Alterations of mannosylated glycopatterns recognized by Hippeastrum hybrid lectin in saliva of patients with lung cancer.

OBJECTIVES: Lung cancer (LC) is the malignant tumor with the highest mortality rate worldwide, and precise early diagnosis can improve patient prognosis. The purpose of this study was to investigate whether alterations in the glycopatterns recognized by the Hippeastrum hybrid lectin (HHL) in salivary proteins are associated with the development of LC. MATERIALS AND METHODS: First, we collected saliva samples from LC (15 lung adenocarcinoma (ADC); 15 squamous cell carcinoma (SCC); 15 small cell lung cancer (SCLC)) and 15 benign pulmonary disease (BPD) for high-throughput detection of abundance levels of HHL-recognized glycopatterns using protein microarrays, and then validated the pooled samples from each group with lectin blotting analysis. Finally, the N-glycan profiles of salivary glycoproteins isolated from the pooled samples using HHL-magnetic particle conjugates were characterized separately using MALDI-TOF/TOF-MS. RESULTS: The results showed that the abundance level of glycopatterns recognized by HHL in salivary proteins was elevated in LC compared to BPD. The proportion of mannosylated N-glycans was notably higher in ADC (31.7%), SCC (39.0%), and SCLC (46.6%) compared to BPD (23.3%). CONCLUSIONS: The altered salivary glycopatterns such as oligomannose, Manα1-3Man, or Manα1-6Man N-glycans recognized by HHL might serve as potential biomarkers for the diagnosis of LC patients. CLINICAL RELEVANCE: This study provides crucial information for studying changes in salivary to differentiate between BPD and LC and facilitate the discovery of biomarkers for LC diagnosis based on precise alterations of mannosylated N-glycans in saliva.

Machine Learning Reveals Serum Glycopatterns as Potential Biomarkers for the Diagnosis of Nonalcoholic Fatty Liver Disease (NAFLD).

Nonalcoholic fatty liver disease (NAFLD) has emerged as the predominant chronic liver condition globally, and underdiagnosis is common, particularly in mild cases, attributed to the asymptomatic nature and traditional ultrasonography's limited sensitivity to detect early-stage steatosis. Consequently, patients may experience progressive liver pathology. The objective of this research is to ascertain the efficacy of serum glycan glycopatterns as a potential diagnostic biomarker, with a particular focus on the disease's early stages. We collected a total of 170 serum samples from volunteers with mild-NAFLD (Mild), severe-NAFLD (Severe), and non-NAFLD (None). Examination via lectin microarrays has uncovered pronounced disparities in serum glycopatterns identified by 19 distinct lectins. Following this, we employed four distinct machine learning algorithms to categorize the None, Mild, and Severe groups, drawing on the alterations observed in serum glycopatterns. The gradient boosting decision tree (GBDT) algorithm outperformed other models in diagnostic accuracy within the validation set, achieving an accuracy rate of 95% in differentiating the None group from the Mild group. Our research indicates that employing lectin microarrays to identify alterations in serum glycopatterns, when integrated with advanced machine learning algorithms, could constitute a promising approach for the diagnosis of NAFLD, with a special emphasis on its early detection.

N-Glycan Profiles of Neuraminidase from Avian Influenza Viruses.

The cleavage of sialic acids by neuraminidase (NA) facilitates the spread of influenza A virus (IV) descendants. Understanding the enzymatic activity of NA aids research into the transmission of IVs. An effective method for purifying NA was developed using p-aminophenyloxamic acid-modified functionalized hydroxylated magnetic particles (AAMPs), and from 0.299 to 0.401 mg of NA from eight IV strains was isolated by 1 mg AAMP. A combination of lectin microarrays and MALDI-TOF/TOF-MS was employed to investigate the N-glycans of isolated NAs. We found that more than 20 N-glycans were identified, and 16 glycan peaks were identical in the strains derived from chicken embryo cultivation. Multi-antennae, bisected, or core-fucosylated N-glycans are common in all the NAs. The terminal residues of N-glycans are predominantly composed of galactose and N-acetylglucosamine residues. Meanwhile, sialic acid residue was uncommon in these N-glycans. Further computational docking analysis predicted the interaction mechanism between NA and p-aminophenyloxamic acid.

Evaluation of salivary glycopatterns based diagnostic models for prediction of diabetic vascular complications.

