Nonlinear-mirror mode-locked crystal waveguide laser by intracavity fourth-harmonic loss modulation: publisher's note.

This publisher's note contains a correction to Opt. Lett.48, 6064 (2024)10.1364/OL.509275.

Stevioside Ameliorates Palmitic Acid-Induced Abnormal Glucose Uptake via the PDK4/AMPK/TBC1D1 Pathway in C2C12 Myotubes.

BACKGROUND: Stevioside (SV) with minimal calories is widely used as a natural sweetener in beverages due to its high sweetness and safety. However, the effects of SV on glucose uptake and the pyruvate dehydrogenase kinase isoenzyme (PDK4) as an important protein in the regulation of glucose metabolism, remain largely unexplored. In this study, we used C2C12 skeletal muscle cells that was induced by palmitic acid (PA) to assess the effects and mechanisms of SV on glucose uptake and PDK4. METHODS: The glucose uptake of C2C12 cells was determined by 2-NBDG; expression of the Pdk4 gene was measured by quantitative real-time PCR; and expression of the proteins PDK4, p-AMPK, TBC1D1 and GLUT4 was assessed by Western blotting. RESULTS: In PA-induced C2C12 myotubes, SV could significantly promote cellular glucose uptake by decreasing PDK4 levels and increasing p-AMPK and TBC1D1 levels. SV could promote the translocation of GLUT4 from the cytoplasm to the cell membrane in cells. Moreover, in Pdk4-overexpressing C2C12 myotubes, SV decreased the level of PDK4 and increased the levels of p-AMPK and TBC1D1. CONCLUSION: SV was found to ameliorate PA-induced abnormal glucose uptake via the PDK4/AMPK/TBC1D1 pathway in C2C12 myotubes. Although these results warranted further investigation for validation, they may provide some evidence of SV as a safe natural sweetener for its use in sugar-free beverages to prevent and control T2DM.

A novel deep generative model for mRNA vaccine development: Designing 5' UTRs with N1-methyl-pseudouridine modification.

Efficient translation mediated by the 5' untranslated region (5' UTR) is essential for the robust efficacy of mRNA vaccines. However, the N1-methyl-pseudouridine (m1Ψ) modification of mRNA can impact the translation efficiency of the 5' UTR. We discovered that the optimal 5' UTR for m1Ψ-modified mRNA (m1Ψ-5' UTR) differs significantly from its unmodified counterpart, highlighting the need for a specialized tool for designing m1Ψ-5' UTRs rather than directly utilizing high-expression endogenous gene 5' UTRs. In response, we developed a novel machine learning-based tool, Smart5UTR, which employs a deep generative model to identify superior m1Ψ-5' UTRs in silico. The tailored loss function and network architecture enable Smart5UTR to overcome limitations inherent in existing models. As a result, Smart5UTR can successfully design superior 5' UTRs, greatly benefiting mRNA vaccine development. Notably, Smart5UTR-designed superior 5' UTRs significantly enhanced antibody titers induced by COVID-19 mRNA vaccines against the Delta and Omicron variants of SARS-CoV-2, surpassing the performance of vaccines using high-expression endogenous gene 5' UTRs.

The molecular subtypes of autoimmune diseases.

Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS "inflammation & EGF" subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.

Recent progresses in gut microbiome mediates obstructive sleep apnea-induced cardiovascular diseases.

Obstructive sleep apnea (OSA) is a multifactorial sleep disorder with a high prevalence in the general population. OSA is associated with an increased risk of developing cardiovascular diseases (CVDs), particularly hypertension, and is linked to worse outcomes. Although the correlation between OSA and CVDs is firmly established, the mechanisms are poorly understood. Continuous positive airway pressure is primary treatment for OSA reducing cardiovascular risk effectively, while is limited by inadequate compliance. Moreover, alternative treatments for cardiovascular complications in OSA are currently not available. Recently, there has been considerable attention on the significant correlation between gut microbiome and pathophysiological changes in OSA. Furthermore, gut microbiome has a significant impact on the cardiovascular complications that arise from OSA. Nevertheless, a detailed understanding of this association is lacking. This review examines recent advancements to clarify the link between the gut microbiome, OSA, and OSA-related CVDs, with a specific focus on hypertension, and also explores potential health advantages of adjuvant therapy that targets the gut microbiome in OSA.

