TCR clustering by contrastive learning on antigen specificity.

Effective clustering of T-cell receptor (TCR) sequences could be used to predict their antigen-specificities. TCRs with highly dissimilar sequences can bind to the same antigen, thus making their clustering into a common antigen group a central challenge. Here, we develop TouCAN, a method that relies on contrastive learning and pretrained protein language models to perform TCR sequence clustering and antigen-specificity predictions. Following training, TouCAN demonstrates the ability to cluster highly dissimilar TCRs into common antigen groups. Additionally, TouCAN demonstrates TCR clustering performance and antigen-specificity predictions comparable to other leading methods in the field.

ACP-ESM: A novel framework for classification of anticancer peptides using protein-oriented transformer approach.

Anticancer peptides (ACPs) are a class of molecules that have gained significant attention in the field of cancer research and therapy. ACPs are short chains of amino acids, the building blocks of proteins, and they possess the ability to selectively target and kill cancer cells. One of the key advantages of ACPs is their ability to selectively target cancer cells while sparing healthy cells to a greater extent. This selectivity is often attributed to differences in the surface properties of cancer cells compared to normal cells. That is why ACPs are being investigated as potential candidates for cancer therapy. ACPs may be used alone or in combination with other treatment modalities like chemotherapy and radiation therapy. While ACPs hold promise as a novel approach to cancer treatment, there are challenges to overcome, including optimizing their stability, improving selectivity, and enhancing their delivery to cancer cells, continuous increasing in number of peptide sequences, developing a reliable and precise prediction model. In this work, we propose an efficient transformer-based framework to identify ACPs for by performing accurate a reliable and precise prediction model. For this purpose, four different transformer models, namely ESM, ProtBERT, BioBERT, and SciBERT are employed to detect ACPs from amino acid sequences. To demonstrate the contribution of the proposed framework, extensive experiments are carried on widely-used datasets in the literature, two versions of AntiCp2, cACP-DeepGram, ACP-740. Experiment results show the usage of proposed model enhances classification accuracy when compared to the literature studies. The proposed framework, ESM, exhibits 96.45% of accuracy for AntiCp2 dataset, 97.66% of accuracy for cACP-DeepGram dataset, and 88.51% of accuracy for ACP-740 dataset, thence determining new state-of-the-art. The code of proposed framework is publicly available at github (https://github.com/mstf-yalcin/acp-esm).

Discovery of potential antidiabetic peptides using deep learning.

Antidiabetic peptides (ADPs), peptides with potential antidiabetic activity, hold significant importance in the treatment and control of diabetes. Despite their therapeutic potential, the discovery and prediction of ADPs remain challenging due to limited data, the complex nature of peptide functions, and the expensive and time-consuming nature of traditional wet lab experiments. This study aims to address these challenges by exploring methods for the discovery and prediction of ADPs using advanced deep learning techniques. Specifically, we developed two models: a single-channel CNN and a three-channel neural network (CNN + RNN + Bi-LSTM). ADPs were primarily gathered from the BioDADPep database, alongside thousands of non-ADPs sourced from anticancer, antibacterial, and antiviral peptide datasets. Subsequently, data preprocessing was performed with the evolutionary scale model (ESM-2), followed by model training and evaluation through 10-fold cross-validation. Furthermore, this work collected a series of newly published ADPs as an independent test set through literature review, and found that the CNN model achieved the highest accuracy (90.48 %) in predicting the independent test set, surpassing existing ADP prediction tools. Finally, the application of the model was considered. SeqGAN was used to generate new candidate ADPs, followed by screening with the constructed CNN model. Selected peptides were then evaluated using physicochemical property prediction and structural forecasts for pharmaceutical potential. In summary, this study not only established robust ADP prediction models but also employed these models to screen a batch of potential ADPs, addressing a critical need in the field of peptide-based antidiabetic research.

Endothelial cell heterogeneity in colorectal cancer: tip cells drive angiogenesis.

