Thermodynamic Modeling of the Au-Ge-X (X = In, Sb, Si, Zn) Ternary Systems.

In this study, the CALPHAD approach was employed to model the thermodynamics of the Au-Ge-X (X = In, Sb, Si, Zn) ternary systems, leveraging experimental phase equilibria data and previous assessments of related binary subsystems. The solution phases were modeled as substitutional solutions, and their excess Gibbs energies were expressed using the Redlich-Kister polynomial. Owing to the unavailability of experimental data, the solubility of the third elements in the Au-In, Au-Sb, and Au-Zn binary intermetallic compounds was excluded from consideration. Additionally, stable ternary intermetallic compounds were not reported in the literature and, thus, were not taken into account in the present thermodynamic calculations. Calculations of liquidus projections, isothermal sections, and vertical sections for these ternary systems have been performed, aligning with existing experimental findings. These thermodynamic parameters form a vital basis for creating a comprehensive thermodynamic database for Au-Ge-based alloys, which is essential for the design and development of new high-temperature Pb-free solders.

Improving Laryngoscopy Image Analysis Through Integration of Global Information and Local Features in VoFoCD Dataset.

The diagnosis and treatment of vocal fold disorders heavily rely on the use of laryngoscopy. A comprehensive vocal fold diagnosis requires accurate identification of crucial anatomical structures and potential lesions during laryngoscopy observation. However, existing approaches have yet to explore the joint optimization of the decision-making process, including object detection and image classification tasks simultaneously. In this study, we provide a new dataset, VoFoCD, with 1724 laryngology images designed explicitly for object detection and image classification in laryngoscopy images. Images in the VoFoCD dataset are categorized into four classes and comprise six glottic object types. Moreover, we propose a novel Multitask Efficient trAnsformer network for Laryngoscopy (MEAL) to classify vocal fold images and detect glottic landmarks and lesions. To further facilitate interpretability for clinicians, MEAL provides attention maps to visualize important learned regions for explainable artificial intelligence results toward supporting clinical decision-making. We also analyze our model's effectiveness in simulated clinical scenarios where shaking of the laryngoscopy process occurs. The proposed model demonstrates outstanding performance on our VoFoCD dataset. The accuracy for image classification and mean average precision at an intersection over a union threshold of 0.5 (mAP50) for object detection are 0.951 and 0.874, respectively. Our MEAL method integrates global knowledge, encompassing general laryngoscopy image classification, into local features, which refer to distinct anatomical regions of the vocal fold, particularly abnormal regions, including benign and malignant lesions. Our contribution can effectively aid laryngologists in identifying benign or malignant lesions of vocal folds and classifying images in the laryngeal endoscopy process visually.

Integration Analysis of Single-Cell Multi-Omics Reveals Prostate Cancer Heterogeneity.

Prostate cancer (PCa) is an extensive heterogeneous disease with a complex cellular ecosystem in the tumor microenvironment (TME). However, the manner in which heterogeneity is shaped by tumors and stromal cells, or vice versa, remains poorly understood. In this study, single-cell RNA sequencing, spatial transcriptomics, and bulk ATAC-sequence are integrated from a series of patients with PCa and healthy controls. A stemness subset of club cells marked with SOX9highARlow expression is identified, which is markedly enriched after neoadjuvant androgen-deprivation therapy (ADT). Furthermore, a subset of CD8+CXCR6+ T cells that function as effector T cells is markedly reduced in patients with malignant PCa. For spatial transcriptome analysis, machine learning and computational intelligence are comprehensively utilized to identify the cellular diversity of prostate cancer cells and cell-cell communication in situ. Macrophage and neutrophil state transitions along the trajectory of cancer progression are also examined. Finally, the immunosuppressive microenvironment in advanced PCa is found to be associated with the infiltration of regulatory T cells (Tregs), potentially induced by an FAP+ fibroblast subset. In summary, the cellular heterogeneity is delineated in the stage-specific PCa microenvironment at single-cell resolution, uncovering their reciprocal crosstalk with disease progression, which can be helpful in promoting PCa diagnosis and therapy.