Recent advances in the synthesis of single-stranded DNA in vitro.

Single-stranded DNA (ssDNA) is the foundation of modern biology, with wide applications in gene editing, sequencing, DNA information storage, and materials science. However, synthesizing ssDNA with high efficiency, high throughput, and low error rate in vitro remains a major challenge. Various methods have been developed for ssDNA synthesis, and some significant results have been achieved. In this review, six main methods were introduced, including solid-phase oligonucleotide synthesis, terminal deoxynucleotidyl transferase-based ssDNA synthesis, reverse transcription, primer exchange reaction, asymmetric polymerase chain reaction, and rolling circle amplification. The advantages and limitations of each method were compared, as well as illustrate their representative achievements and applications. Especially, rolling circle amplification has received significant attention, including ssDNA synthesis, assembly, and application based on recent work. Finally, the future challenges and opportunities of ssDNA synthesis were summarized and discussed. Envisioning the development of new methods and significant progress will be made in the near future with the efforts of scientists around the world.

Clinicopathological factors predict residual lymph node metastasis in locally advanced rectal cancer with ypT0-2 after neoadjuvant chemoradiotherapy.

PURPOSE: Residual lymph node metastases (RLNM) remained a great concern in the implementation of organ-preserving strategies and led to poor prognosis in locally advanced rectal cancer (LARC). In this study, we aimed to identify the clinicopathological factors correlated with RLNM in LARC patients with ypT0-2 after neoadjuvant chemoradiotherapy (NCRT). METHODS: We retrospectively analyzed 417 patients histologically diagnosed middle-low LARC after NCRT and total mesorectal excision (TME), whose pathological staging was ypT0-2. All patients received pelvic magnetic resonance imaging (MRI) before NCRT. The radiation doses were 50-50.6 Gy for the planning gross tumor volume and 41.8-45 Gy for the planning target volume, respectively. A nomogram for predicting RLNM was constructed using a binary logistic regression. Nomogram performance was assessed by receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC). RESULTS: After surgery, 191 patients (45.8%) were ypT0, 43 patients (10.3%) were ypT1 and 183 patients (43.9%) were ypT2, and a total of 49 patients (11.8%) were found the presence of RLNM. Multivariable analyses identified MRI-defined mesorectal fascia (MRF)-positive, high-grade histopathology at biopsy, advanced ypT-category, and the presence of perineural invasion (PNI) as the predictive factors. The nomogram, incorporating all these predictors, showed good discrimination and calibration efficacy, with the areas under the ROC curve of 0.690 (95% CI: 0.610-0.771). Both DCA and CIC demonstrated that this nomogram has good clinical usefulness. CONCLUSION: The nomogram model can predict RLNM in patients with ypT0-2 tumors. It can help select suitable patients for performing organ-preserving strategies after NCRT.

Effect of acidic polymers on the morphology of non-photochemical laser-induced nucleation of potassium bromide.

Non-photochemical laser-induced nucleation (NPLIN) in supersaturated potassium bromide (KBr) solutions with the addition of acidic polymers is reported here for the first time. Upon absorbing the incident laser, crystallites are immediately induced along the laser pathway in the solution, eventually growing into needle-shaped crystals of varying sizes. When comparing induction time, nucleation probability, and crystal habits with spontaneous nucleation, the results suggest that NPLIN creates a distinct morphological pathway, transforming cubic crystals into needle-like structures. Additionally, it improves crystallization probability and growth rate. This paper aims to realize control from crystal nucleation to crystal growth by adding acidic polymers to the process of laser-induced nucleation, potentially influencing crystal morphology modification in NPLIN. With 19 wt% acidic polymers added to the solution as additives, control over both crystal growth and morphological modifications was observed: cubic KBr crystals with square patterns were produced through laser irradiation, and there was a varying reduction in both the number and growth rate of the crystals. The influence of acidic polymers on the solution environment was analyzed to determine the reasons for the variations in crystal quantity and growth speed. The underlying mechanisms responsible for the changes in crystal shape were also discussed.

RyR2 Stabilizer Attenuates Cardiac Hypertrophy by Downregulating TNF-α/NF-κB/NLRP3 Signaling Pathway through Inhibiting Calcineurin.