This study aims to uncover the heterogeneity of endothelial cells (ECs) in colorectal cancer (CRC) and their crucial role in angiogenesis, with a special focus on tip cells. Using single-cell RNA sequencing to profile ECs, our data suggests that CRC ECs predominantly exhibit enhanced angiogenesis and decreased antigen presentation, a shift in phenotype largely steered by tip cells. We also observed that an increase in the density and proportion of tip cells correlates with CRC occurrence, progression, and poorer patient prognosis. Furthermore, we identified endothelial cell-specific molecule 1 (ESM1), specifically expressed in tip cells, sustains a VEGFA-KDR-ESM1 positive feedback loop, promoting angiogenesis and CRC proliferation and migration. We also found the enrichment of KDR in tip cells and spotlight a unique long-tail effect in VEGFA expression: while VEGFA is primarily expressed by epithelial cells, the highest level of VEGFA expression is found in individual myeloid cells. Moreover, we observed that effective PD-1 blockade immunotherapy significantly reduced tip cells, disrupting the VEGFA-KDR-ESM1 positive feedback loop in the process. Our investigation into the heterogeneity of ECs in CRC at a single-cell level offers important insights that may contribute to the development of more effective immunotherapies targeting tip cells in CRC.

PEL-PVP: Application of plant vacuolar protein discriminator based on PEFT ESM-2 and bilayer LSTM in an unbalanced dataset.

Plant vacuoles, play a crucial role in maintaining cellular stability, adapting to environmental changes, and responding to external pressures. The accurate identification of vacuolar proteins (PVPs) is crucial for understanding the biosynthetic mechanisms of intracellular vacuoles and the adaptive mechanisms of plants. In order to more accurately identify vacuole proteins, this study developed a new predictive model PEL-PVP based on ESM-2. Through this study, the feasibility and effectiveness of using advanced pre-training models and fine-tuning techniques for bioinformatics tasks were demonstrated, providing new methods and ideas for plant vacuolar protein research. In addition, previous datasets for vacuolar proteins were balanced, but imbalance is more closely related to the actual situation. Therefore, this study constructed an imbalanced dataset UB-PVP from the UniProt database，helping the model better adapt to the complexity and uncertainty in real environments, thereby improving the model's generalization ability and practicality. The experimental results show that compared with existing recognition techniques, achieving significant improvements in multiple indicators, with 6.08 %, 13.51 %, 11.9 %, and 5 % improvements in ACC, SP, MCC, and AUC, respectively. The accuracy reaches 94.59 %, significantly higher than the previous best model GraphIdn. This provides an efficient and precise tool for the study of plant vacuole proteins.

Clinical Utility of Protein Language Models in Resolution of Variants of Uncertain Significance in KCNQ1, KCNH2, and SCN5A Compared With Patch-Clamp Functional Characterization.

BACKGROUND: Genetic testing for cardiac channelopathies is the standard of care. However, many rare genetic variants remain classified as variants of uncertain significance (VUS) due to lack of epidemiological and functional data. Whether deep protein language models may aid in VUS resolution remains unknown. Here, we set out to compare how 2 deep protein language models perform at VUS resolution in the 3 most common long-QT syndrome-causative genes compared with the gold-standard patch clamp. METHODS: A total of 72 rare nonsynonymous VUS (9 KCNQ1, 19 KCNH2, and 50 SCN5A) were engineered by site-directed mutagenesis and expressed in either HEK293 cells or TSA201 cells. Whole-cell patch-clamp technique was used to functionally characterize these variants. The protein language models, ESM1b and AlphaMissense, were used to predict the variant effect of missense variants and compared with patch clamp. RESULTS: Considering variants in all 3 genes, the ESM1b model had a receiver operator curve-area under the curve of 0.75 (P=0.0003). It had a sensitivity of 88% and a specificity of 50%. AlphaMissense performed well compared with patch-clamp with an receiver operator curve-area under the curve of 0.85 (P<0.0001), sensitivity of 80%, and specificity of 76%. CONCLUSIONS: Deep protein language models aid in VUS resolution with high sensitivity but lower specificity. Thus, these tools cannot fully replace functional characterization but can aid in reducing the number of variants that may require functional analysis.

