The landscape and prognostic value of immune characteristics in uterine corpus endometrial cancer

In the present study, we explored the clinical and immunological characteristics of 575 uterine corpus endometrial carcinoma (UCEC) samples obtained from The Cancer Genome Atlas (TCGA) using the ESTIMATE and CIBERSORT algorithms. First, Kaplan–Meier and univariate Cox regression analyses indicated that the immune cell score was a prognostic factor for overall survival (OS) and recurrence-free survival (RFS). Multivariate Cox regression analysis further revealed that the immune cell score was an independent prognostic factor for UCEC patients. Second, we investigated the correlation between the infiltration levels of 22 types of immune cells and the immune score. Survival analysis based on the 22 immune cell types showed that higher levels of regulatory T cell, activated NK cell, and follicular helper T-cell infiltration were associated with longer OS, while higher levels of CD8+ T cell and naive B-cell infiltration were associated with longer RFS. Next, we performed differential expression and prognosis analyses on 1534 immune-related genes and selected five from 14 candidate genes to construct a prognostic prediction model. The area under the receiver-operating characteristic (ROC) curve (AUC) for 3- and 5-year survival were 0.711 and 0.728, respectively. Further validation using a stage I–II subgroup showed similar results, presenting AUC values for 3- and five-year survival of 0.677 and 0.692, respectively. Taken together, the present study provides not only a deeper understanding of the relationship between UCEC and the immune landscape but also guidance for the future development of UCEC immunotherapy.     

Prognostic significance of immune landscape in tumour microenvironment of endometrial cancer

Tumour microenvironment (TME) is crucial to tumorigenesis. This study aimed to uncover the differences in immune phenotypes of TME in endometrial cancer (EC) using Uterine Corpus Endometrial Carcinoma (UCEC) cohort and explore the prognostic significance. We employed GVSA enrichment analysis to cluster The Cancer Genome Atlas (TCGA) EC samples into immune signature cluster modelling, evaluated immune cell profiling in UCEC cohort (n = 538) and defined four immune subtypes of EC. Next, we analysed the correlation between immune subtypes and clinical data including patient prognosis. Furthermore, we analysed the expression of immunomodulators and DNA methylation modification. The profiles of immune infiltration in TCGA UCEC cohort showed significant difference among four immune subtypes of EC. Among each immune subtype, natural killer T cells (NKT), dendritic cells (DCs) and CD8⁺T cells were significantly associated with EC patients survival. Each immune subtype exhibited specific molecular classification, immune cell characterization and immunomodulators expression. Moreover, the expression immunomodulators were significantly related to DNA methylation level. In conclusion, the identification of immune subtypes in EC tissues could reveal unique immune microenvironments in EC and predict the prognosis of EC patients.     

Multiomics profiling of the expression and prognosis of MCMs in endometrial carcinoma

Minichromosome maintenance (MCM) family members are a group of genes involved in regulating DNA replication and cell division and have been identified as oncogenes in various cancer types. Several experimental studies have suggested that MCMs are dysregulated in endometrial carcinoma (EC). However, the expression pattern, clinical value and functions of different MCMs have yet to be analyzed systematically and comprehensively. We analyzed expression, survival rate, DNA alteration, PPT network, GGI network, functional enrichment cancer hallmarks and drug sensitivity of MCMs in patients with EC based on diverse datasets, including Oncomine, GEPIA, Kaplan–Meier Plotter, HPA, Sangerbox and GSCALite databases. The results indicated that most MCM members were increased in EC and showed a prognostic value in survival analysis, which were considerately well in terms of PFS and OS prognostic prediction. Importantly, functional enrichment, PPI network and GGI network suggested that MCMs interact with proteins related to DNA replication and cell division, which may be the mechanism of MCM promote EC progression. Further data mining illustrated that MCMs have broad DNA hypomethylation levels and high levels of copy number aberrations in tumor tissue samples, which may be the mechanism causing the high expression level of MCMs. Moreover, MCM2 can activate or suppress diverse cancer-related pathways and is implicated in EC drug sensitivity. Taking together, our findings illustrate the expression pattern, clinical value and function of MCMs in EC and imply that MCMs are potential targets for precision therapy and new biomarkers for the prognosis of patients with EC.     