Wnt signaling: Modulating tumor-associated macrophages and related immunotherapeutic insights.

Wnt signaling pathways are highly conserved cascades that mediate multiple biological processes through canonical or noncanonical pathways, from embryonic development to tissue maintenance, but they also contribute to the pathogenesis of numerous cancers. Recent studies have revealed that Wnt signaling pathways critically control the interplay between cancer cells and tumor-associated macrophages (TAMs) in the tumor microenvironment (TME) and potentially impact the efficacy of cancer immunotherapy. In this review, we summarize the evidence that Wnt signaling pathways boost the maturation and infiltration of macrophages for immune surveillance in the steady state but also polarize TAMs toward immunosuppressive M2-like phenotypes for immune escape in the TME. Both cancer cells and TAMs utilize Wnt signaling to transmit signals, and this interaction is crucial for the carcinogenesis and progression of common solid cancers, such as colorectal, gastric, hepatocellular, breast, thyroid, prostate, kidney, and lung cancers; osteosarcoma; and glioma. Specifically, compared with those in solid cancers, Wnt signaling pathways play a distinct role in the pathogenesis of leukemia. Efforts to develop Wnt-based drugs for cancer treatment are still ongoing, and some indeed enhance the anticancer immune response. We believe that the combination of Wnt signaling-based therapy with conventional or immune therapies is a promising therapeutic approach and can facilitate personalized treatment for most cancers.

Differential sedimentary evolution of typical aulacogens of Meso-Neoproterozoic in North China craton.

Many countries and regions in the world have obtained industrial oil flow in the Meso-Neoproterozoic sedimentary strata and formed commercial exploitation in recent years. The development horizon of high-quality source rocks in the Proterozoic in North China can be compared with the international, indicating that the Meso-Neoproterozoic in North China has great exploration potential. The sedimentary characteristics of typical aulacogen in multiple cratons in the Meso-Neoproterozoic North China Craton are compared and studied by using field outcrop data, drilling data and analysis and test data, aiming to provide sedimentary support for the prediction of oil and gas distribution and evaluation of exploration field in the Meso-Neoproterozoic in this area. The results show that there are four sedimentary systems in the study area, including Marine clastic rock sedimentary system, Marine carbonate sedimentary system, Marine-continental transitional facies sedimentary system and glacial sedimentary system. They are divided into seven sedimentary facies types: barrier coastal facies, non-barrier coast facies, shallow shelf facies, carbonate platform facies, reef facies, fan delta facies and glacial facies, and further divided into 15 subfacies and 21 microfacies. On this basis, the Meso-Neoproterozoic sedimentary filling sequences of two typical aulacogens, Yanliao and Xiong 'er, in the study area are clarified, showing that the formation time of each sedimentary filling sequence stage of different aulacogens is different, and the rock characteristics, lithology combination, lithologic structure, contact relationship, vertical sequence and sedimentary facies assemblage of the same sedimentary filling sequence stage are obviously different. The filling characteristics of the two aulacogens completely record the geological events related to the breakup of the Colombian supercontinent.

CropGS-Hub: a comprehensive database of genotype and phenotype resources for genomic prediction in major crops.

The explosive amount of multi-omics data has brought a paradigm shift both in academic research and further application in life science. However, managing and reusing the growing resources of genomic and phenotype data points presents considerable challenges for the research community. There is an urgent need for an integrated database that combines genome-wide association studies (GWAS) with genomic selection (GS). Here, we present CropGS-Hub, a comprehensive database comprising genotype, phenotype, and GWAS signals, as well as a one-stop platform with built-in algorithms for genomic prediction and crossing design. This database encompasses a comprehensive collection of over 224 billion genotype data and 434 thousand phenotype data generated from >30 000 individuals in 14 representative populations belonging to 7 major crop species. Moreover, the platform implemented three complete functional genomic selection related modules including phenotype prediction, user model training and crossing design, as well as a fast SNP genotyper plugin-in called SNPGT specifically built for CropGS-Hub, aiming to assist crop scientists and breeders without necessitating coding skills. CropGS-Hub can be accessed at https://iagr.genomics.cn/CropGS/.

