Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Metastasis‐related mRNAs have showed great promise as prognostic biomarkers in various types of cancers. Therefore, we attempted to develop a metastasis‐associated gene signature to enhance prognostic prediction of breast cancer (BC) based on gene expression profiling. We firstly screened and identified 56 differentially expressed mRNAs by analysing BC tumour tissues with and without metastasis in the discovery cohort (GSE102484, n = 683). We then found 26 of these differentially expressed genes were associated with metastasis‐free survival (MFS) in the training set (GSE20685, n = 319). A metastasis‐associated gene signature built using a LASSO Cox regression model, which consisted of four mRNAs, can classify patients into high‐ and low‐risk groups in the training cohort. Patients with high‐risk scores in the training cohort had shorter MFS (hazard ratio [HR] 3.89, 95% CI 2.53‐5.98; P < 0.001), disease‐free survival (DFS) (HR 4.69, 2.93‐7.50; P < 0.001) and overall survival (HR 4.06, 2.56‐6.45; P < 0.001) than patients with low‐risk scores. The prognostic accuracy of mRNAs signature was validated in the two independent validation cohorts (GSE21653, n = 248; GSE31448, n = 246). We then developed a nomogram based on the mRNAs signature and clinical‐related risk factors (T stage and N stage) that predicted an individual's risk of disease, which can be assessed by calibration curves. Our study demonstrated that this 4‐mRNA signature might be a reliable and useful prognostic tool for DFS evaluation and will facilitate tailored therapy for BC patients at different risk of disease.     

Development and verification of a nomogram for prediction of recurrence‐free survival in clear cell renal cell carcinoma

Nowadays, gene expression profiling has been widely used in screening out prognostic biomarkers in numerous kinds of carcinoma. Our studies attempt to construct a clinical nomogram which combines risk gene signature and clinical features for individual recurrent risk assessment and offer personalized managements for clear cell renal cell carcinoma. A total of 580 differentially expressed genes (DEGs) were identified via microarray. Functional analysis revealed that DEGs are of fundamental importance in ccRCC progression and metastasis. In our study, 338 ccRCC patients were retrospectively analysed and a risk gene signature which composed of 5 genes was obtained from a LASSO Cox regression model. Further analysis revealed that identified risk gene signature could usefully distinguish the patients with poor prognosis in training cohort (hazard ratio [HR] = 3.554, 95% confidence interval [CI] 2.261‐7.472, P < .0001, n = 107). Moreover, the prognostic value of this gene‐signature was independent of clinical features (P = .002). The efficacy of risk gene signature was verified in both internal and external cohorts. The area under receiver operating characteristic curve of this signature was 0.770, 0.765 and 0.774 in the training, testing and external validation cohorts, respectively. Finally, a nomogram was developed for clinicians and did well in the calibration plots. This nomogram based on risk gene signature and clinical features might provide a practical way for recurrence prediction and facilitating personalized managements of ccRCC patients after surgery.     

A 16-mRNA signature optimizes recurrence-free survival prediction of Stages II and III gastric cancer

High-throughput messenger RNA (mRNA) analysis has become a powerful tool for exploring tumor recurrence or metastasis mechanisms. Here, we constructed a signature to predict the recurrence risk of Stages II and III gastric cancer (GC) patients. A least absolute shrinkage and selection operator method Cox regression model was utilized to construct the signature. Using this method, a 16-mRNA signature was identified to be associated with the relapse-free survival of Stages II and III GCs in training dataset GSE62254 (n = 194). Then this signature was validated in an independent Gene Expression Omnibus cohort GSE26253 (n = 297) and a dataset of The Cancer Genome Atlas (TCGA; n = 235). This classifier could successfully screen out the high-risk Stages II and III GCs in the training cohort (hazard ratio [HR] = 40.91; 95% confidence interval [CI] = 5.58–299.7; p < .0001). Analysis in two independent validation cohorts yielded consistent results (GSE26253: HR = 1.69, 95% CI = 1.17–2.43,; p = .0045; TCGA: HR = 2.01, 95% CI = 1.13–3.56, p = .0146). Cox regression analyses revealed that the risk score derived from this signature was an independent risk factor in Stages II and III GCs. Besides, a nomogram was constructed to serve clinical practice. Through gene set variation analysis, we found several gene sets associated with chemotherapeutic drug resistance and tumor metastasis significantly enriched in high-risk patients. In summary, this 16-mRNA signature can be used as a powerful tool for prognostic evaluation and help clinicians identify high-risk patients.     