Prognostic implication and functional annotations of Rad50 expression in patients with prostate cancer

Increasing evidence has shown that Rad50, a protein involved in the DNA damage repair process, significantly correlated with tumor prognosis. This study focused on Rad50 expression in tumor samples and its prognostic value for patients with prostate cancer (PCa). In this study, significantly elevated Rad50 expression in PCa tissues compared to normal tissues (P < .01). Five independent Oncomine databases validated significant differential expression of Rad50 (P < .001). Hence, 80 patients with PCa from Fudan University Shanghai Cancer Center (FUSCC) and 351 patients with PCa with available protein expression data from The Cancer Genome Atlas (TCGA) were included to investigate the survival benefit. Univariate and multivariate Cox regression analyses were performed to investigate the significance of clinicopathological factors on disease-free survival (DFS) and overall survival (OS). Kaplan-Meier analysis indicated that elevated Rad50 protein expression levels significantly correlated with unfavorable DFS (P = .005) in the FUSCC cohort and poorer OS (P = .04) in TCGA cohort. Furthermore, coregulation analysis of proteins indicated that 76 coregulated proteins were associated with Rad50, while 11 most highly involved hub proteins, including Rad50, MRE11A, DUT, POLR3A, MCM3AP, RECQL, PNPT1, RANBP3, DDX1, SNRPB, and UGN, were significantly coregulated in the protein-protein interaction network. Functional enrichment analysis consecutively indicated significant functions and signaling pathways including DNA replication, spliceosome, DNA geometric change, homologous recombination, and G2M checkpoint. This study first reveals that elevated Rad50 expression is significantly associated with aggressive progression and poor survival for patients with PCa. Together, these data suggest that Rad50 may act as an oncoprotein, guide the molecular diagnosis, and may shed light on novel individual therapeutic strategies for progressive PCa patients.     

PRMT5 promotes colorectal cancer growth by interaction with MCM7

Protein arginine methyltransferase 5 (PRMT5) is a type of methyltransferase enzyme that can catalyse arginine methylation of histones and non-histone proteins. Accumulating evidence indicates that PRMT5 promotes cancer development and progression. However, its function in colorectal cancer (CRC) is poorly understood. In this study, we revealed the oncogenic roles of PRMT5 in CRC. We found that PRMT5 promoted CRC cell proliferation, migration and invasion in vitro and in vivo. We identified minichromosome maintenance-7 (MCM7) as the direct PRMT5-binding partner. A co-immunoprecipitation (co-IP) assay indicated that PRMT5 physically interacted with MCM7 and that the direct binding domain was located between residues 1-248 in MCM7. In addition, our results from analysis of 99 CRC tissues and 77 adjacent non-cancerous tissues indicated that the PRMT5 and MCM7 expression levels were significantly higher in CRC tissues than in control tissues, which was further confirmed by bioinformatic analysis using TCGA and GEO datasets. We also found that MCM7 promoted CRC cell proliferation, migration and invasion in vitro. Furthermore, we observed that increased PRMT5 expression predicted unfavourable patient survival in CRC patients and in the subgroup of patients with a tumour size of ≤5 cm. These data suggested that PRMT5 and MCM7 might be novel potential targets for the treatment of CRC.     