The effect of Ryanodine receptor2 (RyR2) and its stabilizer on cardiac hypertrophy is not well known. C57/BL6 mice underwent transverse aortic contraction (TAC) or sham surgery were administered dantrolene, the RyR2 stabilizer, or control drug. Dantrolene significantly alleviated TAC-induced cardiac hypertrophy in mice, and RNA sequencing was performed implying calcineurin/NFAT3 and TNF-α/NF-κB/NLRP3 as critical signaling pathways. Further expression analysis and Western blot with heart tissue as well as neonatal rat cardiomyocyte (NRCM) model confirmed dantrolene decreases the activation of calcineurin/NFAT3 signaling pathway and TNF-α/NF-κB/NLRP3 signaling pathway, which was similar to FK506 and might be attenuated by calcineurin overexpression. The present study shows for the first time that RyR2 stabilizer dantrolene attenuates cardiac hypertrophy by inhibiting the calcineurin, therefore downregulating the TNF-α/NF-κB/NLRP3 pathway.

Coding genomes with gapped pattern graph convolutional network.

MOTIVATION: Genome sequencing technologies reveal a huge amount of genomic sequences. Neural network-based methods can be prime candidates for retrieving insights from these sequences because of their applicability to large and diverse datasets. However, the highly variable lengths of genome sequences severely impair the presentation of sequences as input to the neural network. Genetic variations further complicate tasks that involve sequence comparison or alignment. RESULTS: Inspired by the theory and applications of "spaced seeds," we propose a graph representation of genome sequences called "gapped pattern graph." These graphs can be transformed through a Graph Convolutional Network to form lower-dimensional embeddings for downstream tasks. On the basis of the gapped pattern graphs, we implemented a neural network model and demonstrated its performance on diverse tasks involving microbe and mammalian genome data. Our method consistently outperformed all the other state-of-the-art methods across various metrics on all tasks, especially for the sequences with limited homology to the training data. In addition, our model was able to identify distinct gapped pattern signatures from the sequences. AVAILABILITY AND IMPLEMENTATION: The framework is available at https://github.com/deepomicslab/GCNFrame.

Corals-inspired magnetic absorbents for fast and efficient removal of microplastics in various water sources.

Microplastics (MPs) as the formidable pollutants with high toxicity and difficult degradation may threaten the aquaculture industry and human health, making it highly necessary to develop the effective removal methods. In this article, Fe3O4 nanoparticles (NPs) were initially fabricated with mesoporous structure, but showing undesirable adsorption efficiencies for the adsorption of MPs (lower than 70%). Inspired by the reefs-rebuilding corals acting as the sinks for various marine pollutants like plastic, Fe3O4 NPs were coated further with adhesive polymerized dopamine (PDA) yielding Fe3O4@PDA absorbents. Unexpectedly, it was discovered that the corals-mimicking absorbents so formed could allow for the removal of MPs with dramatically enhanced efficiencies up to 98.5%, which is over about 30% higher than those of bare Fe3O4 NPs. Herein, the PDA shells might conduct the increased adhesion to MPs, presumably through the formation of hydrogen bonding, π-π stacking, and hydrophobic interactions. A fast (within 20 min) and stable adsorption of MPs can also be expected, in addition to the PDA-improved environmental storage of Fe3O4 NPs. Subsequently, the Fe3O4@PDA adsorbents were utilized to remove MPs from different water sources with high efficiencies, including pure water, suburban streams, village rivers, lake water, inner-city moats, and aquaculture water. Such a magnet-recyclable adsorbent may provide a new way for rapid, effective, and low-cost removal of MPs pollutants from various water systems.

Study on mNGS Technique in Diagnosing Pneumocystis jirovecii Pneumonia in Non-HIV-Infected Patients.