Prognostic value of a gene signature in clear cell renal cell carcinoma

Renal cancer is a common urogenital system malignance. Novel biomarkers could provide more and more critical information on tumor features and patients’ prognosis. Here, we performed an integrated analysis on the discovery set and established a three-gene signature to predict the prognosis for clear cell renal cell carcinoma (ccRCC). By constructing a LASSO Cox regression model, a 3-messenger RNA (3-mRNA) signature was identified. Based on the 3-mRNA signature, we divided patients into high- and low-risk groups, and validated this by using three other data sets. In the discovery set, this signature could successfully distinguish between the high- and low-risk patients (hazard ratio (HR), 2.152; 95% confidence interval (CI),1.509–3.069; p < 0.0001). Analysis of internal and two external validation sets yielded consistent results (internal: HR, 2.824; 95% CI, 1.601–4.98; p < 0.001; GSE29609: HR, 3.002; 95% CI, 1.113–8.094; p = 0.031; E-MTAB-3267: HR, 2.357; 95% CI, 1.243–4.468; p = 0.006). Time-dependent receiver operating characteristic (ROC) analysis indicated that the area under the ROC curve at 5 years was 0.66 both in the discovery and internal validation set, while the two external validation sets also suggested good performance of the 3-mRNA signature. Besides that, a nomogram was built and the calibration plots and decision curve analysis indicated the good performance and clinical utility of the nomogram. In conclusion, this 3-mRNA classifier proved to be a useful tool for prognostic evaluation and could facilitate personalized management of ccRCC patients.     

A five-mRNA signature associated with post-translational modifications can better predict recurrence and survival in cervical cancer

High mortality of patients with cervical cancer (CC) stresses the imperative of prognostic biomarkers for CC patients. Additionally, the vital status of post-translational modifications (PTMs) in the progression of cancers has been reported by numerous researches. Therefore, the purpose of this research was to dig a prognostic signature correlated with PTMs for CC. We built a five-mRNA (GALNTL6, ARSE, DPAGT1, GANAB and FURIN) prognostic signature associated with PTMs to predict both disease-free survival (DFS) (hazard ratio [HR] = 3.967, 95% CI = 1.985-7.927; P < .001) and overall survival (HR = 2.092, 95% CI = 1.138-3.847; P = .018) for CC using data from The Cancer Genome Atlas database. Then, the robustness of the signature was validated using GSE44001 and the Human Protein Atlas (HPA) database. CIBERSORT algorithm analysis displayed that activated CD4 memory T cell was also an independent indicator for DFS (HR = 0.426, 95% CI = 0.186-0.978; P = .044) which could add additional prognostic value to the signature. Collectively, the PTM-related signature and activated CD4 memory T cell can provide new avenues for the prognostic predication of CC. These findings give further insights into effective treatment strategies for CC, providing opportunities for further experimental and clinical validations.     

The discovery of a novel eight-mRNA-lncRNA signature predicting survival of hepatocellular carcinoma patients

Increasing evidence indicates that the expressions of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) undergo a frequent and aberrant change in carcinogenesis and cancer development. But some research was carried out on mRNA-lncRNA signatures for prediction of hepatocellular carcinoma (HCC) prognosis. We aimed to establish an mRNA-lncRNA signature to improve the ability to predict HCC patients’ survival. The subjects from the cancer genome atlas (TCGA) data set were randomly divided into two parts: training data set (n = 246) and testing data set (n = 124). Using computational methods, we selected eight gene signatures (five mRNAs and three lncRNAs) to generate the risk score model, which were significantly correlated with overall survival of patients with HCC in both training and testing data set. The signature had the ability to classify the patients in training data set into a high-risk group and low-risk group with significantly different overall survival (hazard ratio = 4.157, 95% confidence interval = 2.648-6.526, P < 0.001). The prognostic value was further validated in testing data set and the entire data set. Further analysis revealed that this signature was independent of tumor stage. In addition, Gene Set Enrichment Analysis suggested that high risk score group was associated with cell proliferation and division related pathways. Finally, we developed a well-performed nomogram integrating the prognostic signature and other clinical information to predict 3- and 5-year overall survival. In conclusion, the prognostic mRNAs and lncRNAs identified in our study indicate their potential role in HCC biogenesis. The risk score model based on the mRNA-lncRNA may be an efficient classification tool to evaluate the prognosis of patients’ with HCC.     