Gut microbiome-based machine learning for diagnostic prediction of liver fibrosis and cirrhosis: a systematic review and meta-analysis.

BACKGROUND: Invasive detection methods such as liver biopsy are currently the gold standard for diagnosing liver cirrhosis and can be used to determine the degree of liver fibrosis and cirrhosis. In contrast, non-invasive diagnostic methods, such as ultrasonography, elastography, and clinical prediction scores, can prevent patients from invasiveness-related discomfort and risks and are often chosen as alternative or supplementary diagnostic methods for liver fibrosis or cirrhosis. However, these non-invasive methods cannot specify the pathological grading and early diagnosis of the lesions. Recent studies have revealed that gut microbiome-based machine learning can be utilized as a non-invasive diagnostic technique for liver cirrhosis or fibrosis, but there is no evidence-based support. Therefore, this study conducted a systematic review and meta-analysis for the first time to investigate the accuracy of machine learning based on the gut microbiota in the prediction of liver fibrosis and cirrhosis. METHODS: A comprehensive and systematic search of publications published before April 2th, 2023 in PubMed, Cochrane Library, Embase, and Web of Science was conducted for relevant studies on the application of gut microbiome-based metagenomic sequencing modeling technology to the diagnostic prediction of liver cirrhosis or fibrosis. A bivariate mixed-effects model and Stata software 15.0 were adopted for the meta-analysis. RESULTS: Ten studies were included in the present study, involving 11 prediction trials and 838 participants, 403 of whom were fibrotic and cirrhotic patients. Meta-analysis showed the pooled sensitivity (SEN) = 0.81 [0.75, 0.85], specificity (SEP) = 0.85 [0.77, 0.91], positive likelihood ratio (PLR) = 5.5 [3.6, 8.7], negative likelihood ratio (NLR) = 0.23 [0.18, 0.29], diagnostic odds ratio (DOR) = 24 [14, 41], and area under curve (AUC) = 0.86 [0.83-0.89]. The results demonstrated that machine learning methods had excellent potential to analyze gut microbiome data and could effectively predict liver cirrhosis or fibrosis. Machine learning provides a powerful tool for non-invasive prediction and diagnosis of liver cirrhosis or liver fibrosis, with broad clinical application prospects. However, these results need to be interpreted with caution due to limited clinical data. CONCLUSION: Gut microbiome-based machine learning can be utilized as a practical, non-invasive technique for the diagnostic prediction of liver cirrhosis or fibrosis. However, most of the included studies applied the random forest algorithm in modeling, so a diversified prediction system based on microorganisms is needed to improve the non-invasive detection of liver cirrhosis or fibrosis.

MANet: Multi-branch attention auxiliary learning for lung nodule detection and segmentation.

BACKGROUND AND OBJECTIVE: Pulmonary nodule detection and segmentation are currently two primary tasks in analyzing chest computed tomography (Chest CT) in order to detect signs of lung cancer, thereby providing early treatment measures to reduce mortality. Even though there are many proposed methods to reduce false positives for obtaining effective detection results, distinguishing between the pulmonary nodule and background region remains challenging because their biological characteristics are similar and varied in size. The purpose of our work is to propose a method for automatic nodule detection and segmentation in Chest CT by enhancing the feature information of pulmonary nodules. METHODS: We propose a new UNet-based backbone with multi-branch attention auxiliary learning mechanism, which contains three novel modules, namely, Projection module, Fast Cascading Context module, and Boundary Enhancement module, to further enhance the nodule feature representation. Based on that, we build MANet, a lung nodule localization network that simultaneously detects and segments precise nodule positions. Furthermore, our MANet contains a Proposal Refinement step which refines initially generated proposals to effectively reduce false positives and thereby produce the segmentation quality. RESULTS: Comprehensive experiments on the combination of two benchmarks LUNA16 and LIDC-IDRI show that our proposed model outperforms state-of-the-art methods in the tasks of nodule detection and segmentation tasks in terms of FROC, IoU, and DSC metrics. Our method reports an average FROC score of 88.11% in lung nodule detection. For the lung nodule segmentation, the results reach an average IoU score of 71.29% and a DSC score of 82.74%. The ablation study also shows the effectiveness of the new modules which can be integrated into other UNet-based models. CONCLUSIONS: The experiments demonstrated our method with multi-branch attention auxiliary learning ability are a promising approach for detecting and segmenting the pulmonary nodule instances compared to the original UNet design.