Diabetic vascular complications (DVC) are the main cause of death in diabetic patients. However, there is a lack of effective biomarkers or convenient methods for early diagnosis of DVC. In this study, the salivary glycopatterns from 130 of healthy volunteers (HV), 139 patients with type 2 diabetes mellitus (T2DM) and 167 patients with DVC were case-by-case analyzed by using lectin microarrays. Subsequently, diagnostic models were developed using logistic regression and machine learning algorithms based on the data of lectin microarrays in training set. The performance of diagnostic models was evaluated in an independent blind cohort. The results of lectin microarrays indicated that the glycopatterns identified by 16 lectins (e.g. BS-I, PWM and EEL) were significantly altered in DVC patients compared with patients with T2DM, which suggested the alterations in salivary glycopatterns could reflect onset of DVC. Notably, K-Nearest Neighbor (KNN) model exhibited better performance for distinguishing DVC (accuracy: 0.939) than other models in blind cohort. The integrated classifier, which combined three machine learning models, exhibited a higher overall accuracy (≥ 0.933) than other models in blind cohort. Our study provided a cost-effective and non-invasive method for auxiliary diagnosis DVC based on the combination of salivary glycopatterns and machine learning algorithms.

Serum uric acid levels and health outcomes in CKD: a prospective cohort study.

BACKGROUND: Hyperuricemia is prevalent in individuals with chronic kidney disease (CKD). Elevated serum uric acid (SUA) concentrations have been considered an independent risk factor for the onset of CKD. However, the relationship between SUA concentrations and long-term health outcomes among patients with CKD remains unclear. METHODS: We performed a prospective cohort study with nationally representative sample to investigate the relationship between SUA concentrations and mortality risk including all-cause, cardiovascular disease (CVD) and cancer mortality, among patients with CKD. The weighted restricted cubic spline analyses combined with the multivariate-adjusted Cox proportional hazard models were used to test the nonlinearity of relationship. RESULTS: The 6642 patients participating in National Health and Nutrition Examination Survey 1999-2018 were enrolled. During 656 885 person-months of follow-up time, 2619 all-cause deaths were recorded, including 1030 CVD deaths and 458 cancer deaths. Our study presented J-shaped non-linear relationships between SUA concentrations and all-cause and CVD mortality with inflection points at 311.65 µmol/L and 392.34 µmol/L, respectively. When SUA concentration was higher than those inflection points, every increase of 50 µmol/L SUA was associated with 11.7% and 17.0% greater multivariable-adjusted hazard ratio of all-cause and CVD mortality, respectively. In addition, a negative linear correlation with cancer mortality was detected. CONCLUSION: These findings suggested that maintaining appropriate SUA concentrations may improve long-term health outcomes among CKD patients. The corresponding inflection points of J-shaped non-linear relationships were 311.65 and 392.34 µmol/L for all-cause and CVD mortality. Further clinical trials are required to investigate uric acid-lowering targets.

Single-cell transcriptomics reveals variations in monocytes and Tregs between gout flare and remission.

Gout commonly manifests as a painful, self-limiting inflammatory arthritis. Nevertheless, the understanding of the inflammatory and immune responses underlying gout flares and remission remains ambiguous. Here, based on single-cell RNA-Seq and an independent validation cohort, we identified the potential mechanism of gout flare, which likely involves the upregulation of HLA-DQA1+ nonclassical monocytes and is related to antigen processing and presentation. Furthermore, Tregs also play an essential role in the suppressive capacity during gout remission. Cell communication analysis suggested the existence of altered crosstalk between monocytes and other T cell types, such as Tregs. Moreover, we observed the systemic upregulation of inflammatory and cytokine genes, primarily in classical monocytes, during gout flares. All monocyte subtypes showed increased arachidonic acid metabolic activity along with upregulation of prostaglandin-endoperoxide synthase 2 (PTGS2). We also detected a decrease in blood arachidonic acid and an increase in leukotriene B4 levels during gout flares. In summary, our study illustrates the distinctive immune cell responses and systemic inflammation patterns that characterize the transition from gout flares to remission, and it suggests that blood monocyte subtypes and Tregs are potential intervention targets for preventing recurrent gout attacks and progression.

The glycopatterns of Pseudomonas aeruginosa as a potential biomarker for its carbapenem resistance.