Catalysts of digital excellence: Unraveling the threads of connectivity, embedment, and visibility in enterprise social media for enhanced task and innovative performances.

In an era characterized by rapid digital transformation, this research delves into the intricate dynamics of enterprise social media (ESM) ubiquitous connectivity, drawing on the influential framework of social cognitive theory (SCT), we unravel the complex relationships among compatibility, presenteeism, ESM-enabled embeddedness, and the visibility of digital interactions, shedding light on how these factors collectively shape task performance and innovative contributions in the contemporary hybrid work landscape. By incorporating SCT into our research model, we not only enhance the theoretical underpinning but also respond to the imperative of understanding how individual cognition and observational learning impact behaviors in digital work environments. Data is collected from 467 participants from China. Results indicated that ESM-enabled embeddedness mediates the connection between presenteeism and both digital-enabled task performance, digital-enabled innovative performance. Findings conformed that ESM-enabled embeddedness mediates the connection between compatibility and both digital-enabled task performance, digital-enabled innovative performance. ESM visibility strengthens the connection between ubiquitous connectivity (presenteeism, compatibility) and ESM-enabled embeddedness. In addition, results also indicated that ESM visibility moderates the mediation effect of ESM-enabled embeddedness between the dimensions of ESM ubiquitous connectivity (presenteeism) and both digital-enabled task performance, digital-enabled innovative performance. In contrast, ESM visibility did not moderates the mediation effect of ESM-enabled embeddedness between the dimensions of ESM ubiquitous connectivity (compatibility) and both digital-enabled task performance, digital-enabled innovative performance. This study not only contributes depth to ESM literature but also lays the groundwork for future research endeavors, encouraging exploration of diverse contexts and additional moderating factors to comprehensively grasp the intricate dynamics within modern digital workspaces.

From anticipation to action: A RCT on mental imagery exercises in daily life as a motivational amplifier for individuals with depressive symptoms.

Encouraging engagement in rewarding or pleasant activities is one of the most important treatment goals for depression. Mental imagery exercises have been shown to increase the motivation for planned behaviour in the lab but it is unclear whether this is also the case in daily life. Therefore, we aimed to investigate the effect of mental imagery exercises on motivation and behaviour in daily life. Participants with depressive symptoms (N = 59) were randomly assigned to a group receiving mental imagery (MI) exercises or a control group receiving relaxation (RE) exercises via study phones. We employed an experience sampling design with 10 assessments per day for 10 days (three days baseline, four days with two exercises per day and three days post-intervention). Data was analysed using t-tests and multilevel linear regression analyses. As predicted, MI exercises enhanced motivation and reward anticipation during the intervention phase compared to RE. However, MI did not enhance active behaviour or strengthen the temporal association from reward anticipation (t-1) to active behaviour (t). Mental imagery exercises can act as a motivational amplifier but its effects on behaviour and real-life reward processes remain to be elucidated.

[Quality control method for phenolic acid components in abandoned stems and leaves of Artemisia selengensis based on UPLC fingerprint combined with quantitative analysis of multi-components by single marker (QAMS)].

This study established an ultra-performance liquid chromatography(UPLC) fingerprint of abandoned stems and leaves of Artemisia selengensis and quantitative analysis of multi-components by single marker(QAMS) for five phenolic acid components. Waters Acquity UPLC BEH C_(18) chromatography column(2.1 mm×100 mm, 1.7 µm) was used. The gradient elution was carried out with the mobile phase composed of 0.1% phosphoric acid water and acetonitrile at a flow rate of 0.3 mL·min~(-1) and a column temperature at 30 ℃. The detection wavelength was 330 nm, and the injection volume was 2 µL. Similarity evaluation and cluster analysis were conducted on the fingerprint data, and 15 common components in 13 batches of abandoned stems and leaves of A. selengensis were identified. The relative correction factors of ferulic acid, isochlorogenic acid A, isochlorogenic acid B, and isochlorogenic acid C were calculated using chlorogenic acid as the internal reference. The QAMS for determining five components in the abandoned stems and leaves of A. selengensis was established. At the same time, the content of these five components was determined using the external standard method(ESM), and the results showed that there were no significant differences in their content determined by the QAMS and the ESM. The results indicated that the content of phenolic acid components in the abandoned stems and leaves of A. selengensis from different varieties and different origins had obvious differences. In addition, the content of phenolic acid components in the abandoned stems and leaves of lignified A. selengensis was significantly higher than that of non-lignified A. selengensis. In summary, QAMS established in this study can be quickly, accurately, and economically used to determine the content of five phenolic acid components in abandoned stems and leaves of A. selengensis, laying a foundation for the resource development and utilization of abandoned stems and leaves of A. selengensis.