Effect of fracture risk in inhaled corticosteroids in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis.

BACKGROUND: The fracture risk of patients with chronic obstructive pulmonary disease (COPD) treated with inhaled corticosteroids is controversial. And some large-scale randomized controlled trials have not solved this problem. The purpose of our systematic review and meta-analysis including 44 RCTs is to reveal the effect of inhaled corticosteroids on the fracture risk of COPD patients. METHODS: Two reviewers independently retrieved randomized controlled trials of inhaled corticosteroids or combinations of inhaled corticosteroids in the treatment of COPD from PubMed, Embase, Medline, Cochrane Library, and Web of Science. The primary outcome was a fracture event. This study was registered at PROSPERO (CRD42022366778). RESULTS: Forty-four RCTs were performed in 87,594 patients. Inhaled therapy containing ICSs (RR, 1.19; 95%CI, 1.04-1.37; P = 0.010), especially ICS/LABA (RR, 1.30; 95%CI, 1.10-1.53; P = 0.002) and triple therapy (RR, 1.49; 95%CI, 1.03-2.17; P = 0.04) were significantly associated with the increased risk of fracture in COPD patients when compared with inhaled therapy without ICSs. Subgroup analyses showed that treatment duration ≥ 12 months (RR, 1.19; 95%CI, 1.04-1.38; P = 0.01), budesonide therapy (RR, 1.64; 95%CI., 1.07-2.51; P = 0.02), fluticasone furoate therapy (RR, 1.37; 95%CI, 1.05-1.78; P = 0.02), mean age of study participants ≥ 65 (RR, 1.27; 95%CI, 1.01-1.61; P = 0.04), and GOLD stage III(RR, 1.18; 95%CI, 1.00-1.38; P = 0.04) were significantly associated with an increased risk of fracture. In addition, budesonide ≥ 320 ug bid via MDI (RR, 1.75; 95%CI, 1.07-2.87; P = 0.03) was significantly associated with the increased risk of fracture. CONCLUSION: Inhalation therapy with ICSs, especially ICS/LABA or triple therapy, increased the risk of fracture in patients with COPD compared with inhaled therapy without ICS. Treatment duration, mean age of participants, GOLD stage, drug dosage form, and drug dose participated in this association. Moreover, different inhalation devices of the same drug also had differences in risk of fracture.

The epigenetic modifier lysine methyltransferase 2C is frequently mutated in gastric remnant carcinoma.

Gastric remnant carcinoma (GRC), which occurs in the stomach after partial gastrectomy, is a rare and aggressive form of gastric adenocarcinoma (GAC). Comprehensive profiling of genomic mutations in GRC could provide the basis for elucidating the origin and characteristics of this cancer. Herein, whole-exome sequencing (WES) was performed on 36 matched tumor-normal samples from patients with GRC and identified recurrent mutations in epigenetic modifiers, notably KMT2C, ARID1A, NSD1, and KMT2D, in 61.11% of cases. Mutational signature analysis revealed a low frequency of microsatellite instability (MSI) in GRC, which was further identified by MSIsensor, MSI-polymerase chain reaction, and immunohistochemistry analysis. Comparative analysis demonstrated that GRC had a distinct mutation spectrum compared to that of GAC in The Cancer Genome Atlas samples, with a significantly higher mutation rate of KMT2C. Targeted deep sequencing (Target-seq) of an additional 25 paired tumor-normal samples verified the high mutation frequency (48%) of KMT2C in GRC. KMT2C mutations correlated with poor overall survival in both WES and Target-seq cohorts and were independent prognosticators in GRC. In addition, KMT2C mutations were positively correlated with favorable outcomes in immune checkpoint inhibitor-treated pan-cancer patients and associated with higher intratumoral CD3+ , CD8+ tumor-infiltrating lymphocyte counts, and PD-L1 expression in GRC samples (p = 0.018, 0.092, 0.047, 0.010, and 0.034, respectively). Our dataset provides a platform for information and knowledge mining of the genomic characteristics of GRC and helps to frame new therapeutic approaches for this disease.

