Meta-analysis shows the impacts of ecological restoration on greenhouse gas emissions.

International initiatives set ambitious targets for ecological restoration, which is considered a promising greenhouse gas mitigation strategy. Here, we conduct a meta-analysis to quantify the impacts of ecological restoration on greenhouse gas emissions using a dataset compiled from 253 articles. Our findings reveal that forest and grassland restoration increase CH4 uptake by 90.0% and 30.8%, respectively, mainly due to changes in soil properties. Conversely, wetland restoration increases CH4 emissions by 544.4%, primarily attributable to elevated water table depth. Forest and grassland restoration have no significant effect on N2O emissions, while wetland restoration reduces N2O emissions by 68.6%. Wetland restoration enhances net CO2 uptake, and the transition from net CO2 sources to net sinks takes approximately 4 years following restoration. The net ecosystem CO2 exchange of the restored forests decreases with restoration age, and the transition from net CO2 sources to net sinks takes about 3-5 years for afforestation and reforestation sites, and 6-13 years for clear-cutting and post-fire sites. Overall, forest, grassland and wetland restoration decrease the global warming potentials by 327.7%, 157.7% and 62.0% compared with their paired control ecosystems, respectively. Our findings suggest that afforestation, reforestation, rewetting drained wetlands, and restoring degraded grasslands through grazing exclusion, reducing grazing intensity, or converting croplands to grasslands can effectively mitigate greenhouse gas emissions.

N6-methyladenosine reader hnRNPA2B1 recognizes and stabilizes NEAT1 to confer chemoresistance in gastric cancer.

BACKGROUND: Chemoresistance is a major cause of treatment failure in gastric cancer (GC). Heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1) is an N6-methyladenosine (m6A)-binding protein involved in a variety of cancers. However, whether m6A modification and hnRNPA2B1 play a role in GC chemoresistance is largely unknown. In this study, we aimed to investigate the role of hnRNPA2B1 and the downstream mechanism in GC chemoresistance. METHODS: The expression of hnRNPA2B1 among public datasets were analyzed and validated by quantitative PCR (qPCR), Western blotting, immunofluorescence, and immunohistochemical staining. The biological functions of hnRNPA2B1 in GC chemoresistance were investigated both in vitro and in vivo. RNA sequencing, methylated RNA immunoprecipitation, RNA immunoprecipitation, and RNA stability assay were performed to assess the association between hnRNPA2B1 and the binding RNA. The role of hnRNPA2B1 in maintenance of GC stemness was evaluated by bioinformatic analysis, qPCR, Western blotting, immunofluorescence, and sphere formation assays. The expression patterns of hnRNPA2B1 and downstream regulators in GC specimens from patients who received adjuvant chemotherapy were analyzed by RNAscope and multiplex immunohistochemistry. RESULTS: Elevated expression of hnRNPA2B1 was found in GC cells and tissues, especially in multidrug-resistant (MDR) GC cell lines. The expression of hnRNPA2B1 was associated with poor outcomes of GC patients, especially in those who received 5-fluorouracil treatment. Silencing hnRNPA2B1 effectively sensitized GC cells to chemotherapy by inhibiting cell proliferation and inducing apoptosis both in vitro and in vivo. Mechanically, hnRNPA2B1 interacted with and stabilized long noncoding RNA NEAT1 in an m6A-dependent manner. Furthermore, hnRNPA2B1 and NEAT1 worked together to enhance the stemness properties of GC cells via Wnt/ß-catenin signaling pathway. In clinical specimens from GC patients subjected to chemotherapy, the expression levels of hnRNPA2B1, NEAT1, CD133, and CD44 were markedly elevated in non-responders compared with responders. CONCLUSION: Our findings indicated that hnRNPA2B1 interacts with and stabilizes lncRNA NEAT1, which contribute to the maintenance of stemness property via Wnt/ß-catenin pathway and exacerbate chemoresistance in GC.

Association of CDX2 and mucin expression with chemotherapeutic benefits in patients with stage II/III gastric cancer.