A robust two-gene signature for glioblastoma survival prediction

Glioblastoma multiforme (GBM) is a highly malignant brain tumor. We explored the prognostic gene signature in 443 GBM samples by systematic bioinformatics analysis, using GSE16011 with microarray expression and corresponding clinical data from Gene Expression Omnibus as the training set. Meanwhile, patients from The Chinese Glioma Genome Atlas database (CGGA) were used as the test set and The Cancer Genome Atlas database (TCGA) as the validation set. Through Cox regression analysis, Kaplan-Meier analysis, t-distributed Stochastic Neighbor Embedding algorithm, clustering, and receiver operating characteristic analysis, a two-gene signature (GRIA2 and RYR3) associated with survival was selected in the GSE16011 dataset. The GRIA2-RYR3 signature divided patients into two risk groups with significantly different survival in the GSE16011 dataset (median: 0.72, 95% confidence interval [CI]: 0.64-0.98, vs median: 0.98, 95% CI: 0.65-1.61 years, logrank test P < .001), the CGGA dataset (median: 0.84, 95% CI: 0.70-1.18, vs median: 1.21, 95% CI: 0.95-2.94 years, logrank test P = .0017), and the TCGA dataset (median: 1.03, 95% CI: 0.86-1.24, vs median: 1.23, 95% CI: 1.04-1.85 years, logrank test P = .0064), validating the predictive value of the signature. And the survival predictive potency of the signature was independent from clinicopathological prognostic features in multivariable Cox analysis. We found that after transfection of U87 cells with small interfering RNA, GRIA2 and RYR3 influenced the biological behaviors of proliferation, migration, and invasion of glioblastoma cells. In conclusion, the two-gene signature was a robust prognostic model to predict GBM survival.     

Identification of a prognostic metabolic gene signature in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a clinically diverse disease. Given the numerous genetic mutations and variations associated with it, a prognostic gene signature that can be related to the overall survival (OS) is a clinical implication. We used the mRNA expression profiles and clinicopathological data of patients with DLBCL from the Gene Expression Omnibus (GEO) database to identify a metabolism-related gene signature. Using LASSO regression analysis, a novel 13-metabolic gene signature was identified to evaluate prognosis. The information gathered was used to construct the nomogram model to improve risk stratification and quantify risk factors for individual patients. We performed gene set enrichment analysis to identify the enriched signalling axes to further understand the underlying biological pathways. The receiver operating characteristic (ROC) curve revealed a satisfactory performance in the training cohorts. The model also showed clinical benefit when compared to the standard prognostic factors (P < .05) in validation cohorts. This study aimed to combine metabolic dysregulation with clinical features of patients with DLBCL to generate a prognostic model that might not only indicate the value of the metabolic microenvironment for prognostic stratification but also improve the decision-making during individual therapy.     

ERCC1 expression as a prognostic and predictive factor in patients with non-small cell lung cancer: a meta-analysis

It is hypothesized that high expression of the excision repair cross-complementation group 1 (ERCC1) gene might be a positive prognostic factor, but predict decreased sensitivity to platinum-based chemotherapy. Results from the published data are inconsistent. To derive a more precise estimation of the relationship between ERCC1 and the prognosis and predictive response to chemotherapy of non-small cell lung cancer (NSCLC), a meta-analysis was performed. An electronic search of the PubMed and Embase database was performed. Hazard ratio (HR) for overall survival (OS) was pooled in early stage patients received surgery alone to analyze the prognosis of ERCC1 on NSCLC. HRs for OS in patients received surgery plus adjuvant chemotherapy and in patients received palliative chemotherapy and relative risk (RR) for overall response to chemotherapy were aggregated to analyze the prediction of ERCC1 on NSCLC. The pooled HR indicated that high ERCC1 levels were associated with longer survival in early stage patients received surgery alone (HR, 0.69; 95% confidence interval (CI), 0.58–0.83; P = 0.000). There was no difference in survival between high and low ERCC1 levels in patients received surgery plus adjuvant chemotherapy (HR, 1.41; 95% CI, 0.93–2.12; P = 0.106). However, high ERCC1 levels were associated with shorter survival and lower response to chemotherapy in advanced NSCLC patients received palliative chemotherapy (HR, 1.75; 95% CI, 1.39–2.22; P = 0.000; RR, 0.77; 95% CI, 0.64–0.93; P = 0.007; respectively). The meta-analysis indicated that high ERCC1 expression might be a favourable prognostic and a drug resistance predictive factor for NSCLC.     