Eggshell membrane and green seaweed (Ulva lactuca) micronized powders for in vivo diabetic wound healing in albino rats: a comparative study.

BACKGROUND: Nonhealing diabetic wounds are a serious complication associated with extremely lethargic wound closure and a high risk of infection, leading to amputation or limb loss, as well as substantial health care costs and a poor quality of life for the patient. The effects of either eggshell membrane (ESM) and green seaweed (Ulva lactuca) extracts alone or in combination were evaluated for in vivo skin wound healing in a rat model of induced diabetes. METHODS: Micronized powders of waste hen ESM, Ulva lactuca, and their 1:1 mixture were prepared using regular procedures. The mechanical, electrical, and surface morphology characteristics of powders were examined using direct compression, LCR-impedancemetry, and scanning electron microscopy. The effect of ESM, Ulva lactuca, and their mixture as compared to standard Dermazin treatments were evaluated on wounds inflicted on male Wistar Albino rats with induced diabetes. Quantitative wound healing rates at baseline and at 3, 7, 14, and 21 days of treatments among all rat groups were conducted using ANOVA. Qualitative histological analysis of epidermal re-epithelization, keratinocytes, basement membrane, infiltrating lymphocytes, collagen fibrines, and blood vessels at day 21 were performed using Image J processing program. RESULTS: Compressive strength measurements of tablets showed a Young's modulus of 44.14 and 27.17 MPa for the ESM and ESM + Ulva lactuca mixture, respectively. Moreover, both samples exhibited relatively low relative permittivity values of 6.62 and 6.95 at 1 MHz, respectively, due to the porous surface morphology of ESM shown by scanning electron microscopy. On day 21, rats treated with ESM had a complete diabetic wound closure, hair regrowth, and a healing rate of 99.49%, compared to 96.79% for Dermazin, 87.05% for Ulva lactuca, 90.23% for the mixture, and only 36.44% for the negative controls. A well-formed basement membrane, well-differentiated epithelial cells, and regular thick keratinocytes lining the surface of the epidermal cells accompanied wound healing in rats treated with ESM, which was significantly better than in control rats. CONCLUSION: Ground hen ESM powder, a low-cost effective biomaterial, is better than Ulva lactuca or their mixture for preventing tissue damage and promoting diabetic wound healing, in addition to various biomedical applications.

A Systematic Review of Momentary Social Predictors of Positive Psychosis Experiences.

BACKGROUND: Social factors can play an important role in the development and maintenance of psychosis. Clarifying this relationship is vital for advancing theoretical understanding and development of targeted interventions. Psychosis is increasingly researched with an experience sampling methodology (ESM), which provides an ecologically valid approach, that reduces recall biases. Studies examining momentary associations between social factors and psychosis have not yet been summarised. METHOD: We identified 29 ESM studies investigating associations between social factors and positive psychotic experiences through a pre-registered systematic search of the published literature. RESULTS: Being alone did not predict increase in psychosis; however, appraisals and feelings associated with being alone such as feeling socially disconnected, lonely and unwanted did. Being with familiar company was found to reduce psychosis experiences but feeling stressed by the current company increased psychosis. CONCLUSIONS: While issues with sample size and generalisability mean these results should be interpreted with caution, some putative conclusions can be made. Individuals with psychosis or emerging symptoms should be offered interventions that improve social networks such as peer support, community participation and engagement skills training. These individuals may also benefit from virtual reality or compassion-based interventions which aim to dampen perceived social threat. Moreover, digital interventions which monitor changes in social variables that predict relapse in symptoms would allow early intervention to prevent mental health crises.

