morbidity and mortality in all types of cancers. This study aimed to uncover the biological functions and underlying mechanism of MCM8 in the development and progression of CRC. The expression level of MCM8 was found to be upregulated in CRC tissues and significantly associated with tumor grade and patients' survival. Knocking down MCM8 expression in CRC cells could restrain cell growth and cell motility while promoting cell apoptosis in vitro, as well as inhibit tumor growth in xenograft mice model. Based on the RNA screening performing on CRC cells with or without MCM8 knockdown and the following IPA analysis, CHSY1 was identified as a potential target of MCM8 in CRC, whose expression was also found to be higher in tumor tissues than in normal tissues. Moreover, it was demonstrated that MCM8 may regulate the expression of CHSY1 through affecting its NEDD4-mediated ubiquitination, both of which synergistically execute tumor promotion effects on CRC. In conclusion, the outcomes of our study showed the first evidence that MCM8 act as a tumor promotor in CRC, and may be a promising therapeutic target of CRC treatment.

Noninvasive predictive models based on lifestyle analysis and risk factors for early-onset colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) incidence has increased among patients aged <50 years. Exploring high-risk factors and screening high-risk populations may help lower early-onset CRC (EO-CRC) incidence. We developed noninvasive predictive models for EO-CRC and investigated its risk factors. METHODS: This retrospective multicenter study collected information on 1756 patients (811 patients with EO-CRC and 945 healthy controls) from two medical centers in China. Sociodemographic features, clinical symptoms, medical and family history, lifestyle, and dietary factors were measured. Patients from one cohort were randomly assigned (8:2) to two groups for model establishment and internal validation, and another independent cohort was used for external validation. Multivariable logistic regression, random forest, and eXtreme Gradient Boosting (XGBoost) were performed to establish noninvasive predictive models for EO-CRC. Some variables in the model influenced EO-CRC occurrence and were further analyzed. Multivariable logistic regression analysis yielded adjusted odd ratios (ORs) and 95% confidence intervals (CIs). RESULTS: All three models showed good performance, with areas under the receiver operator characteristic curves (AUCs) of 0.82, 0.84, and 0.82 in the internal and 0.78, 0.79, and 0.78 in the external validation cohorts, respectively. Consumption of sweet (OR 2.70, 95% CI 1.89-3.86, P < 0.001) and fried (OR 2.16, 95% CI 1.29-3.62, P < 0.001) foods ≥3 times per week was significantly associated with EO-CRC occurrence. CONCLUSION: We established noninvasive predictive models for EO-CRC and identified multiple nongenetic risk factors, especially sweet and fried foods. The model has good performance and can help predict the occurrence of EO-CRC in the Chinese population.

Early Detection of Molecular Residual Disease and Risk Stratification for Stage I to III Colorectal Cancer via Circulating Tumor DNA Methylation.