An integrated mRNA-lncRNA signature for relapse prediction in laryngeal cancer

Patients with laryngeal cancer with early relapse usually have a poor prognosis. In this study, we aimed to identify a multi-gene signature to improve the relapse prediction in laryngeal cancer. One microarray data set GSE27020 (training set, N = 109) and one RNA-sequencing data set (validation set, N = 85) were included into the analysis. In the training set, the microarray expression profile was re-annotated into an mRNA-long noncoding RNA (lncRNA) biphasic profile. Then, LASSO Cox regression model identified nine relapse-related RNA (eight mRNA and one lncRNA), and a risk score was calculated for each sample according to the model coefficients. Patients with high-risk showed poorer relapse-free survival than patients with low risk (hazard ratios (HR): 6.189, 95% confidence interval (CI): 3.075-12.460, P < 0.0001). The risk score demonstrated good accuracy in predicting the relapse (area under time-dependent receiver-operating characteristic (AUC): 0.859 at 1 year, 0.822 at 3 years, and 0.815 at 5 years). The results were validated in the validation set (HR: 3.762, 95% CI: 1.594-8.877, P = 0.011; AUC: 0.770 at 1 year, 0.769 at 3 years, and 0.728 at 5 years). The multivariate analysis reached consistent results after adjustment by multiple confounders. When compared with a 27-gene signature, a 2-lncRNA signature, and Tumor-Node-Metastasis stage, the risk score also showed better performance (P < 0.05). In conclusion, we successfully developed a robust mRNA-lncRNA signature that can accurately predict the relapse in laryngeal cancer.     

Prognostic value of Ki-67 in nasopharyngeal carcinoma: a meta-analysis

The prognostic value of Ki-67 in nasopharyngeal carcinoma (NPC) was controversial according to previous studies. We aimed to clarify the association between K-67 expression and survival in NPC through meta-analysis. We conducted a meta-analysis to explore the potential prognostic effect of Ki-67 on overall survival (OS), disease-free survival (DFS), distant metastasis-free survival (DMFS), and local recurrence-free survival (LRFS) in NPC. A total of 13 studies comprising 1314 NPC patients were included. High Ki-67 expression was associated with poor OS (hazard ratio [HR]= 2.70, 95% confidence interval [CI]= 1.97–3.71, P<0.001), DFS (HR = 1.93, 95% CI = 1.49–2.50, P<0.001), and LRFS (HR = 1.86, 95% CI = 1.11–3.12, P=0.019). However, there was no significant association between Ki-67 and DMFS (HR = 1.37, 95% CI = 0.78–2.38, P=0.270). Furthermore, the prognostic role of Ki-67 was maintained throughout different sample sizes, analyses of HR, and study designs for OS and DFS in various subgroups. Elevated Ki-67 expression is a reliable prognostic factor for poorer survival outcomes in NPC.     

Retrospective assessment of cyclin-dependent kinase 5 mRNA and protein expression and its association with patient survival in breast cancer

Cyclin-dependent kinase 5 (Cdk5) is an atypical member of the cyclin-dependent kinase family and functions as a serine/threonine kinase that can be activated by non-cyclin binding activators p35 or p39. Cdk5 expression and activity has been linked with the development and progression of cancer; however, its expression in breast cancer has not been fully described. Protein expression of Cdk5 was determined in a large cohort of early-stage invasive breast cancer tumours (n = 1110) with long-term follow-up data using immunohistochemistry. Expression of CDK5 mRNA was assessed in the METABRIC cohort (n = 1980). Low nuclear and cytoplasmic expression of Cdk5 expression was significantly associated with shorter breast cancer-specific survival (P = .004 and P = .001, respectively). Importantly, low nuclear and cytoplasmic expression of Cdk5 remained associated with survival in multivariate analysis, including potentially confounding factors (hazard ratio (HR) = 0.612, 95% confidence interval (CI) = 0.418-0.896, P = .011 and HR = 0.507, 95% CI = 0.318-0.809, P = .004, respectively). In addition, low nuclear and cytoplasmic expression of Cdk5 was significantly associated with clinicopathological criteria associated with adverse patient prognosis. Low CDK5 mRNA expression was associated with shorter patient survival (P = .005) in the METABRIC cohort; no associations between copy gain or loss and survival were observed. These data suggest that low Cdk5 expression is associated with poor clinical outcome of breast cancer patients and may be of clinical relevance.     