Identification and Validation of a Prognostic Model Based on Three MVI-Related Genes in Hepatocellular Carcinoma

MVI has significant clinical value for treatment selection and prognosis evaluation in hepatocellular carcinoma (HCC). We aimed to construct a model based on MVI-Related Genes (MVIRGs) for risk assessment and prognosis prediction in patients with HCC. This study utilized various statistical analysis methods for prognostic model construction and validation in the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts, respectively. In addition, immunohistochemistry and qRT-PCR were used to analyze and identify the value of the model in our cohort. After the analyses, 153 differentially expressed MVIRGs were identified, and three key genes were selected to construct a prognostic model. The high-risk group showed significantly lower overall survival (OS), and this trend was observed in all subgroups: different age groups, genders, stages, and grades. Risk score was a risk factor independent of age, gender, stage, and grade. Moreover, the ICGC cohort validated the prognostic value of the model corresponding to the TCGA. In our cohort, qRT-PCR and immunohistochemistry showed that all three genes had higher expression levels in HCC samples than in normal controls. High expression levels of genes and high-risk scores showed significantly lower recurrence-free survival (RFS) and OS, especially in MVI-positive HCC samples. Therefore, the prognostic model constructed by three MVIRGs can reliably predict the RFS and OS of patients with HCC and is valuable for guiding clinical treatment selection and prognostic assessment of HCC.     

PPP1R14B is a diagnostic prognostic marker in patients with uterine corpus endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is one of the most common malignancies of the female genital tract. A recently discovered protein-coding gene, PPP1R14B, can inhibit protein phosphatase 1 (PP1) as well as different PP1 holoenzymes, which are important proteins regulating cell growth, the cell cycle, and apoptosis. However, the association between PPP1R14B expression and UCEC remains undefined. The expression profiles of PPP1R14B in multiple cancers were analysed based on TCGA and GTE databases. Then, PPP1R14B expression in UCEC was investigated by gene differential analysis and single gene correlation analysis. In addition, we performed gene ontology term analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, gene set enrichment analysis, and Kaplan–Meier survival analysis to predict the potential function of PPP1R14B and its role in the prognosis of UCEC patients. Then, a tool for predicting the prognosis of UCEC, namely, a nomogram model, was constructed. PPP1R14B expression was higher in UCEC tumour tissues than in normal tissues. The results revealed that PPP1R14B expression was indeed closely associated with tumour development. The results of Kaplan–Meier plotter data indicated that patients with high PPP1R14b expression had poorer overall survival, disease-specific survival, and progression-free interval than those with low expression. A nomogram based on the results of multifactor Cox regression was generated. PPP1R14B is a key player in UCEC progression, is associated with a range of adverse outcomes, and can serve as a prognostic marker in the clinic.     

Molecular Predictors of Long-Term Survival in Glioblastoma Multiforme Patients

Glioblastoma multiforme (GBM) is the most common and aggressive adult primary brain cancer, with <10% of patients surviving for more than 3 years. Demographic and clinical factors (e.g. age) and individual molecular biomarkers have been associated with prolonged survival in GBM patients. However, comprehensive systems-level analyses of molecular profiles associated with long-term survival (LTS) in GBM patients are still lacking. We present an integrative study of molecular data and clinical variables in these long-term survivors (LTSs, patients surviving >3 years) to identify biomarkers associated with prolonged survival, and to assess the possible similarity of molecular characteristics between LGG and LTS GBM. We analyzed the relationship between multivariable molecular data and LTS in GBM patients from the Cancer Genome Atlas (TCGA), including germline and somatic point mutation, gene expression, DNA methylation, copy number variation (CNV) and microRNA (miRNA) expression using logistic regression models. The molecular relationship between GBM LTS and LGG tumors was examined through cluster analysis. We identified 13, 94, 43, 29, and 1 significant predictors of LTS using Lasso logistic regression from the somatic point mutation, gene expression, DNA methylation, CNV, and miRNA expression data sets, respectively. Individually, DNA methylation provided the best prediction performance (AUC = 0.84). Combining multiple classes of molecular data into joint regression models did not improve prediction accuracy, but did identify additional genes that were not significantly predictive in individual models. PCA and clustering analyses showed that GBM LTS typically had gene expression profiles similar to non-LTS GBM. Furthermore, cluster analysis did not identify a close affinity between LTS GBM and LGG, nor did we find a significant association between LTS and secondary GBM. The absence of unique LTS profiles and the lack of similarity between LTS GBM and LGG, indicates that there are multiple genetic and epigenetic pathways to LTS in GBM patients.     