Importance: Detection of molecular residual disease and risk stratification as early as possible may improve the treatment of patients with cancer. Efficient pragmatic tests are therefore required. Objective: To measure circulating tumor DNA (ctDNA) with 6 DNA methylation markers in blood samples and to evaluate the association of the presence of ctDNA with colorectal cancer (CRC) recurrence throughout the disease course. Design, Setting, and Participants: In this multicenter prospective longitudinal cohort study performed from December 12, 2019, to February 28, 2022, 350 patients with stage I to III CRC were recruited from 2 hospitals for collection of blood samples before and after surgery, during and after adjuvant chemotherapy, and every 3 months for up to 2 years. A multiplex, ctDNA methylation, quantitative polymerase chain reaction assay was used to detect ctDNA in plasma samples. Results: A total of 299 patients with stage I to III CRC were evaluated. Of 296 patients with preoperative samples, 232 (78.4%) tested positive for any of the 6 ctDNA methylation markers. A total of 186 patients (62.2%) were male, and the mean (SD) age was 60.1 (10.3) years. At postoperative month 1, ctDNA-positive patients were 17.5 times more likely to relapse than were ctDNA-negative patients (hazard ratio [HR], 17.5; 95% CI, 8.9-34.4; P < .001). The integration of ctDNA and carcinoembryonic antigen tests showed risk stratification for recurrence with an HR of 19.0 (95% CI, 8.9-40.7; P < .001). Furthermore, ctDNA status at postoperative month 1 was strongly associated with prognosis in patients treated with adjuvant chemotherapy of different durations and intensities. After adjuvant chemotherapy, ctDNA-positive patients had a significantly shorter recurrence-free survival than did the ctDNA-negative patients (HR, 13.8; 95% CI, 5.9-32.1; P < .001). Longitudinal ctDNA analysis after the postdefinitive treatment showed a discriminating effect in that ctDNA-positive patients had poorer recurrence-free survival than did the ctDNA-negative patients (HR, 20.6; 95% CI, 9.5-44.9; P < .001). The discriminating effect was enhanced (HR, 68.8; 95% CI, 18.4-257.7; P < .001) when ctDNA status was maintained longitudinally. Postdefinitive treatment analysis detected CRC recurrence earlier than radiologically confirmed recurrence, with a median lead time of 3.3 months (IQR, 0.5-6.5 months). Conclusions and Relevance: The findings of this cohort study suggest that longitudinal assessment of ctDNA methylation may enable the early detection of recurrence, potentially optimizing risk stratification and postoperative treatment of patients with CRC.

Enhanced De Novo Lipid Synthesis Mediated by FASN Induces Chemoresistance in Colorectal Cancer.

BACKGROUND: Oxaliplatin is one of the most widely used chemotherapy drugs for colorectal cancer (CRC). Resistance to oxaliplatin threatens the prognosis of CRC. Since previous studies have aroused interest in fatty acid metabolism in cancer, in this study, we determined whether fatty acid biosynthesis and the related regulating mechanism contribute to oxaliplatin resistance in CRC. METHODS: The effect of the fatty acid synthase (FASN) and its inhibitor Orlistat was characterized in Gene Expression Omnibus (GEO) databases, oxaliplatin-resistant cell lines, and xenografts. MRNA-seq and analysis identified related pathway changes after the application of Orlistat, which was verified by Western blotting. RESULTS: By leveraging the GEO databases, FASN and closely related gene signatures were identified as being correlated with the response to oxaliplatin-based chemotherapy and poor prognosis. Additionally, FASN-upregulated expression promoted oxaliplatin resistance in CRC cell lines. We then applied Orlistat, a typical FASN inhibitor, in cell culture and xenograft models of oxaliplatin-resistant CRC, which attenuated the resistance to oxaliplatin. Additionally, the combination of the FASN inhibitor and oxaliplatin significantly increased cell cycle arrest and facilitated apoptosis, partly due to the diminished phosphorylation of the MAPK/ERK and PI3K/AKT pathways. In vivo studies showed that inhibiting fatty acid biosynthesis with Orlistat restrained the growth of xenograft tumors and increased the responsiveness to oxaliplatin. CONCLUSIONS: Our study revealed that FASN enhanced resistance to oxaliplatin in CRC. The inhibition of FASN could rescue the response to oxaliplatin by regulating MAPK/ERK and PI3K/AKT pathways.

Early detection and prognosis prediction for colorectal cancer by circulating tumour DNA methylation haplotypes: A multicentre cohort study.