Early immune reconstitution as predictor for outcomes after allogeneic hematopoietic cell transplant; a tri-institutional analysis

Background aimsCD4 immune reconstitution (IR) after allogeneic hematopoietic cell transplant (allo-HCT) correlates with lower non-relapse mortality (NRM), but its impact on leukemia relapse remains less clear, especially in children. We studied the correlation between IR of lymphocyte subsets and HCT outcomes in a large cohort of children/young adults with hematological malignancies.MethodsWe retrospectively analyzed CD4, CD8, B-cell and natural killer (NK) cell reconstitution in patients after first allo-HCT for a hematological malignancy at three large academic institutions (n = 503; period 2008–2019). We used Cox proportional hazard and Fine–Gray competing risk models, martingale residual plots and maximally selected log-rank statistics to assess the impact of IR on outcomes.ResultsAchieving CD4 >50 and/or B cells >25 cells/μL before day 100 after allo-HCT was a predictor of lower NRM (CD4 IR: hazard ratio [HR] 0.26, 95% confidence interval [CI] 0.11–0.62, P = 0.002; CD4 and B cell IR: HR 0.06, 95% CI 0.03–0.16, P < 0.001), acute graft-versus-host disease (GVHD) (CD4 and B cell IR: HR 0.02, 95% CI 0.01–0.04, P < 0.001) and chronic GVHD (CD4 and B cell IR: HR 0.16, 95% CI 0.05–0.49, P = 0.001) in the full cohort, and of lower risk of relapse (CD4 and B cell IR: HR 0.24, 95% CI 0.06–0.92, P = 0.038) in the acute myeloid leukemia subgroup. No correlation between CD8 and NK-cell IR and relapse or NRM was found.ConclusionsCD4 and B-cell IR was associated with clinically significant lower NRM, GVHD and, in patients with acute myeloid leukemia, disease relapse. CD8 and NK-cell IR was neither associated with relapse nor NRM. If confirmed in other cohorts, these results can be easily implemented for risk stratification and clinical decision making.     

Effect of early adverse events on response and survival outcomes of advanced melanoma patients treated with vemurafenib or vemurafenib plus cobimetinib: A pooled analysis of clinical trial data

This study aimed to evaluate the impact of early adverse events on overall survival (OS), progression‐free survival (PFS) and objective response within a pooled secondary analysis of participants treated with first‐line vemurafenib or vemurafenib plus cobimetinib in the clinical trials BRIM3 and coBRIM. The study included 583 participants who received vemurafenib monotherapy and 247 who received vemurafenib plus cobimetinib. Adverse events requiring vemurafenib/cobimetinib dose adjustment within the first 28 days of therapy were significantly associated with OS (hazard ratio (HR) [95% CI]: dose reduced/interrupted = 0.79 [0.65–0.96]; drug withdrawn = 1.18 [0.71–1.96]; p = 0.032), PFS (HR [95% CI]: dose reduced/interrupted = 0.82 [0.67–0.99]; drug withdrawn = 1.58 [0.97–2.58]; p = 0.017) and objective response (odds ratio (OR) [95% CI]: dose reduced/interrupted = 1.35 [0.99–1.85]; drug withdrawn = 0.17 [0.06–0.43]; p = <0.001). Arthralgia occurring within the first 28 days of vemurafenib or vemurafenib plus cobimetinib therapy was also significantly associated with favourable OS (p = 0.026), PFS (p = 0.042) and objective response (p = 0.047).     

Pooling analysis reveals that galectin-1 is a reliable prognostic biomarker in various cancers

Galectin-1 is reported to be upregulated in various human cancers. However, the relationship between galectin-1 expression and cancer prognosis has not been systematically assessed. In this study, we searched PubMed, Web of Science, and Embase to collect all relevant studies and a meta-analysis was performed. We found that increased galectin-1 expression was associated with tumor size (odds ratio [OR] = 1.75; 95% confidence interval [CI]: 1.06–2.89; p = 0.029), clinical stage (OR = 3.89; 95% CI: 2.40–6.31; p < 0.001), and poorer differentiation (OR = 1.39; 95% CI: 1.14–1.69; p = 0.001), but not with age (OR = 1.07; 95% CI: 0.82–1.39; p = 0.597), sex (OR = 0.89; 95% CI: 0.74–1.07; p = 0.202), or lymph node metastasis (OR = 2.57; 95% CI: 0.98–6.78; p = 0.056). In addition, we found that high galectin-1 expression levels were associated with poor overall survival (HR = 2.12; 95% CI: 1.71–2.64; p < 0.001). The results were further validated using The Cancer Genome Atlas data set. Moreover, high galectin-1 expression was significantly associated with disease-free survival (hazard ratio [HR] = 1.60; 95% CI: 1.17–2.19; p = 0.003), progression-free survival (HR = 1.93; 95% CI: 1.65–2.25; p < 0.001), and cancer-specific survival (HR = 1.82; 95% CI: 1.30–2.55; p < 0.001). Our meta-analysis demonstrated that galectin-1 might be a useful common biomarker for predicting prognosis in patients with cancer.     