Solidification Behavior of Dy-Tb-Fe Alloys through Experimental Study and Thermodynamic Calculation.

In this work, the solidification microstructure and phase transitions of Dy-Tb-Fe alloy samples were studied by using scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS), X-ray diffraction (XRD) and differential thermal analysis (DTA). No stable ternary compound was detected in the present experiments. The phase transformation temperatures of eight Dy-Tb-Fe alloy samples were measured. Based on the experimental results determined in this work and reported in the literature, the phase equilibria of the Dy-Tb-Fe system was calculated using the CALPHAD method. The calculated vertical sections are consistent with the experimental results determined in this work and reported in the literature. Furthermore, in combination with the experimental solidification microstructure, the solidification behavior of Dy-Tb-Fe alloy samples was analyzed through the thermodynamic calculation with the Gulliver-Scheil non-equilibrium model. The simulated results agree well with the experimental results. This indicates that the reasonable thermodynamic parameters of the Dy-Tb-Fe system were finally obtained.

Advanced gastric cancer achieving major pathologic regression after chemoimmunotherapy combined with hypofractionated radiotherapy: A case report.

BACKGROUND: Currently, chemotherapy combined with immunotherapy is the established first-line standard treatment for advanced gastric cancer (GC). In addition, the combination of radiotherapy and immunotherapy is considered a promising treatment strategy. CASE SUMMARY: In this report, we present a case of achieving nearly complete remission of highly advanced GC with comprehensive therapies. A 67-year-old male patient was referred to the hospital because he presented with dyspepsia and melena for several days. Based on fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT), endoscopic examination and abdominal CT, he was diagnosed with GC with a massive lesion and two distant metastatic lesions. The patient received mFOLFOX6 regimen chemotherapy, nivolumab and a short course of hypofractionated radiotherapy (4 Gy × 6 fractions) targeting the primary lesion. After the completion of these therapies, the tumor and the metastatic lesions showed a partial response. After having this case discussed by a multidisciplinary team, the patient underwent surgery, including total gastrectomy and D2 lymph node dissection. Postoperative pathology showed that major pathological regression of the primary lesion was achieved. Chemoimmunotherapy started four weeks after surgery, and examination was performed every three months. Since surgery, the patient has been stable and healthy with no evidence of recurrence. CONCLUSION: The combination of radiotherapy and immunotherapy for GC is worthy of further exploration.

Dynamic pruning group equivariant network for motor imagery EEG recognition.

Introduction: The decoding of the motor imaging electroencephalogram (MI-EEG) is the most critical part of the brain-computer interface (BCI) system. However, the inherent complexity of EEG signals makes it challenging to analyze and model them. Methods: In order to effectively extract and classify the features of EEG signals, a classification algorithm of motor imagery EEG signals based on dynamic pruning equal-variant group convolutional network is proposed. Group convolutional networks can learn powerful representations based on symmetric patterns, but they lack clear methods to learn meaningful relationships between them. The dynamic pruning equivariant group convolution proposed in this paper is used to enhance meaningful symmetric combinations and suppress unreasonable and misleading symmetric combinations. At the same time, a new dynamic pruning method is proposed to dynamically evaluate the importance of parameters, which can restore the pruned connections. Results and Discussion: The experimental results show that the pruning group equivariant convolution network is superior to the traditional benchmark method in the benchmark motor imagery EEG data set. This research can also be transferred to other research areas.