Background: Early detection and prognosis prediction of colorectal cancer (CRC) can significantly reduce CRC-related mortality. Recently, circulating tumour DNA (ctDNA) methylation has shown good application foreground in the early detection and prognosis prediction of multiple tumours. Methods: This multicentre cohort study evaluated ctDNA methylation haplotype patterns based on archived plasma samples (collected between 2010 and 2018) from 1138 individuals at two medical centres: Fudan University Shanghai Cancer Center (Shanghai, China) and Southern Medical University Nanfang Hospital (Guangzhou, Guangdong, China), including 366 healthy individuals, 182 patients with advanced adenoma (AA), and 590 patients with CRC. Samples were processed using the ColonES assay, a targeted bisulfite sequencing method that detects ctDNA methylation haplotype patterns in 191 genomic regions. Among these 1138 samples, 748 were used to develop a classification model, and 390 served as a blinded cohort for independent validation. The study is registered at https://register.clinicaltrials.gov with the unique identifier NCT03737591. Results: The model obtained from unblinded samples discriminated patients with CRC or AA from normal controls with high accuracy. In the blinded validation set, the ColonES assay achieved sensitivity values of 79.0% (95% confidence interval (CI), 66%-88%) in AA patients and 86.6% (95% CI, 81%-91%) in CRC patients with a specificity of 88.1% (95% CI, 81%-93%) in healthy individuals. The model area under the curve (AUC) for the blinded validation set was 0.903 for AA samples and 0.937 for CRC samples. Additionally, the prognosis of patients with high preoperative ctDNA methylation levels was worse than that of patients with low ctDNA methylation levels (p = 0.001 for relapse-free survival and p = 0.004 for overall survival). Interpretation: We successfully developed and validated an accurate, noninvasive detection method based on ctDNA methylation haplotype patterns that may enable early detection and prognosis prediction for CRC. Funding: The Grant of National Natural Science Foundation of China (No.81871958), National Natural Science Foundation of China (No. 82203215), Shanghai Science and Technology Committee (No. 19140902100), Scientific Research Fund of Fudan University (No.IDF159052), Shanghai Municipal Health Commission (SHWJRS 2021-99), and Shanghai Sailing Program (22YF1408800).

PTPRO represses colorectal cancer tumorigenesis and progression by reprogramming fatty acid metabolism.

BACKGROUND: Abnormal expression of protein tyrosine phosphatases (PTPs) has been reported to be a crucial cause of cancer. As a member of PTPs, protein tyrosine phosphatase receptor type O (PTPRO) has been revealed to play tumor suppressive roles in several cancers, while its roles in colorectal cancer (CRC) remains to be elucidated. Hence, we aimed to explore the roles and mechanisms of PTPRO in CRC initiation and progression. METHODS: The influences of PTPRO on the growth and liver metastasis of CRC cells and the expression patterns of different lipid metabolism enzymes were evaluated in vitro and in vivo. Molecular and biological experiments were conducted to uncover the underpinning mechanisms of dysregulated de novo lipogenesis and fatty acid ß-oxidation. RESULTS: PTPRO expression was notably downregulated in CRC liver metastasis compared to the primary cancer, and such a downregulation was associated with poor prognosis of patients with CRC. PTPRO silencing significantly promoted cell growth and liver metastasis. Compared with PTPRO wild-type mice, PTPRO-knockout mice developed more tumors and harbored larger tumor loads under treatment with azoxymethane and dextran sulfate sodium. Gene set enrichment analysis revealed that PTPRO downregulation was significantly associated with the fatty acid metabolism pathways. Blockage of fatty acid synthesis abrogated the effects of PTPRO silencing on cell growth and liver metastasis. Further experiments indicated that PTPRO silencing induced the activation of the AKT serine/threonine kinase (AKT)/mammalian target of rapamycin (mTOR) signaling axis, thus promoting de novo lipogenesis by enhancing the expression of sterol regulatory element-binding protein 1 (SREBP1) and its target lipogenic enzyme acetyl-CoA carboxylase alpha (ACC1) by activating the AKT/mTOR signaling pathway. Furthermore, PTPRO attenuation decreased the fatty acid oxidation rate by repressing the expression of peroxisome proliferator-activated receptor alpha (PPARα) and its downstream enzyme peroxisomal acyl-coenzyme A oxidase 1 (ACOX1) via activating the p38/extracellular signal-regulated kinase (ERK) mitogen-activated protein kinase (MAPK) signaling pathway. CONCLUSIONS: PTPRO could suppress CRC development and metastasis via modulating the AKT/mTOR/SREBP1/ACC1 and MAPK/PPARα/ACOX1 pathways and reprogramming lipid metabolism.

Bulk and single-cell transcriptome profiling reveal necroptosis-based molecular classification, tumor microenvironment infiltration characterization, and prognosis prediction in colorectal cancer.