Prognostic utility and clinical significance of lysyl oxidase-like 2 protein expression in digestive system cancers

Lysyl oxidase-like 2 (LOXL2) participates in the occurrence and development of digestive system cancers (DSCs). The aim of this study was to determine whether LOXL2 protein could serve as a prognostic biomarker in patients with DSCs. Relevant studies published before October 1, 2018 were identified from a comprehensive literature review in PubMed, Web of Science, and Embase. This meta-analysis was conducted via STATA/SE 14.1 software. Finally, a total of 12 publications and 6 different kinds of DSCs were identified. Meta-analysis indicated that increased expression of LOXL2 protein was significantly correlated with reduced overall survival (hazard ratios [HR]: 1.52; 95% confidence interval [CI]: 1.32–1.72) and worse progression-free survival/disease-free survival (HR: 2.15; 95% CI: 1.48–2.83) in cases with DSCs. In addition, clinicopathological parameters, including tumor invasion, lymph node metastasis, distant metastasis, and clinical stage were significantly related to LOXL2 protein expression in DSCs. High LOXL2 protein expression is significantly associated with worse clinical outcomes in DSCs and its expression level may represent a candidate prognostic biomarker in these cancers.     

Effects of metastasectomy and other factors on survival of patients with ovarian metastases from gastric cancer: a systematic review and meta-analysis

Ovarian metastasis from gastric cancer (Krukenberg tumor [KT]) has no consensus treatment and the role of surgical treatment is still controversial. Identifying prognostic factors for KT could help guide the management of this tumor. We used a meta-analysis to evaluate the prognostic value of metastasectomy and other factors in patients with KT to develop a treatment plan. We searched literature in PubMed, Cochrane library and EMBASE. We analyzed hazard ratios (HR) and 95% confidence intervals (CI) with respect to overall survival (OS). The meta-analysis included 12 cohort studies with 1,031 patients associated with longer OS following metastasectomy (HR = 0.41; 95% CI = 0.32–0.53; P < 0.001), R0 resection (HR = 0.37; 95% CI = 0.26–0.53; P < 0.001), metachronous ovarian metastasis (HR = 0.74; 95% CI = 0.58–0.93; P = 0.012), size of KT (<5 cm) (HR = 0.74; 95% CI = 0.58–0.95; P = 0.019), ECOG PS (Eastern Cooperative Oncology Group performance status) 0 to 1 (HR = 0.48; 95% CI = 0.29–0.80; P = 0.004), tumor confined to ovary (HR = 0.40; 95% CI = 0.16–0.99; P = 0.047), and tumor confined to pelvic cavity (HR = 0.36; 95% CI = 0.14–0.92; P = 0.033). Shorter OS was associated with peritoneal carcinomatosis (HR = 2.00; 95% CI = 1.25–3.21; P = 0.004), ascites (HR = 1.66; 95% CI = 1.19–2.31; P = 0.003) and positive CEA (HR = 1.41; 95% CI = 1.10–1.82; P = 0.007). Gastrectomy led to a slight improvement in OS, but without statistical significance (HR = 0.69; 95% CI = 0.47–1.02; P = 0.061). No significant difference in OS was observed in patients with signet-ring cells (HR = 1.17; 95% CI = 0.91–1.51; P = 0.226), bilateral ovarian metastasis (HR = 0.87; 95% CI = 0.70–1.08; P = 0.212), age ≥ 50 years (HR = 0.93; 95% CI = 0.71–1.22; P = 0.619), positive CA19-9 (HR = 1.01; 95% CI = 0.75–1.35; P = 0.960), and positive CA-125 (HR = 0.98; 95% CI = 0.73–1.33; P = 0.915). Various factors affect OS in patients with KT.