IMPORTANCE: Bacterial surface glycans are an attractive therapeutic target in response to antibiotics; however, current knowledge of the corresponding mechanisms is rather limited. Antimicrobial susceptibility testing, genome sequencing, and MALDI-TOF MS, commonly used in recent years to analyze bacterial resistance, are unable to rapidly and efficiently establish associations between glycans and resistance. The discovery of new antimicrobial strategies still requires the introduction of promising analytical methods. In this study, we applied lectin microarray technology and a machine-learning model to screen for important glycan structures associated with carbapenem-resistant P. aeruginosa. This work highlights that specific glycopatterns can be important biomarkers associated with bacterial antibiotic resistance, which promises to provide a rapid entry point for exploring new resistance mechanisms in pathogens.

MSU crystal deposition contributes to inflammation and immune responses in gout remission.

As a prominent feature of gout, monosodium urate (MSU) crystal deposition induces gout flares, but its impact on immune inflammation in gout remission remains unclear. Using single-cell RNA sequencing (scRNA-seq), we characterize the transcription profiling of peripheral blood mononuclear cells (PBMCs) among intercritical remission gout, advanced remission gout, and normal controls. We find systemic inflammation in gout remission with MSU crystal deposition at the intercritical and advanced stages, evidenced by activated inflammatory pathways, strengthened inflammatory cell-cell interactions, and elevated arachidonic acid metabolic activity. We also find increased HLA-DQA1high classic monocytes and PTGS2high monocytes in advanced gout and overactivated CD8+ T cell subtypes in intercritical and advanced gout. Additionally, the osteoclast differentiation pathway is significantly enriched in monocytes, T cells, and B cells from advanced gout. Overall, we demonstrate systemic inflammation and distinctive immune responses in gout remission with MSU crystal deposition, allowing further exploration of the underlying mechanism and clinical significance in conversion from intercritical to advanced stage.

TGF-ß1 Triggers Salivary Hypofunction via Attenuating Protein Secretion and AQP5 Expression in Human Submandibular Gland Cells.

Aging-related salivary gland degeneration usually causes poor oral health. Periductal fibrosis frequently occurs in the submandibular gland of the elderly. Transforming growth factor ß1 (TGF-ß1) is the primary driving factor for fibrosis, which exhibits an increase in the fibrotic submandibular gland tissue. This study aimed to investigate the effects of TGF-ß1 on the human submandibular gland (HSG) cell secretory function and its influences on aquaporin 5 (AQP5) expressions and distribution. We found that TGF-ß1 reduces the protein secretion amount of HSG and leads to the abundance alteration of 151 secretory proteins. Data are available via ProteomeXchange with the identifier PXD043185. The majority of HSG secretory proteins (84.11%) could be matched to the human saliva proteome. Meanwhile, TGF-ß1 enhances the expression of COL4A2, COL5A1, COL7A1, COL1A1, COL2A1, and α-SMA, hinting that TGF-ß1 possesses the potential to drive HSG fibrosis-related events. Besides, TGF-ß1 also attenuates the AQP5 expression and its membrane distribution in HSGs. The percentage for TGF-ß1-induced AQP5 reduction (52.28%) is much greater than that of the TGF-ß1-induced secretory protein concentration reduction (16.53%). Taken together, we concluded that TGF-ß1 triggers salivary hypofunction via attenuating protein secretion and AQP5 expression in HSGs, which may be associated with TGF-ß1-driven fibrosis events in HSGs.

Beneficial or detrimental: Recruiting more types of benign cases for cancer diagnosis based on salivary glycopatterns.

With the advantages of convenient, painless and non-invasive collection, saliva holds great promise as a valuable biomarker source for cancer detection, pathological assessment and therapeutic monitoring. Salivary glycopatterns have shown significant potential for cancer screening in recent years. However, the understanding of benign lesions at non-cancerous sites in cancer diagnosis has been overlooked. Clarifying the influence of benign lesions on salivary glycopatterns and cancer screening is crucial for advancing the development of salivary glycopattern-based diagnostics. In this study, 2885 samples were analyzed using lectin microarrays to identify variations in salivary glycopatterns according to the number, location, and type of lesions. By utilizing our previously published data of tumor-associated salivary glycopatterns, the performance of machine learning algorithm for cancer screening was investigated to evaluate the effect of adding benign disease cases to the control group. The results demonstrated that both the location and number of lesions had discernible effects on salivary glycopatterns. And it was also revealed that incorporating a broad range of benign diseases into the controls improved the classifier's performance in distinguishing cancer cases from controls. This finding holds guiding significance for enhancing salivary glycopattern-based cancer screening and facilitates their practical implementation in clinical settings.