Everything AlphaFold tells us about protein knots.

Recent advances in Machine Learning methods in structural biology opened up new perspectives for protein analysis. Utilizing these methods allows us to go beyond the limitations of empirical research, and take advantage of the vast amount of generated data. We use a complete set of potentially knotted protein models identified in all high-quality predictions from the AlphaFold Database to search for any common trends that describe them. We show that the vast majority of knotted proteins have 31 knot and that the presence of knots is preferred in neither Bacteria, Eukaryota, or Archaea domains. On the contrary, the percentage of knotted proteins in any given proteome is around 0.4%, regardless of the taxonomical group. We also verified that the organism's living conditions do not impact the number of knotted proteins in its proteome, as previously expected. We did not encounter an organism without a single knotted protein. What is more, we found four universally present families of knotted proteins in Bacteria, consisting of SAM synthase, and TrmD, TrmH, and RsmE methyltransferases.

Investigating adverse daily life effects following a psychosocial laboratory stress task, and the moderating role of Psychopathology.

Laboratory stress tasks are necessary to closely investigate the stress response in a controlled environment. However, to our knowledge, no study has tested whether participating in such tasks can pose any daily life adverse effect. Fifty-three healthy participants (46 women) took part in a laboratory session where stress was induced using a typical psychosocial stressor: the repeated Montreal Imaging Stress Task (rMIST). Average levels of negative affect (NA), heart rate (HR), root mean square of successive differences (RMSSD), and skin conductance level (SCL), as well as reactivity across all these parameters as measured with the experience sampling method (ESM) in the four days prior to the laboratory session were compared with the four days following the session. We also assessed whether vulnerability to psychopathology moderated these associations. Findings showed that the task did not pose any significant adverse effect on participants. However, there was an unexpected increase in average RMSSD and a decrease in average SCL pre- to post- task. In addition, more vulnerable individuals were more likely to experience an increase in average levels of NA in the days following the task compared to the days preceding it. Our findings suggest that laboratory stress tasks may pose a significant risk to more vulnerable individuals.

The MoodWheel app: validation and factor structure of a new tool for the assessment of mood based on the experience sampling method.

BACKGROUND: The MoodWheel app is a newly developed tool that uses experience sampling method (ESM) for assessing negative and positive emotions, based on the circumplex model of emotions and the binary model of distress, and including behavioral and heart rate (HR) measurement via photoplethysmography and the possibility to personalize the application with additional measures. Aims: This study was designed to assess the factorial structure, reliability and validity of the MoodWheel (MW) application for evaluating emotions in children, adolescents. METHODS: A sample of 490 children and adolescents were recruited from the schools. Internal consistency was assessed via Cronbach's alpha test. Concurrent validity was assessed by evaluating the correlations between MW and Profile of Emotional Distress scale (PED) scores, in terms of functional/dysfunctional negative and positive emotions. RESULTS: Results obtained show that MW has good to excellent internal consistency and test-retest reliability, while the convergent validity was also adequate. Moreover, we found support for the organization of the MW based on the binary model of distress, given the predictive value found for the irrational and rational beliefs. CONCLUSIONS: The MW application is a useful and easy to use tool that can be used for the accurate measurement of emotions, which will be complimented in future with additional behavioral parameters to provide a comprehensive and dynamic assessment.

The relation between stress-induced dopamine release in the ventromedial prefrontal cortex, fronto-striatal functional connectivity, and negative urgency: A multimodal investigation using [18F]Fallypride PET, MRI and experience sampling.