Inhibition of signal peptidase complex expression affects the development and survival of Schistosoma japonicum.

Background: Schistosomiasis, the second most neglected tropical disease defined by the WHO, is a significant zoonotic parasitic disease infecting approximately 250 million people globally. This debilitating disease has seriously threatened public health, while only one drug, praziquantel, is used to control it. Because of this, it highlights the significance of identifying more satisfactory target genes for drug development. Protein translocation into the endoplasmic reticulum (ER) is vital to the subsequent localization of secretory and transmembrane proteins. The signal peptidase complex (SPC) is an essential component of the translocation machinery and functions to cleave the signal peptide sequence (SP) of secretory and membrane proteins entering the ER. Inhibiting the expression of SPC can lead to the abolishment or weaker cleavage of the signal peptide, and the accumulation of uncleaved protein in the ER would affect the survival of organisms. Despite the evident importance of SPC, in vivo studies exploring its function have yet to be reported in S. japonicum. Methods: The S. japonicum SPC consists of four proteins: SPC12, SPC18, SPC22 and SPC25. RNA interference was used to investigate the impact of SPC components on schistosome growth and development in vivo. qPCR and in situ hybridization were applied to localize the SPC25 expression. Mayer's carmalum and Fast Blue B staining were used to observe morphological changes in the reproductive organs of dsRNA-treated worms. The effect of inhibitor treatment on the worm's viability and pairing was also examined in vitro. Results: Our results showed that RNAi-SPC delayed the worm's normal development and was even lethal for schistosomula in vivo. Among them, the expression of SPC25 was significantly higher in the developmental stages of the reproductive organs in schistosomes. Moreover, SPC25 possessed high expression in the worm tegument, testes of male worms and the ovaries and vitellarium of female worms. The SPC25 knockdown led to the degeneration of reproductive organs, such as the ovaries and vitellarium of female worms. The SPC25 exhaustion also reduced egg production while reducing the pathological damage of the eggs to the host. Additionally, the SPC-related inhibitor AEBSF or suppressing the expression of SPC25 also impacted cultured worms' pairing and viability in vitro. Conclusions: These data demonstrate that SPC is necessary to maintain the development and reproduction of S. japonicum. This research provides a promising anti-schistosomiasis drug target and discovers a new perspective on preventing worm fecundity and maturation.

Support of deep learning to classify vocal fold images in flexible laryngoscopy.

PURPOSE: To collect a dataset with adequate laryngoscopy images and identify the appearance of vocal folds and their lesions in flexible laryngoscopy images by objective deep learning models. METHODS: We adopted a number of novel deep learning models to train and classify 4549 flexible laryngoscopy images as no vocal fold, normal vocal folds, and abnormal vocal folds. This could help these models recognize vocal folds and their lesions within these images. Ultimately, we made a comparison between the results of the state-of-the-art deep learning models, and another comparison of the results between the computer-aided classification system and ENT doctors. RESULTS: This study exhibited the performance of the deep learning models by evaluating laryngoscopy images collected from 876 patients. The efficiency of the Xception model was higher and steadier than almost the rest of the models. The accuracy of no vocal fold, normal vocal folds, and vocal fold abnormalities on this model were 98.90 %, 97.36 %, and 96.26 %, respectively. Compared to our ENT doctors, the Xception model produced better results than a junior doctor and was near an expert. CONCLUSION: Our results show that current deep learning models can classify vocal fold images well and effectively assist physicians in vocal fold identification and classification of normal or abnormal vocal folds.

Digital Image Analysis of Ki67 Heterogeneity Improves the Diagnosis and Prognosis of Gastroenteropancreatic Neuroendocrine Neoplasms.