Antidiabetic activity of Tartary buckwheat protein-derived peptide AFYRW and its effects on protein glycosylation of pancreas in mice.

Diabetes Mellitus (DM) is one of the most important public health problems, and new antidiabetic drugs with fewer side effects are urgently needed. Here, we measured the antidiabetic effects of an antioxidant peptide (Ala-Phe-Tyr-Arg-Trp, AFYRW) from Tartary Buckwheat Albumin (TBA) in a high-fat diet/streptozotocin (HFD/STZ)-induced diabetic mouse model. The data showed that AFYRW suppressed hepatocyte steatosis and triglycerides while ameliorating insulin resistance in mice. Successively, the influence of AFYRW on aberrant protein glycosylation in diabetic mice was further investigated by lectin microarrays. The results suggested AFYRW could restore the expression of GalNAc, GalNAcα1-3Gal and GalNAcα1-3Galß1-3/4Glc recognized by PTL-I, Siaα2-3Galß1-4Glc(NAc)/Glc, Siaα2-3Gal, Siaα2-3 and Siaα2-3GalNAc recognized by MAL-II, terminating in GalNAcα/ß1-3/6Gal recognized by WFA and αGalNAc, αGal, anti-A and B recognized by GSI-I to normal levels in the pancreas of HFD-STZ-induced diabetic mice. This work may provide new targets for the future discovery of potential biomarkers to evaluate the efficacy of food-derived antidiabetic drugs based on precise alterations of glycopatterns in DM.

Characterization of aberrant glycosylation associated with osteoarthritis based on integrated glycomics methods.

BACKGROUND: Osteoarthritis (OA) is the most common form of arthritis, affecting millions of aging people. Investigation of abnormal glycosylation is essential for the understanding of pathological mechanisms of OA. METHODS: The total protein was isolated from OA (n = 13) and control (n = 11) cartilages. Subsequently, glycosylation alterations of glycoproteins in OA cartilage were investigated by lectin microarrays and intact glycopeptides analysis. Finally, the expression of glycosyltransferases involved in the synthesis of altered glycosylation was assessed by qPCR and GEO database. RESULTS: Our findings revealed that several glycopatterns, such as α-1,3/6 fucosylation and high-mannose type of N-glycans were altered in OA cartilages. Notably, over 27% of identified glycopeptides (109 glycopeptides derived from 47 glycoproteins mainly located in the extracellular region) disappeared or decreased in OA cartilages, which is related to the cartilage matrix degradation. Interestingly, the microheterogeneity of N-glycans on fibronectin and aggrecan core protein was observed in OA cartilage. Our results combined with GEO data indicated that the pro-inflammatory cytokines altered the expression of glycosyltransferases (ALG3, ALG5, MGAT4C, and MGAT5) which may contribute to the alterations in glycosylation. CONCLUSION: Our study revealed the abnormal glycopatterns and heterogeneities of site-specific glycosylation associated with OA. To our knowledge, it is the first time that the heterogeneity of site-specific N-glycans was reported in OA cartilage. The results of gene expression analysis suggested that the expression of glycosyltransferases was impacted by pro-inflammatory cytokines, which may facilitate the degradation of protein and accelerate the process of OA. Our findings provide valuable information for the understanding of molecular mechanisms in the pathogenesis of OA.

Joint Application of Multiple Inflammatory Cytokines in Diagnosis of Gout Flare.

Purpose: This study aimed to explore the accuracy for joint application of inflammatory cytokines in diagnosis of gout flare by comparison with peripheral blood cells. Methods: We collected the clinical data of 96 acute gout patients and 144 remission gout patients, and compared the levels of peripheral blood cells, inflammatory cytokines and blood biochemistry indexes between acute and remission gout. We respectively assessed the area under curves (AUCs) for single and multiple inflammatory cytokines including C-reactive protein (CRP), interleukin-1ß (IL-1ß), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α), and single and multiple peripheral blood cells including platelet (PLT), white blood cell (WBC), percentages of neutrophils (N%), lymphocytes (L%), eosinophils (E%), basophils (B%) in diagnosis of acute gout by receiver operating characteristic (ROC) curve analysis. Results: By contrast with remission gout, the levels of PLT, WBC, N%, CRP, IL-1ß, IL-6 and TNF-α increased, and the levels of L%, E% and B% decreased in acute gout. The AUCs of PLT, WBC, N%, L%, E% and B% in diagnosis of acute gout were respectively 0.591, 0.601, 0.581, 0.567, 0.608 and 0.635, while the AUC for joint application of these peripheral blood cells was 0.674. Moreover, the AUCs of CRP, IL-1ß, IL-6 and TNF-α in diagnosis of acute gout were respectively 0.814, 0.683, 0.622 and 0.746, while the AUC for joint application of these inflammatory cytokines was 0.883, reflecting significantly higher levels than peripheral blood cells. Conclusion: The joint application of multiple inflammatory cytokines can better distinguish acute gout from remission gout compared with peripheral blood cells.