Objective: To investigate the value of metagenomic Next-Generation Sequencing (mNGS) in diagnosing Pneumocystis jirovecii pneumonia (PJP) in non-human immunodeficiency virus (HIV)-infected patients. Methods: In this retrospective study, non-HIV-infected patients with PJP and those diagnosed with non-PJP from August 2022 to December 2024 were selected as subjects. The presence of Pneumocystis jirovecii (PJ) and other co-pathogens in bronchoalveolar lavage fluid (BALF) was analyzed, and the diagnostic efficacy of NGS, polymerase chain reaction (PCR) and serum 1,3-ß-D-glucan (BDG) in PJP was compared with the reference standard of clinical compound diagnosis. Results: Eighty-nine non-HIV-infected patients were recruited, with dyspnea as the primary symptom (69.66%) and solid malignant tumor as the most common underlying disease (20.22%). Taking clinical compound diagnosis as the reference standard, the sensitivity, specificity, negative predictive value and positive predictive value of mNGS were higher than those detected by PCR and serum BDG. Among 42 non-HIV-infected patients with PJP who underwent mNGS and conventional pathogen detection of BALF, 6 had simple PJ infection and 36 had combined PJ infection. The detection rate of mNGS in mixed infections was significantly higher than that of conventional pathogen detection (85.71 vs 61.70%, P = 0.012). A total of 127 pathogens were detected in BALF using mNGS, among which fungi had the highest detection rate (46.46%). The fungi, viruses and bacteria detected were mainly Pneumocystis jirovecii, human gammaherpesvirus 4 and Acinetobacter baumannii. Conclusion: mNGS is highly effective in diagnosing non-HIV-infected patients with PJP and exhibits ideal performance in the detection of co-pathogens. In addition, it has certain value for clinical diagnosis and guidance of targeted anti-infective drug treatment.

African swine fever virus infection regulates pyroptosis by cleaving gasdermin A (GSDMA) via active caspase-3 and caspase-4.

African swine fever (ASF), caused by the African swine fever virus (ASFV), is a viral hemorrhagic disease that affects domestic pigs and wild boars. ASFV infection causes extensive tissue damage, and the associated mechanism is poorly understood. Pyroptosis is characterized by the activation of inflammatory caspases and pore formation in the cellular plasma membrane, resulting in the release of inflammatory cytokines and cell damage. How ASFV infection regulates pyroptosis remains unclear. Here, using small interfering RNA assay and overexpression methods, we report that ASFV infection regulated pyroptosis by cleaving the pyroptosis execution protein gasdermin A (GSDMA). ASFV infection activated caspase-3 and caspase-4, which specifically cleaved GSDMA at D75-P76 and D241-V242 to produce GSDMA into five fragments, including GSDMA-N1-75, GSDMA-N1-241, and GSDMA-N76-241 fragments at the N terminal end of GSDMA. Only GSDMA-N1-241, which was produced in the late stage of ASFV infection, triggered pyroptosis and inhibited ASFV replication. The fragments GSDMA-N1-75 and GSDMA-N76-241 lose the ability to induce pyroptosis. Overall ASFV infection differentially regulates pyroptosis by GSDMA in the indicated phase, which may be conducive to its own replication. Our findings reveal a novel molecular mechanism for the regulation of pyroptosis.

Insights into the Distinct Behaviors between Bifunctional and Binary Organoborane Catalysts through Terpolymerization of Epoxide, CO2, and Anhydride.

Alkyl borane compounds-mediated polymerizations have expanded to Lewis pair polymerization, free radical polymerization, ionic ring-opening polymerization, and polyhomologation. The bifunctional organoborane catalysts that contain the Lewis acid and ammonium or phosphonium salt in one molecule have demonstrated superior catalytic performance for ring-opening polymerization of epoxides and ring-opening copolymerization of epoxides and CO2 than their two-component analogues, i.e., the blend of organoborane and ammonium or phosphonium salt. To explore the origin of the differences of the one-component and two-component organoborane catalysts, here we conducted a systematic investigation on the catalytic performances of these two kinds of organoborane catalysts via terpolymerization of epoxide, carbon dioxide and anhydride. The resultant terpolymers produced independently by bifunctional and binary organoborane catalyst exhibited distinct microstructures, where a series of gradient polyester-polycarbonate terpolymers with varying polyester content were afforded using the bifunctional catalyst, while tapering diblock terpolymers were obtained using the binary system. The bifunctional catalyst enhances the competitiveness of CO2 insertion than anhydride, which leads to the premature incorporation of CO2 into the polymer chains and ultimately results in the formation of gradient terpolymers. DFT calculations revealed the role of electrostatic interaction and charge distribution caused by intramolecular synergistic effect for bifunctional organoborane catalyst.