Ki67 is a reliable grading and prognostic biomarker of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). The intratumor heterogeneity of Ki67, correlated with tumor progression, is a valuable factor that requires image analysis. The application of digital image analysis (DIA) enables new approaches for the assessment of Ki67 heterogeneity distribution. We investigated the diagnostic utility of Ki67 heterogeneity parameters in the classification and grading of GEP-NENs and explored their clinical values with regard to their prognostic relevance. The DIA algorithm was performed on whole-slide images of 102 resection samples with Ki67 staining. Good agreement was observed between the manual and DIA methods in the hotspot evaluation (R2 = 0.94, P < .01). Using the grid-based region of interest approach, score-based heat maps provided a distinctive overview of the intratumoral distribution of Ki67 between neuroendocrine carcinomas and neuroendocrine tumors. The computation of heterogeneity parameters related to DIA-determined Ki67 showed that the coefficient of variation and Morisita-Horn index were directly related to the classification and grading of GEP-NENs and provided insights into distinguishing high-grade neuroendocrine neoplasms (grade 3 neuroendocrine tumor vs neuroendocrine carcinoma, P < .01). Our study showed that a high Morisita-Horn index correlated with poor disease-free survival (multivariate analysis: hazard ratio, 56.69), which was found to be the only independent predictor of disease-free survival in patients with GEP-NEN. These spatial biomarkers have an impact on the classification and grading of tumors and highlight the prognostic associations of tumor heterogeneity. Digitization of Ki67 variations provides a direct and objective measurement of tumor heterogeneity and better predicts the biological behavior of GEP-NENs.

PROX1-mediated epigenetic silencing of SIRT3 contributes to proliferation and glucose metabolism in colorectal cancer.

Prospero-related homeobox 1 (PROX1) is a homeobox transcription factor known to promote malignant transformation and stemness in human colorectal cancer (CRC). However, the biological function of PROX1 in metabolic rearrangement in CRC remains unclear. Here, we aimed to uncover the relationship between the expression profile and role of PROX1 and CRC cell glucose metabolism and to elucidate the underlying molecular mechanism. PROX1 expression was significantly upregulated in human CRC tissues and positively associated with the maximum standardized uptake value (SUVmax), a measure of tissue 18-fluoro-2-deoxy-D-glucose uptake and an indicator of glycolysis and tumor cell activity, in patients with CRC. Knockdown of PROX1 suppressed CRC cell proliferation and glucose metabolism in vitro and in vivo. Mechanistically, through a physical interaction, PROX1 recruited EZH2 to the SIRT3 promoter and inhibited SIRT3 promoter activity. Moreover, PROX1 or EZH2 knockdown decreased cell glycolysis by targeting SIRT3. Clinically, high PROX1 expression combined with low SIRT3 expression predicted poor prognosis in patients with CRC. Thus, our study suggests that the PROX1-EZH2 complex positively regulates cell proliferation and glucose metabolism by engaging SIRT3 in CRC, which may serve as a promising therapeutic strategy for CRC.

Comprehensive analysis of immune subtypes reveals the prognostic value of cytotoxicity and FAP+ fibroblasts in stomach adenocarcinoma.

BACKGROUND: The heterogeneity limits the effective application of immune checkpoint inhibitors for patients with stomach adenocarcinoma (STAD). Precise immunotyping can help select people who may benefit from immunotherapy and guide postoperative management by describing the characteristics of tumor microenvironment. METHODS: Gene expression profiles and clinical information of patients were collected from ACRG and TCGA-STAD datasets. The immune subtypes (ISs) were identified by consensus clustering analysis. The tumor immune microenvironments (TIME) of each IS were characterized using a series of immunogenomics methods and further confirmed by multiplex immunohistochemistry (mIHC) staining in clinical samples. Two online datasets and one in-house dataset were utilized to construct and validate a prognostic immune-related gene (IRG) signature. RESULTS: STAD patients were stratified into five reproducible ISs. IS1 (immune deserve subtype) had low immune infiltration and the highest degree of HER2 gene mutation. With abundant CD8+ T cells infiltration and activated cytotoxicity reaction, patients in the IS2 (immune-activated subtype) had the best overall survival (OS). IS3 and IS4 subtypes were both in the reactive stroma state and indicated the worst prognosis. However, IS3 (immune-inhibited subtype) was characterized by enrichment of FAP+ fibroblasts and upregulated TGF-ß signaling pathway, while IS4 (activated stroma subtype) was characterized by enrichment of ACTA2+ fibroblasts. In addition, mIHC staining confirmed that TGF-ß upregulated FAP+ fibroblasts were independent risk factor of OS. IS5 (chronic inflammation subtype) displayed moderate immune cells infiltration and had a relatively good survival. Lastly, we developed a nine-IRG signature model with a robust performance on overall survival prognostication. CONCLUSIONS: The immunotyping is indicative for characterize the TIME heterogeneity and the prediction of tumor prognosis for STADs, which may provide valuable stratification for the design of future immunotherapy.