PHF6 promotes the progression of endometrial carcinoma by increasing cancer cells growth and decreasing T-cell infiltration

Uterine corpus endometrial carcinoma (UCEC) is the most common cancer of the female reproductive tract. The overall survival of advanced and recurrent UCEC patients is still unfavourable nowadays. It is urgent to find a predictive biomarker and block tumorgenesis at an early stage. Plant homeodomain finger protein 6 (PHF6) is a key player in epigenetic regulation, and its alterations lead to various diseases, including tumours. Here, we found that PHF6 expression was upregulated in UCEC tissues compared with normal tissues. The UCEC patients with high PHF6 expression had poor survival than UCEC patients with low PHF6 expression. PHF6 mutation occurred in 12% of UCEC patients, and PHF6 mutation predicted favourable clinical outcome in UCEC patients. Depletion of PHF6 effectively inhibited HEC-1-A and KLE cell proliferation in vitro and decreased HEC-1-A cell growth in vivo. Furthermore, high PHF6 level indicated a subtype of UCECs characterized by low immune infiltration, such as CD3⁺ T-cell infiltration. While knockdown of PHF6 in endometrial carcinoma cells increased T-cell migration by promoting IL32 production and secretion. Taken together, our findings suggested that PHF6 might play an oncogenic role in UCEC patients. Thus, PHF6 could be a potential biomarker in predicting the prognosis of UCEC patients. Depletion of PHF6 may be a novel therapeutic strategy for UCEC patients.     

基于DNA甲基化模式筛选评估结肠腺癌预后的标记物

为了通过分析DNA甲基化谱识别出与预后相关的结肠腺癌亚型。从TCGA数据库获取了结肠腺癌患者的甲基化数据,通过差异甲基化分析和构建COX比例风险回归模型筛得与预后显著相关的CpG位点,并通过一致性聚类识别出7个亚型。生存分析和临床特征检验显示7个亚型间预后差异显著且亚型特征可由多种临床特征反映。此外,用7个亚型间识别出的差异甲基化位点构建的基于SMO(序列最小最优化)的预测模型在各亚型上都有较高的AUC值,并用检验集进行了验证。综上,本研究利用生物信息学算法识别了7个预后差异的结肠腺癌亚型并挖掘了它们的特异性甲基化标记。该研究结果或可使得结肠腺癌预后被更精准地评估,为早期诊断及治疗方案提供新思路。     

Construction and validation of a seven-gene signature for predicting overall survival in patients with kidney renal clear cell carcinoma via an integrated bioinformatics analysis

ABSTRACT

Kidney renal clear cell carcinoma (KIRC) remains a significant challenge worldwide because of its poor prognosis and high mortality rate, and accurate prognostic gene signatures are urgently required for individual therapy. This study aimed to construct and validate a seven-gene signature for predicting overall survival (OS) in patients with KIRC. The mRNA expression profile and clinical data of patients with KIRC were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC). Prognosis-associated genes were identified, and a prognostic gene signature was constructed. Then, the prognostic efficiency of the gene signature was assessed. The results obtained using data from the TCGA were validated using those from the ICGC and other online databases. Gene set enrichment analyses (GSEA) were performed to explore potential molecular mechanisms. A seven-gene signature (PODXL, SLC16A12, ZIC2, ATP2B3, KRT75, C20orf141, and CHGA) was constructed, and it was found to be effective in classifying KIRC patients into high- and low-risk groups, with significantly different survival based on the TCGA and ICGC validation data set. Cox regression analysis revealed that the seven-gene signature had an independent prognostic value. Then, we established a nomogram, including the seven-gene signature, which had a significant clinical net benefit. Interestingly, the seven-gene signature had a good performance in distinguishing KIRC from normal tissues. GSEA revealed that several oncological signatures and GO terms were enriched. This study developed a novel seven-gene signature and nomogram for predicting the OS of patients with KIRC, which may be helpful for clinicians in establishing individualized treatments.     