New RNN Algorithms for Different Time-Variant Matrix Inequalities Solving Under Discrete-Time Framework.

A series of discrete time-variant matrix inequalities is generally regarded as one of the challenging problems in science and engineering fields. As a discrete time-variant problem, the existing solving schemes generally need the theoretical support under the continuous-time framework, and there is no independent solving scheme under the discrete-time framework. The theoretical deficiency of solving scheme greatly limits the theoretical research and practical application of discrete time-variant matrix inequalities. In this article, new discrete-time recurrent neural network (RNN) algorithms are proposed, analyzed, and investigated for solving different time-variant matrix inequalities under the discrete-time framework, including discrete time-variant matrix vector inequality (discrete time-variant MVI), discrete time-variant generalized matrix inequality (discrete time-variant GMI), discrete time-variant generalized-Sylvester matrix inequality (discrete time-variant GSMI), and discrete time-variant complicated-Sylvester matrix inequality (discrete time-variant CSMI), and all solving processes are based on the direct discretization thought. Specifically, first of all, four discrete time-variant matrix inequalities are presented as the target problems of these researches. Second, for solving such problems, we propose corresponding discrete-time recurrent neural network (RNN) (DT-RNN) algorithms (termed DT-RNN-MVI algorithm, DT-RNN-GMI algorithm, DT-RNN-GSMI algorithm, and DT-RNN-CSMI algorithm), which are different from the traditional DT-RNN design thought because second-order Taylor expansion is applied to derive the DT-RNN algorithms. This creative process avoids the intervention of continuous-time framework. Then, theoretical analyses are presented, which show the convergence and precision of the DT-RNN algorithms. Abundant numerical experiments are further carried out, which further confirm the excellent properties of the DT-RNN algorithms.

Variability in the prevalence of depression among adults with chronic pain: UK Biobank analysis through clinical prediction models.

BACKGROUND: The prevalence of depression among people with chronic pain remains unclear due to the heterogeneity of study samples and definitions of depression. We aimed to identify sources of variation in the prevalence of depression among people with chronic pain and generate clinical prediction models to estimate the probability of depression among individuals with chronic pain. METHODS: Participants were from the UK Biobank. The primary outcome was a "lifetime" history of depression. The model's performance was evaluated using discrimination (optimism-corrected C statistic) and calibration (calibration plot). RESULTS: Analyses included 24,405 patients with chronic pain (mean age 64.1 years). Among participants with chronic widespread pain, the prevalence of having a "lifetime" history of depression was 45.7% and varied (25.0-66.7%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.66; good calibration on the calibration plot) included age, BMI, smoking status, physical activity, socioeconomic status, gender, history of asthma, history of heart failure, and history of peripheral artery disease. Among participants with chronic regional pain, the prevalence of having a "lifetime" history of depression was 30.2% and varied (21.4-70.6%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.65; good calibration on the calibration plot) included age, gender, nature of pain, smoking status, regular opioid use, history of asthma, pain location that bothers you most, and BMI. CONCLUSIONS: There was substantial variability in the prevalence of depression among patients with chronic pain. Clinically relevant factors were selected to develop prediction models. Clinicians can use these models to assess patients' treatment needs. These predictors are convenient to collect during daily practice, making it easy for busy clinicians to use them.

Metabolic profiles outperform the microbiota in assessing the response of vaginal microenvironments to the changed state of HPV infection.

There is a deficiency in population-based studies investigating the impact of HPV infection on vaginal microenvironment, which influences the risk of persistent HPV infection. This prospective study aimed to unravel the dynamics of vaginal microbiota (VM) and vaginal metabolome in reaction to the changed state of HPV infection. Our results propose that the vaginal metabolome may be a superior indicator to VM when assessing the impact of altered HPV state on the vaginal microenvironment.

Impact of the addition of dexmedetomidine to patient-controlled intravenous analgesia on postoperative pain-sleep interaction cycle and delirium: A systematic review and meta-analysis of randomized controlled trials.