Calcipotriol abrogates cancer-associated fibroblast-derived IL-8-mediated oxaliplatin resistance in gastric cancer cells via blocking PI3K/Akt signaling.

Activation of vitamin D receptor (VDR) in cancer-associated fibroblasts (CAFs) has been implicated in hesitating tumor progression and chemoresistance of several human malignancies. Yet, the role of VDR in CAF-induced chemotherapy resistance of gastric cancer (GC) cells remains elusive. In this study we first conducted immunohistochemistry analysis on tissue microarrays including 88 pairs of GC and normal mucosa samples, and provided clinical evidence that VDR was mainly expressed in gastric mucous cells but almost invisible in CAFs, and VDR expression was negatively correlated with malignant clinical phenotype and advanced stages, low VDR expression confers to poor overall survival rate of patients with GC. In a co-culture system of primary CAFs and cancer cells, we showed that treatment of HGC-27 and AGS GC cells with VDR ligand calcipotriol (Cal, 500 nM) significantly inhibited CAF-induced oxaliplatin resistance. By using RNA-sequencing and Human Cytokine Antibody Array, we demonstrated that IL-8 secretion from CAFs induced oxaliplatin resistance via activating the PI3K/AKT pathway in GC, whereas Cal treatment greatly attenuated the tumor-supportive effect of CAF-derived IL-8 on GC cells. Taken together, this study verifies the specific localization of VDR in GC tissues and demonstrates that activation of VDR abrogates CAF-derived IL-8-mediated oxaliplatin resistance in GC via blocking PI3K/Akt signaling, suggesting vitamin D supplementation as a potential strategy of enhancing the anti-tumor effect of chemotherapy in GC.

Genome architecture and diverged selection shaping pattern of genomic differentiation in wild barley.

Divergent selection of populations in contrasting environments leads to functional genomic divergence. However, the genomic architecture underlying heterogeneous genomic differentiation remains poorly understood. Here, we de novo assembled two high-quality wild barley (Hordeum spontaneum K. Koch) genomes and examined genomic differentiation and gene expression patterns under abiotic stress in two populations. These two populations had a shared ancestry and originated in close geographic proximity but experienced different selective pressures due to their contrasting micro-environments. We identified structural variants that may have played significant roles in affecting genes potentially associated with well-differentiated phenotypes such as flowering time and drought response between two wild barley genomes. Among them, a 29-bp insertion into the promoter region formed a cis-regulatory element in the HvWRKY45 gene, which may contribute to enhanced tolerance to drought. A single SNP mutation in the promoter region may influence HvCO5 expression and be putatively linked to local flowering time adaptation. We also revealed significant genomic differentiation between the two populations with ongoing gene flow. Our results indicate that SNPs and small SVs link to genetic differentiation at the gene level through local adaptation and are maintained through divergent selection. In contrast, large chromosome inversions may have shaped the heterogeneous pattern of genomic differentiation along the chromosomes by suppressing chromosome recombination and gene flow. Our research offers novel insights into the genomic basis underlying local adaptation and provides valuable resources for the genetic improvement of cultivated barley.