Mutual regulation between glycosylation and transforming growth factor-ß isoforms signaling pathway.

Transforming growth factor-beta (TGF-ß) superfamily members orchestrate a wide breadth of biological processes. Through Sma and Mad (Smad)-related dependent or noncanonical pathways, TGF-ß members involve in the occurrence and development of many diseases such as cancers, fibrosis, autoimmune diseases, cardiovascular diseases and brain diseases. Glycosylation is one kind of the most common posttranslational modifications on proteins or lipids. Abnormal protein glycosylation can lead to protein malfunction and biological process disorder, thereby causing serious diseases. Previously, researchers commonly make comprehensive systematic overviews on the roles of TGF-ß signaling in a specific disease or biological process. In recent years, more and more evidences associate glycosylation modification with TGF-ß signaling pathway, and we can no longer disengage and ignore the roles of glycosylation from TGF-ß signaling to make investigation. In this review, we provide an overview of current findings involved in glycosylation within TGF-ßs and theirs receptors, and the interaction effects between glycosylation and TGF-ß subfamily signaling, concluding that there is an intricate mutual regulation between glycosylation and TGF-ß signaling, hoping to present the glycosylation regulatory patterns that concealed in TGF-ßs signaling pathways.

Pregnancy diagnosis and sex identification with urinary glycopatterns of two mammal species.

Glycome in urine could be promising biomarkers for detecting pregnancy diagnosis and sex noninvasively for animals, especially for rare species. We explore the applicability of grouping golden snub-nosed monkeys by sex or diagnosing pregnancy based on their urinary glycopatterns, which are determined via lectin microarray combining mass spectrometry analysis. Sprague-Dawley rats are used to verify whether this approach and whether the glycomic biomarkers can be generalized to other mammalian species. The results show that, for both species, lectin microarray combining mass spectrometry can distinguish individuals' pregnancy status and sex; significant differences are found in the types, amounts, and terminal modification of glycans between pregnant and non-pregnant females and between females and males. This indicates the approach could be generalized to other mammalian species to group sex and detect pregnancy, yet the glycopatterns appear to be species-specific and markers developed from one species may not be directly applicable to another.

[Glycosphingolipid-mediated apoptosis and tumor therapy: a review].

Glycosphingolipids (GSLs) are widely distributed in the phospholipid bilayer of various cell membranes, which play an important role in maintaining cell membrane stability, and regulate various cellular processes including adhesion, proliferation, apoptosis and recognition, as well as participate in various cellular activities. In addition, GSLs are not only involved in the process of apoptosis, but also regulate multiple signals in tumorigenesis and tumor development. The tumor-associated GSLs are expected to be used as diagnostic markers and immunotherapeutic targets for malignant tumors. These findings have important implications for the study of apoptosis and provide the new direction of tumor therapy. This review summarized the latest research progress of GSLs-mediated apoptosis and its effect on the genesis, development and metastasis of tumor cells. Moreover, we discussed the metabolic pathway of GSLs-mediated apoptosis and its application in tumor therapy, as well as the development prospect of targeted therapy strategies based on GSLs.

Diagnosis of hepatocellular carcinoma based on salivary protein glycopatterns and machine learning algorithms.