基于在线数据库和真实世界样本探讨肺腺癌患者POLE/POLD1突变的临床意义

摘要 目的：POLE和POLD1突变导致DNA聚合酶校对功能丧失可能会影响基因组稳定性并导致突变增加和肿瘤形成。本文结合在线数据库和真实世界样本进一步分析肺腺癌（LUAD）患者POLE和/或 POLD1 突变的临床意义。方法：纳入2021年1月～2021年8月徐州医科大学附属医院肺癌术后组织标本115例,利用二代测序技术（NGS）检测基因突变;从癌症基因组图谱（TCGA）数据库收集肺腺癌数据集,通过Cbioportal在线数据库获得肿瘤突变分布图,通过Cibersort法计算获得样本的免疫相关细胞浸润情况。结果：真实世界样本中POLE/ POLD1突变的比例为7.83%（9/115）。TCGA数据显示POLE/POLD1突变的LUAD患者总生存期（OS）减少（P=0.0359）。然而,携带该突变的患者并发其他基因改变的频率明显增加,尤其是与TP53突变存在正相关;同时,POLE/POLD1突变与LUAD组织浸润性免疫杀伤细胞呈正相关,与免疫抑制细胞呈负相关,提示这部分患者对免疫检查点抑制剂（ICI）敏感。结论：LUAD患者POLE/POLD1突变预示较高的肿瘤突变负荷和免疫微环境改变,可作为ICI疗效预测的潜在生物标志物,值得临床关注。     

A deep survival interpretable radiomics model of hepatocellular carcinoma patients

This work aims to identify a new radiomics signature using imaging phenotypes and clinical variables for risk prediction of overall survival (OS) in hepatocellular carcinoma (HCC) patients treated with stereotactic body radiation therapy (SBRT). 167 patients were retrospectively analyzed with repeated nested cross-validation to mitigate overfitting issues. 56 radiomic features were extracted from pre-treatment contrast-enhanced (CE) CT images. 37 clinical factors were obtained from patients' electronic records. Variational autoencoders (VAE) based survival models were designed for radiomics and clinical features and a convolutional neural network (CNN) survival model was used for the CECT. Finally, radiomics, clinical and raw image deep learning network (DNN) models were combined to predict the risk probability for OS. The final models yielded c-indices of 0.579 (95%CI: 0.544–0.621), 0.629 (95%CI: 0.601–0.643), 0.581 (95%CI: 0.553–0.613) and 0.650 (95%CI: 0.635–0.683) for radiomics, clinical, image input and combined models on nested cross validation scheme, respectively. Integrated gradients method was used to interpret the trained models. Our interpretability analysis of the DNN showed that the top ranked features were clinical liver function and liver exclusive of tumor radiomics features, which suggests a prominent role of side effects and toxicities in liver outside the tumor region in determining the survival rate of these patients. In summary, novel deep radiomic analysis provides improved performance for risk assessment of HCC prognosis compared with Cox survival models and may facilitate stratification of HCC patients and personalization of their treatment strategies. Liver function was found to contribute most to the OS for these HCC patients and radiomics can aid in their management.     

Screening and verification of microRNA promoter methylation sites in hepatocellular carcinoma