BACKGROUND: Necroptosis is a new form of programmed cell death that is associated with cancer initiation, progression, immunity, and chemoresistance. However, the roles of necroptosis-related genes (NRGs) in colorectal cancer (CRC) have not been explored comprehensively. METHODS: In this study, we obtained NRGs and performed consensus molecular subtyping by "ConsensusClusterPlus" to determine necroptosis-related subtypes in CRC bulk transcriptomic data. The ssGSEA and CIBERSORT algorithms were used to evaluate the relative infiltration levels of different cell types in the tumor microenvironment (TME). Single-cell transcriptomic analysis was performed to confirm classification related to NRGs. NRG_score was developed to predict patients' survival outcomes with low-throughput validation in a patients' cohort from Fudan University Shanghai Cancer Center. RESULTS: We identified three distinct necroptosis-related classifications (NRCs) with discrepant clinical outcomes and biological functions. Characterization of TME revealed that there were two stable necroptosis-related phenotypes in CRC: a phenotype characterized by few TME cells infiltration but with EMT/TGF-pathways activation, and another phenotype recognized as immune-excluded. NRG_score for predicting survival outcomes was established and its predictive capability was verified. In addition, we found NRCs and NRG_score could be used for patient or drug selection when considering immunotherapy and chemotherapy. CONCLUSIONS: Based on comprehensive analysis, we revealed the potential roles of NRGs in the TME, and their correlations with clinicopathological parameters and patients' prognosis in CRC. These findings could enhance our understanding of the biological functions of necroptosis, which thus may aid in prognosis prediction, drug selection, and therapeutics development.

Ferroptosis-associated molecular classification characterized by distinct tumor microenvironment profiles in colorectal cancer.

Ferroptosis is a non-apoptotic form of cell death recognized in recent years. Nonetheless, the potential role of ferroptosis-associated genes in immune regulation and tumor microenvironment formation remains unknown. In this study, we characterized the ferroptosis-associated patterns of colorectal cancer through integrative analyses of multiple datasets with transcriptomics, genomics, and single-cell transcriptome profiling. Three distinct ferroptosis-associated clusters (FAC1, FAC2 and FAC3) were identified from 1251 CRC bulk samples, which were associated with different clinical outcomes and biological pathways. The TME characterization revealed that the three patterns were highly consistent with known immune profiles: immune-desert (FAC1), immune-inflamed (FAC2) and immune-excluded (FAC3), respectively. Ferroptosis-associated immune and stromal-activated genes were obtained and characterized by corresponding function in CRC tumorigenesis. Further single-cell analyses identified the ferroptosis-associated immune responding tumor cells and ferroptosis-associated stromal cells infiltration pattern. Based on the Fersig score, which was extracted from the ferroptosis phenotype-related signature, patients with lower Fersig score were characterized by prolonged survival time and effective immune responses. Collectively, we uncovered the ferroptosis-associated patterns associated with TME diversity and immune response phenotype. The Fersig we constructed could be the potential therapeutic target genes to improve the efficacy of patients' immunotherapy. The Fersig scoring scheme could enhance the understanding of TME infiltration associated with ferroptosis and prediction of immunotherapy efficacy.

Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients.

Limited previous studies focused on the death and progression risk stratification of colorectal cancer (CRC) lung metastasis patients. The aim of this study is to construct a nomogram model combing machine learning-pathomics, radiomics features, Immunoscore and clinical factors to predict the postoperative outcome of CRC patients with lung metastasis. In this study, a total of 103 CRC patients having metastases limited to lung and undergoing radical lung resection were identified. Patch-level convolutional neural network training in weakly supervised manner was used to perform whole slides histopathological images survival analysis. Synthetic minority oversampling technique and support vector machine classifier were used to identify radiomics features and build predictive signature. The Immunoscore for each patient was calculated from the density of CD3+ and CD8+ cells at the invasive margin and the center of metastatic tumor which were assessed on consecutive sections of automated digital pathology. Finally, pathomics and radiomics signatures were successfully developed to predict the overall survival (OS) and disease free survival (DFS) of patients. The predicted pathomics and radiomics scores are negatively correlated with Immunoscore and they are three independent prognostic factors for OS and DFS prediction. The combined nomogram showed outstanding performance in predicting OS (AUC = 0.860) and DFS (AUC = 0.875). The calibration curve and decision curve analysis demonstrated the considerable clinical usefulness of the combined nomogram. Taken together, the developed nomogram model consisting of machine learning-pathomics signature, radiomics signature, Immunoscore and clinical features could be reliable in predicting postoperative OS and DFS of colorectal lung metastasis patients.