OBJECTIVES: Hepatocellular carcinoma (HCC) is difficult to diagnose early and progresses rapidly, making it one of the most deadly malignancies worldwide. This study aimed to evaluate whether salivary glycopattern changes combined with machine learning algorithms could help in the accurate diagnosis of HCC. METHODS: Firstly, we detected the alteration of salivary glycopatterns by lectin microarrays in 118 saliva samples. Subsequently, we constructed diagnostic models for hepatic cirrhosis (HC) and HCC using three machine learning algorithms: Least Absolute Shrinkage and Selector Operation, Support Vector Machine (SVM), and Random Forest (RF). Finally, the performance of the diagnostic models was assessed in an independent validation cohort of 85 saliva samples by a series of evaluation metrics, including area under the receiver operator curve (AUC), accuracy, specificity, and sensitivity. RESULTS: We identified alterations in the expression levels of salivary glycopatterns in patients with HC and HCC. The results revealed that the glycopatterns recognized by 22 lectins showed significant differences in the saliva of HC and HCC patients and healthy volunteers. In addition, after Boruta feature selection, the best predictive performance was obtained with the RF algorithm for the construction of models for HC and HCC. The AUCs of the RF-HC model and RF-HCC model in the validation cohort were 0.857 (95% confidence interval [CI]: 0.780-0.935) and 0.886 (95% CI: 0.814-0.957), respectively. CONCLUSIONS: Detecting alterations in salivary protein glycopatterns with lectin microarrays combined with machine learning algorithms could be an effective strategy for diagnosing HCC in the future.

Machine learning reveals salivary glycopatterns as potential biomarkers for the diagnosis and prognosis of papillary thyroid cancer.

The diagnosis of thyroid cancer, especially papillary thyroid cancer (PTC), is increasing rapidly worldwide. In this study, we aimed to study the glycosylation of salivary proteins associated with PTC and assess the likelihood that salivary glycopatterns may be a potential biomarker of PTC diagnosis. Firstly, 22 benign thyroid nodule (BTN) samples, 27 PTC samples, and 30 healthy volunteers (HV) samples were collected to probe the difference of salivary glycopatterns associated with PTC using lectin microarrays. Then, five machine learning models including K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) were established to distinguish HV, BTN and PTC based on the changes of salivary glycopatterns. As a result, SVM had the best diagnostic effect with an accuracy rate of 92 % in testing set. Besides, lectin microarrays were used to explore the differences in salivary glycopatterns of 26 paired salivary samples of PTC patients before and after operation in order to probe into salivary glycopatterns as potential biomarkers for prognosis of PTC patients. The results showed that the levels of salivary glycopatterns recognized by 6 different lectins in patients after the operation almost convergenced with HVs. This study could help to screen and assess patients with PTC and their prognosis based on precise changes of salivary glycopatterns.

Identification of CD8+ T Cell Related Biomarkers in Ovarian Cancer.

Background: Immunotherapy is a promising strategy for ovarian cancer (OC), and this study aims to identify biomarkers related to CD8+ T cell infiltration to further discover the potential therapeutic target. Methods: Three datasets with OC transcriptomic data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Two immunotherapy treated cohorts were obtained from the Single Cell Portal and Mariathasan's study. The infiltration fraction of immune cells was quantified using three different algorithms, Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), and microenvironment cell populations counter (MCPcounter), and single-sample GSEA (ssGSEA). Weighted gene co-expression network analysis (WGCNA) was applied to identify the co-expression modules and related genes. The nonnegative matrix factorization (NMF) method was proposed for sample classification. The mutation analysis was conducted using the "maftools" R package. Key molecular markers with implications for prognosis were screened by univariate COX regression analysis and K-M survival analysis, which were further determined by the receiver operating characteristic (ROC) curve. Results: A total of 313 candidate CD8+ T cell-related genes were identified by taking the intersection from the TCGA-OV and GSE140082 cohorts. The NMF clustering analysis suggested that patients in the TCGA-OV cohort were divided into two clusters and the Cluster 1 group showed a worse prognosis. In contrast, Cluster 2 had higher amounts of immune cell infiltration, elevated ssGSEA scores in immunotherapy, and a higher mutation burden. CSMD3, MACF1, PDE4DIP, and OBSCN were more frequently mutated in Cluster 1, while SYNE2 was more frequently mutated in Cluster 2. CD38 and CXCL13 were identified by univariate COX regression analysis and K-M survival analysis in the TCGA-OV cohort, which were further externally validated in GSE140082 and GSE32062. Of note, patients with lower CXCL13 expression showed a worse prognosis and the CR/PR group had a higher expression of CXCL13 in two immunotherapy treated cohorts. Conclusion: OC patients with different CD8+ T cell infiltration had distinct clinical prognoses. CXCL13 might be a potential therapeutic target for the treatment of OC.