Background: The reciprocal nexus between sleep and pain is well-documented, with the deleterious impact of operative trauma potentially playing a pivotal role in the dysregulation of this interplay, which could significantly contribute to the manifestation of postoperative delirium (POD). Studies have investigated the effect of adding dexmedetomidine (DEX) to patient-controlled intravenous analgesia (PCIA) pumps on postoperative pain-sleep interaction cycle and POD, but conclusions remained uncertain. The objective of this investigation is to perform a meta-analysis that thoroughly assesses the impact of integrating DEX into PCIA, focusing on analgesic effectiveness, sleep quality, and the incidence of delirium in postoperative patients. Methods: PubMed, Embase, Cochrane Library, SinoMed, and Wanfang Data Knowledge Service Platform were searched, for publications in any language, from database inception to September 2023. Our analysis encompassed randomized controlled trials (RCTs) that examine the therapeutic efficacy and risk profile of adding DEX to the PCIA on the postoperative pain-sleep interaction cycle, by focusing on changes in postoperative analgesia (Visual analog scale (VAS) score), sleep efficiency, sleep structure, subjective sleep score (Assen insomnia scale and numerical rating scale) and adverse event rate. Results: 34 RCTs (4324 patients) were analyzed. This study shows DEX improved analgesia and reduced VAS scores at 6, 12, and 24 h after surgery. Sleep efficiency was enhanced on the 1st and 2nd postoperative night. DEX improved sleep structure at the 1st postoperative night by reducing non-rapid eye movement stage 1 (N1) sleep and increasing non-rapid eye movement stage 2 (N2) and non-rapid eye movement stage 3 (N3) sleep. At the 2nd night, DEX reduced N1 sleep and increased N2 sleep, but not N3 sleep. Data from AIS and NRS showed improvement in subjective sleep scores on the 1st postoperative night and 2nd night. Additionally, DEX decreased the occurrence of POD on the 24 h and first-three days. Conclusion: This study shows that the typical DEX doses added to PCIA with sufentanil were 2-5 µg/kg or approximately 200-250 µg, and the addition of DEX to PCIA can improve pain-sleep interaction cycle from multiple perspectives, and further decrease the occurrence of POD.

Construction of a gene model related to the prognosis of patients with gastric cancer receiving immunotherapy and exploration of COX7A1 gene function.

BACKGROUND: GC is a highly heterogeneous tumor with different responses to immunotherapy, and the positive response depends on the unique interaction between the tumor and the tumor microenvironment (TME). However, the currently available methods for prognostic prediction are not satisfactory. Therefore, this study aims to construct a novel model that integrates relevant gene sets to predict the clinical efficacy of immunotherapy and the prognosis of GC patients based on machine learning. METHODS: Seven GC datasets were collected from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database and literature sources. Based on the immunotherapy cohort, we first obtained a list of immunotherapy related genes through differential expression analysis. Then, Cox regression analysis was applied to divide these genes with prognostic significancy into protective and risky types. Then, the Single Sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to score the two categories of gene sets separately, and the scores differences between the two gene sets were used as the basis for constructing the prognostic model. Subsequently, Weighted Correlation Network Analysis (WGCNA) and Cytoscape were applied to further screen the gene sets of the constructed model, and finally COX7A1 was selected for the exploration and prediction of the relationship between the clinical efficacy of immunotherapy for GC. The correlation between COX7A1 and immune cell infiltration, drug sensitivity scoring, and immunohistochemical staining were performed to initially understand the potential role of COX7A1 in the development and progression of GC. Finally, the differential expression of COX7A1 was verified in those GC patients receiving immunotherapy. RESULTS: First, 47 protective genes and 408 risky genes were obtained, and the ssGSEA algorithm was applied for model construction, showing good prognostic discrimination ability. In addition, the patients with high model scores showed higher TMB and MSI levels, and lower tumor heterogeneity scores. Then, it is found that the COX7A1 expressions in GC tissues were significantly lower than those in their corresponding paracancerous tissues. Meanwhile, the patients with high COX7A1 expression showed higher probability of cancer invasion, worse clinical efficacy of immunotherapy, worse overall survival (OS) and worse disease-free survival (DFS). CONCLUSIONS: The ssGSEA score we constructed can serve as a biomarker for GC patients and provide important guidance for individualized treatment. In addition, the COX7A1 gene can accurately distinguish the prognosis of GC patients and predict the clinical efficacy of immunotherapy for GC patients.