High-Throughput, Label-Free and Slide-Free Histological Imaging by Computational Microscopy and Unsupervised Learning.

Rapid and high-resolution histological imaging with minimal tissue preparation has long been a challenging and yet captivating medical pursuit. Here, the authors propose a promising and transformative histological imaging method, termed computational high-throughput autofluorescence microscopy by pattern illumination (CHAMP). With the assistance of computational microscopy, CHAMP enables high-throughput and label-free imaging of thick and unprocessed tissues with large surface irregularity at an acquisition speed of 10 mm2 /10 s with 1.1-µm lateral resolution. Moreover, the CHAMP image can be transformed into a virtually stained histological image (Deep-CHAMP) through unsupervised learning within 15 s, where significant cellular features are quantitatively extracted with high accuracy. The versatility of CHAMP is experimentally demonstrated using mouse brain/kidney and human lung tissues prepared with various clinical protocols, which enables a rapid and accurate intraoperative/postoperative pathological examination without tissue processing or staining, demonstrating its great potential as an assistive imaging platform for surgeons and pathologists to provide optimal adjuvant treatment.

GT Factor ZmGT-3b Is Associated With Regulation of Photosynthesis and Defense Response to Fusarium graminearum Infection in Maize Seedling.

It is of critical importance for plants to correctly and efficiently allocate their resources between growth and defense to optimize fitness. Transcription factors (TFs) play crucial roles in the regulation of plant growth and defense response. Trihelix TFs display multifaceted functions in plant growth, development, and responses to various biotic and abiotic stresses. In our previous investigation of maize stalk rot disease resistance mechanism, we found a trihelix TF gene, ZmGT-3b, which is primed for its response to Fusarium graminearum challenge by implementing a rapid and significant reduction of its expression to suppress seedling growth and enhance disease resistance. The disease resistance to F. graminearum was consistently increased and drought tolerance was improved, while seedling growth was suppressed and photosynthesis activity was significantly reduced in the ZmGT-3b knockdown seedlings. Thus, the seedlings finally led to show a kind of growth-defense trade-off phenotype. Moreover, photosynthesis-related genes were specifically downregulated, especially ZmHY5, which encodes a conserved central regulator of seedling development and light responses; ZmGT-3b was confirmed to be a novel interacting partner of ZmHY5 in yeast and in planta. Constitutive defense responses were synchronically activated in the ZmGT-3b knockdown seedlings as many defense-related genes were significantly upregulated, and the contents of major cell wall components, such as lignin, were increased in the ZmGT-3b knockdown seedlings. These suggest that ZmGT-3b is involved in the coordination of the metabolism during growth-defense trade-off by optimizing the temporal and spatial expression of photosynthesis- and defense-related genes.

Lnc-RP11-536 K7.3/SOX2/HIF-1α signaling axis regulates oxaliplatin resistance in patient-derived colorectal cancer organoids.