Negative urgency (NU), or the tendency to act rashly when stress of negative affect is high, could be the result of an insufficient control of the ventromedial prefrontal cortex (vmPFC) over the striatum, through an impaired dopamine (DA) transmission. Therefore, we investigated in vivo human stress-induced DA release in the vmPFC, its relation with fronto-striatal functional connectivity (FC), and NU in daily life. In total, 12 female healthy participants performed a simultaneous [18 F]fallypride PET and fMRI scan during which stress was induced. Regions displaying stress-induced DA release were identified and used to investigate stress-induced changes in fronto-striatal FC. Additionally, participants enrolled in an experience sampling study, reporting on daily life stress and rash actions over a 12-month-long period. Mixed models explored whether stress-induced DA release and FC moderated NU in daily life. Stress led to a lower FC between the vmPFC and dorsal striatum, but a higher FC between the vmPFC and contralateral ventral striatum. Participants with a higher FC between the vmPFC and dorsal striatum displayed more NU in daily life. A higher stress-induced DA release in the vmPFC was related to a higher stress-induced change in FC between the vmPFC and striatum. Participants with a higher DA release in the vmPFC displayed more NU in daily life. In conclusion, stress could differentially impact fronto-striatal FC whereby the connectivity with the dorsal striatum is especially important for NU in daily life. This could be mediated by a higher, but not a lower, stress-induced DA release in the vmPFC.

High-resolution surface temperature changes for Portugal under CMIP6 future climate scenarios.

Future changes in the mean, maximum and minimum temperature in continental Portugal were investigated using high-resolution future climate projections based on the latest IPCC AR6 CMIP6 climate scenarios. The results show that the mean, maximum and minimum temperatures are projected to substantially increase in all continental Portugal, particularly at the south-central inland regions. For the near-term future (2046-2065 period), SSP3-7.0 is the future climate scenario that projects higher increases, of around 1 °C, 1.5 °C and 2 °C for the daily mean, maximum and minimum temperatures, respectively. For the long-term future (2081-2100 period), the projected warming is higher, particularly under the SSP5-8.5 future climate scenario with projected warmings of 3 °C, 3.5 °C and 2.5 °C for the daily mean, maximum and minimum temperatures, respectively. Occurrences of hot days (maximum temperature above 30 °C), very hot days (maximum temperature above 40 °C) and tropical nights (minimum temperature above 20 °C) are all projected to increase up to 35-40, 12-15 and 50 more days per year, respectively, mainly in the interior areas of Portugal. Oppositely, the occurrence of frost days is projected to decrease in practically all mountainous areas in Portugal. These results show a clear tendency of a significant increase in the surface temperatures and frequency of occurrence of extreme temperature episodes in continental Portugal, which can have severe impacts on the population, environment, economy and vital human activities such as agriculture.

SEMA3: A free smartphone platform for daily life surveys.

Traditionally, behavioral, social, and health science researchers have relied on global/retrospective survey methods administered cross-sectionally (i.e., on a single occasion) or longitudinally (i.e., on several occasions separated by weeks, months, or years). More recently, social and health scientists have added daily life survey methods (also known as intensive longitudinal methods or ambulatory assessment) to their toolkit. These methods (e.g., daily diaries, experience sampling, ecological momentary assessment) involve dense repeated assessments in everyday settings. To facilitate research using daily life survey methods, we present SEMA3 ( http://www.SEMA3.com ), a platform for designing and administering intensive longitudinal daily life surveys via Android and iOS smartphones. SEMA3 fills an important gap by providing researchers with a free, intuitive, and flexible platform with basic and advanced functionality. In this article, we describe SEMA3's development history and system architecture, provide an overview of how to design a study using SEMA3 and outline its key features, and discuss the platform's limitations and propose directions for future development of SEMA3.

GATSol, an enhanced predictor of protein solubility through the synergy of 3D structure graph and large language modeling.