BACKGROUND: Better predictors of patients with stage II/III gastric cancer (GC) most likely to benefit from adjuvant chemotherapy are urgently needed. This study aimed to assess the ability of CDX2 and mucin markers to predict prognosis and fluorouracil-based adjuvant chemotherapy benefits. METHODS: CDX2 and mucin protein expressions were examined by immunohistochemistry and compared with survival and adjuvant chemotherapy benefits in a prospective evaluation cohort of 782 stage II/III GC patients. Then, the main findings were validated in an independent validation cohort (n = 386) and an external mRNA sequencing dataset (ACRG cohort, n = 193). RESULTS: In the evaluation cohort, CDX2, CD10, MUC2, MUC5AC, and MUC6 expressions were observed in 59.7%, 26.7%, 27.6%, 55.1%, and 57.7% of patients, respectively. However, only the expression of CDX2 was found to be associated with adjuvant chemotherapy benefits. Most importantly, CDX2-negative patients had a poorer prognosis when treated with surgery only, while the prognosis of CDX2-negative and CDX2-positive patients was similar when receiving postoperative adjuvant chemotherapy. Further analysis revealed that patients with CDX2 negative tumors benefited from chemotherapy (5-year overall survival rates: 60.0% with chemotherapy vs. 23.2% with surgery-only, p < 0.001), whereas patients with CDX2 positive tumors did not (pinteraction = 0.004). Consistent results were obtained in the validation and ACRG cohorts. CONCLUSIONS: Negative expression of CDX2 is an independent risk factor for survival in stage II/III GC, but subsequent adjuvant chemotherapy is able to compensate for this unfavorable effect. Therefore, active chemotherapy is more urgent for patients with negative CDX2 expression than for patients with positive CDX2 expression.

eEF1A1 promotes colorectal cancer progression and predicts poor prognosis of patients.

Colorectal cancer (CRC) is a major leading cause of cancer mortality worldwide in which dysregulated protein synthesis plays an etiologic role. The eukaryotic elongation factor 1 A1 (eEF1A1) exerts significant effects on protein synthesis by contributing to peptide chain extension. Whereas its role in CRC remains to be investigated. In this study, we found that the mRNA and protein levels of eEF1A1 were significantly upregulated in CRC cell lines and tissues. Elevated expression of eEF1A1 was correlated with shorter overall survival in 94 CRC patients. The inhibition of proliferation and cell cycle block were observed in CRC cells after eEF1A1 downregulation. Mechanistically, weighted gene correlation network analysis and further Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that mitogen-activated protein kinases (MAPKs) signaling pathways were significantly enriched in high-eEF1A1 expression group, and the levels of phosphorylated p38/JNK/ERK MAPK were dramatically decreased after eEF1A1 downregulation. Overexpression of eEF1A1 in CRC correlated with a poor prognosis. Collectively, this study determined the oncogenic role of eEF1A1 in CRC proliferation and tumorigenesis. eEF1A1 might be a promising therapeutic target and prognostic biomarker in CRC.

Visualization and quantification of de novo lipogenesis using a FASN-2A-GLuc mouse model.

Background: De novo lipogenesis (DNL) is a dynamic process that converts excess carbohydrates into fatty acids to maintain cellular homeostasis. Dysregulation of DNL is associated with diverse obesity-related diseases and many tumor types. Therefore, monitoring DNL in real-time with high sensitivity should be highly beneficial when screening therapeutic agents for their potential use as obesity treatments. Methods: A sequence coding for Gaussia luciferase (GLuc) preceded by a 2A peptide was inserted into the murine fatty acid synthase (FASN) genetic locus by homologous recombination to generate FASN-2A-GLuc mice. The luciferase mouse model was evaluated in conditions of physical and pharmacological stimuli by in vivo and ex vivo imaging. Results: The distribution of bioluminescence signals in different organs was identical to the FASN expression: high in white fat, brown fat, and the lungs. In addition, the bioluminescence signals accurately recapitulated the dynamic change of FASN in response to fasting and refeeding conditions. Moreover, with this murine reporter model, we also discovered that fatostatin, a synthetic inhibitor of sterol regulatory element-binding proteins, effectively inhibited DNL in multiple organs, especially in adipose tissues under a high-carbohydrate diet. Conclusions: Our FASN-2A-GLuc reporter mouse model proved to be a sensitive visualization tool for monitoring both systemic and organ-specific DNL in real time.