BACKGROUND: Resistance to oxaliplatin is a major obstacle for the management of locally advanced and metastatic colon cancer (CC). Although long noncoding RNAs (lncRNAs) play key roles in CC, the relationships between lncRNAs and resistance to oxaliplatin have been poorly understood yet. METHODS: Chemo-sensitive and chemo-resistant organoids were established from colon cancer tissues of the oxaliplatin-sensitive or -resistant patients. Analysis of the patient cohort indicated that lnc-RP11-536 K7.3 had a potential oncogenic role in CC. Further, a series of functional in vitro and in vivo experiments were conducted to assess the effects of lnc-RP11-536 K7.3 on CC proliferation, glycolysis, and angiogenesis. RNA pull-down assay, luciferase reporter and fluorescent in situ hybridization assays were used to confirm the interactions between lnc-RP11-536 K7.3, SOX2 and their downstream target HIF-1α. RESULTS: In this study, we identified a novel lncRNA, lnc-RP11-536 K7.3, was associated with resistance to oxaliplatin and predicted a poor survival. Knockout of lnc-RP11-536 K7.3 inhibited the proliferation, glycolysis, and angiogenesis, whereas enhanced chemosensitivity in chemo-resistant organoids and CC cells both in vitro and in vivo. Furthermore, we found that lnc-RP11-536 K7.3 recruited SOX2 to transcriptionally activate USP7 mRNA expression. The accumulative USP7 resulted in deubiquitylation and stabilization of HIF-1α, thereby facilitating resistance to oxaliplatin. CONCLUSION: In conclusion, our findings indicated that lnc-RP11-536 K7.3 could promote proliferation, glycolysis, angiogenesis, and chemo-resistance in CC by SOX2/USP7/HIF-1α signaling axis. This revealed a new insight into how lncRNA could regulate chemosensitivity and provide a potential therapeutic target for reversing resistance to oxaliplatin in the management of CC.

MTAP Deficiency-Induced Metabolic Reprogramming Creates a Vulnerability to Cotargeting De Novo Purine Synthesis and Glycolysis in Pancreatic Cancer.

Methylthioadenosine phosphorylase (MTAP) is a key enzyme associated with the salvage of methionine and adenine that is deficient in 20% to 30% of pancreatic cancer. Our previous study revealed that MTAP deficiency indicates a poor prognosis for patients with pancreatic ductal adenocarcinoma (PDAC). In this study, bioinformatics analysis of The Cancer Genome Atlas (TCGA) data indicated that PDACs with MTAP deficiency display a signature of elevated glycolysis. Metabolomics studies showed that that MTAP deletion-mediated metabolic reprogramming enhanced glycolysis and de novo purine synthesis in pancreatic cancer cells. Western blot analysis revealed that MTAP knockout stabilized hypoxia-inducible factor 1α (HIF1α) protein via posttranslational phosphorylation. RIO kinase 1 (RIOK1), a downstream kinase upregulated in MTAP-deficient cells, interacted with and phosphorylated HIF1α to regulate its stability. In vitro experiments demonstrated that the glycolysis inhibitor 2-deoxy-d-glucose (2-DG) and the de novo purine synthesis inhibitor l-alanosine synergized to kill MTAP-deficient pancreatic cancer cells. Collectively, these results reveal that MTAP deficiency drives pancreatic cancer progression by inducing metabolic reprogramming, providing a novel target and therapeutic strategy for treating MTAP-deficient disease. SIGNIFICANCE: This study demonstrates that MTAP status impacts glucose and purine metabolism, thus identifying multiple novel treatment options against MTAP-deficient pancreatic cancer.

Comprehensive Transcriptomic Analysis Reveals Prognostic Value of an EMT-Related Gene Signature in Colorectal Cancer.

Lymph node metastasis (LNM) is closely related to the postoperative recurrence of colorectal cancer (CRC), and greatly affects patient survival. Conducting Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA), we found that the epithelial-mesenchymal transition (EMT) signaling pathway is the signaling pathway most relevant to the process of LNM. An EMT-related gene signature was identified from a discovery dataset obtained 489 patients using LIMMA and LASSO Cox methods. Six external independent dataset analyses including a total of 1,045 CRC patients and stratification analysis showed that EMT-related gene signature could sort out those high- and low-risk CRC patients accurately. Functional analysis and loss-of-function exploration in vitro and in vivo indicated that the EMT-related-signature-associated coding genes might play functional roles in the sophisticated regulation of CRC proliferation and metastasis. Prognostic nomograms integrating the EMT-related gene signature and clinicopathological risk factors were constructed for use as numerical prediction tools to assess clinical prognosis and clinical decision-makings. The comprehensive transcriptomic analysis in this article highlights the prognostic value of an EMT-related gene signature for postoperative disease recurrence in CRC patients and reveals a potential prognostic and therapeutic biomarker for CRC.