BACKGROUND: Protein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the main factors to be considered in the design and production of antibody drugs and a prerequisite for realizing various protein functions. Although several solubility prediction models have emerged in recent years, many of these models are limited to capturing information embedded in one-dimensional amino acid sequences, resulting in unsatisfactory predictive performance. RESULTS: In this study, we introduce a novel Graph Attention network-based protein Solubility model, GATSol, which represents the 3D structure of proteins as a protein graph. In addition to the node features of amino acids extracted by the state-of-the-art protein large language model, GATSol utilizes amino acid distance maps generated using the latest AlphaFold technology. Rigorous testing on independent eSOL and the Saccharomyces cerevisiae test datasets has shown that GATSol outperforms most recently introduced models, especially with respect to the coefficient of determination R2, which reaches 0.517 and 0.424, respectively. It outperforms the current state-of-the-art GraphSol by 18.4% on the S. cerevisiae_test set. CONCLUSIONS: GATSol captures 3D dimensional features of proteins by building protein graphs, which significantly improves the accuracy of protein solubility prediction. Recent advances in protein structure modeling allow our method to incorporate spatial structure features extracted from predicted structures into the model by relying only on the input of protein sequences, which simplifies the entire graph neural network prediction process, making it more user-friendly and efficient. As a result, GATSol may help prioritize highly soluble proteins, ultimately reducing the cost and effort of experimental work. The source code and data of the GATSol model are freely available at https://github.com/binbinbinv/GATSol .

ESM1 May Be Used as a New Indicator for the Diagnosis and Prognosis of Early and Advanced Stage Digestive Tract Cancers.

Background: The biological function and prognostic significance of endothelial cell specific molecule 1 (ESM1) in various cancers have been validated. This study aimed to explore the expression and clinical diagnosis values in patients with stomach adenocarcinoma (STAD) and esophageal carcinoma (ESCA). Methods: Online database Gene Expression Omnibus was used to screen for abnormally expressed genes in STAD and ESCA. Besides, 36 STAD and 36 ESCA patients were enrolled, and their corresponding control groups were also 36 people each. Reverse transcription-quantitative polymerase chain reaction and Western blot were performed to analyze the expression of ESM1. Overall survival (OS) curve and receiver operating characteristics curve (ROC) analysis were used to assess the prognosis, and the sensitivity and specificity of ESM1 for the diagnosis of STAD and ESCA, respectively. Additionally, the effects of ESM1 on cell viability, migration, and invasion were analyzed by cell counting kit-8, transwell migration and invasion assays. Results: The results showed that the poor OS of STAD and ESCA patients was correlated with high ESM1. Besides, ESM1 was increased in ESCA and STAD in in vivo and in vitro studies. ESM1 has a high accuracy [area under the curve (AUC) > 0.79] at stage I and IV of STAD and ESCA. Knockdown of ESM1 suppressed the cell viability, migration, and invasion and increased the apoptosis rate of AGS and TE1 cells. Conclusion: Our study suggested that ESM1 might be used as a new indicator for the diagnosis and prognosis of early and advanced stage digestive tract cancers.

Lactate drives the ESM1-SCD1 axis to inhibit the antitumor CD8+ T-cell response by activating the Wnt/ß-catenin pathway in ovarian cancer cells and inducing cisplatin resistance.

Ovarian cancer (OC) is a gynecological malignancy that results in a global threat to women's lives. Lactic acid, a key metabolite produced from the glycolytic metabolism of glucose molecules, is correlated with tumor immune infiltration and platinum resistance. In our previous study, we found that endothelial cell-specific molecule 1 (ESM1) plays a key role in OC progression. This study revealed that lactate could upregulate ESM1, which enhances SCD1 to attenuate the antitumor CD8+ T-cell response. ESM1 and SCD1 expression levels were significantly greater in OC patients with high lactic acid levels than in those with low lactic acid levels. Further mechanistic studies suggested that the Wnt/ß-catenin pathway was inactivated after ESM1 knockdown and rescued by SCD1 overexpression. IC50 analysis indicated that the ESM1-SCD1 axis induces the resistance of OC cells to platinum agents, including cisplatin, carboplatin, and oxaliplatin, by upregulating P-gp. In conclusion, our study indicated that the induction of SCD1 by lactic acid-induced ESM1 can impede the CD8+ T-cell response against tumors and promote resistance to cisplatin by activating the Wnt/ß-catenin pathway in ovarian cancer. Consequently, targeting ESM1 may have considerable therapeutic potential for modulating the tumor immune microenvironment and enhancing drug sensitivity in OC patients.