Development and validation of an eight-gene signature based predictive model to evaluate the prognosis of hepatocellular carcinoma patients: a bioinformatic study.

Background: Hepatocellular carcinoma (HCC) is a malignant tumor with a poor prognosis, however, biomarkers for the prognostic assessment of HCC remain suboptimal. Consequently, we aimed to develop a reliable tool for prognostic estimation of HCC. Methods: Differentially expressed genes (DEGs) between HCC and adjacent normal tissues in 3 Gene Expression Omnibus (GEO) datasets were identified, followed by hub gene selection and least absolute shrinkage and selection operator (LASSO) Cox regression to develop a prognostic gene signature. Kaplan-Meier survival analysis, univariate and multivariate Cox regression, time-dependent area under the curve (AUC), and integrated value of time-dependent AUC (iAUC) were used to assess the relationship between predictors and clinical outcomes in the training and validation datasets. Then we built nomograms including gene signature and clinicopathological factors to forecast the probability of death. Moreover, we performed quantitative real-time PCR (qPCR) to compare the expression of prognostic genes between HCC and adjacent normal tissues. Finally, the relationship between prognostic genes and tumor microenvironment (TME) was investigated using immune cell infiltration algorithms and single cell transcriptomic database. Results: Eight prognostic genes (CDC20, PTTG1, TOP2A, CXCL2, CXCL14, CYP2C9, MT1F, and GHR) were finally identified to construct the gene signature. Each patient's risk score was calculated according to the gene signature. Patients with high-risk scores showed worse outcomes in the training set [hazard ratio (HR) =3.404, P<0.001]. Risk score, age, body mass index (BMI), and TNM stage were identified as independent prognostic factors for overall survival (OS) in the training set. The nomogram including risk score and other independent prognostic factors showed better performance as opposed to the clinicopathological model. In the validation dataset, we obtained the similar results as well. Moreover, we found a close relationship between risk score and immune cell infiltration. Patients with high-risk scores had elevated expression of immune checkpoint genes, indicating that these patients may be more suitable for immunotherapy. Conclusions: We have established and validated an eight-gene based prognostic model, which could be an effective tool for the prognostic evaluation of HCC patients.

Bioinformatic analysis of cancer-associated fibroblast related gene signature as a predictive model in clinical outcomes and immune characteristics of gastric cancer.

Background: Gastric cancer (GC) has a high incidence and high mortality rate among Asian countries, and distinguishing predictive prognosis biomarkers for GC are essential. Cancer-associated fibroblasts (CAFs) play a significant role in the progression, immune evasion, and therapeutic resistance of GC. Therefore, CAF-associated genes might have huge potential as prognostic biomarkers for predicting tumor progression and survival rate in GC pateints. Methods: A sum of 1,134 GC patients from the The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD), GSE62254, and GSE84437 datasets as well as GC cohorts from Xijing hospital were included. Firstly, we performed univariate Cox regression analysis to identify CAF-associated prognostic genes. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to develop a CAF gene signature (CAFGS) in the TCGA-STAD training cohort. CAFGS's predictive performance was examined in both the training and validation cohorts, and the relationship between CAFGS and the tumor microenvironment (TME) was investigated by ssGSEA, CIBERSORT, TIMER, and ESTIMATE. Finally, a nomogram of CAFGS was established. Results: Ten CAF-associated genes (ANGPTL4, CPNE8, CST2, HTR1F, IL1RAP, NR1D1, NTAN1, OLFML2B, TMEM259, and VTN) were identified to develop CAFGS. A high CAFGS score represented a worse outcome for GC patients in four cohorts, and a strong correlation was found between CAFGS and the infiltration of immune cells. We showed that CAFs contribute to immune evasion and unfavorable prognoses of GC patients by promoting the formation of an immunosuppressive microenvironment, and a high level of CAF infiltration may attenuate the efficacy of immunotherapy. The nomogram based on CAFGS showed reasonable predictive ability and may deliver great clinical net benefits. Conclusions: We established a CAFGS model with 10 CAF-associated genes that had a great predictive value for GC prognosis and survival rate evaluation. This study could provide a novel insight for investigating the role of CAFs in GC.