The promoter methylation mode of microribonucleic acid (miRNA) plays a crucial role in the process of hepatocellular carcinoma (HCC). Therefore, the primary purpose of this study was to screen and verify the miRNA methylation sites associated with the overall survival (OS) and clinical characteristics of HCC patients. Methylation-related data were from the Cancer Genome Atlas (TCGA). R software was utilized to screen the methylation sites. The least absolute shrinkage and selection operator algorithm was utilized to develop the miRNA promoter methylation models. Then, methylation-specific polymerase chain reaction was performed with 146 HCC tissues to verify the accuracy of the vascular infiltration-related model. Additionally, we verified the functions of vascular infiltration-related miRNA by utilizing cells transfected with miR-199a-3p mimic. The model for predicting OS of HCC patients contained eight methylation sites. The Kaplan-Meier analysis suggested that the model could divide HCC patients into high- and low-risk groups (P < .0001). COX regression analysis suggested that the model (P < .001; 95% CI, 1.264-2.709) and T category (P < .001; 95% CI, 1.472-3.119) were independent risk factors for affecting OS of HCC patients. The model for predicting vascular infiltration, pathological grade, and clinical stage contained 7, 10, and 9 methylation sites respectively, with their area under the receiver operating characteristic curve (AUC) values 0.667, 0.745, and 0.725, respectively. The functional analysis suggested that miRNA methylation is involved in various biological processes such as WNT, MAPK, and mTOR signaling pathways. The accuracy of the vascular infiltration-related model was consistent with our previous bioinformatics assay. And upregulation of miR-199a-3p decreased migration and invasion abilities. The screened miRNA promoter methylation sites can be served as biomarkers for judging OS, vascular infiltration, pathology grade, and clinical stage. It can also provide new targets for improving the treatment and prognosis of HCC patients.     

Five candidate biomarkers associated with the diagnosis and prognosis of cervical cancer

Purpose: Cervical cancer (CC) is one of the most general gynecological malignancies and is associated with high morbidity and mortality. We aimed to select candidate genes related to the diagnosis and prognosis of CC.Methods: The mRNA expression profile datasets were downloaded. We also downloaded RNA-sequencing gene expression data and related clinical materials from TCGA, which included 307 CC samples and 3 normal samples. Differentially expressed genes (DEGs) were obtained by R software. GO function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed in the DAVID dataset. Using machine learning, the optimal diagnostic mRNA biomarkers for CC were identified. We used qRT-PCR and Human Protein Atlas (HPA) database to exhibit the differences in gene and protein levels of candidate genes.Results: A total of 313 DEGs were screened from the microarray expression profile datasets. DNA methyltransferase 1 (DNMT1), Chromatin Assembly Factor 1, subunit B (CHAF1B), Chromatin Assembly Factor 1, subunit A (CHAF1A), MCM2, CDKN2A were identified as optimal diagnostic mRNA biomarkers for CC. Additionally, the GEPIA database showed that the DNMT1, CHAF1B, CHAF1A, MCM2 and CDKN2A were associated with the poor survival of CC patients. HPA database and qRT-PCR confirmed that these genes were highly expressed in CC tissues.Conclusion: The present study identified five DEmRNAs, including DNMT1, CHAF1B, CHAF1A, MCM2 and Kinetochore-related protein 1 (KNTC1), as potential diagnostic and prognostic biomarkers of CC.     

Identification of mini‐chromosome maintenance 8 as a potential prognostic marker and its effects on proliferation and apoptosis in gastric cancer

Mini‐chromosome maintenance (MCM) proteins play important roles in initiating eukaryotic genome replication. The MCM family of proteins includes several members associated with the development and progression of certain cancers. We performed online data mining to assess the expression of MCMs in gastric cancer (GC) and the correlation between their expression and survival in patients with GC. Notably, MCM8 expression was undoubtedly up‐regulated in GC, and higher expression correlated with shorter overall survival (OS) and progression‐free survival (PFS) in patients with GC. However, the role of MCM8 in GC has not been previously explored. Our in vitro experiments revealed that MCM8 knockdown inhibited cell growth and metastasis. Moreover, MCM8 knockdown induced apoptosis. Mechanistically, the expression levels of Bax and cleaved caspase‐3 were increased, whereas Bcl‐2 expression decreased. Additionally, we demonstrated that MCM8 knockdown suppressed tumorigenesis in vivo. Overall, these results suggest that MCM8 plays a significant role in GC progression.     

A large-scale transcriptome analysis identified ELANE and PRTN3 as novel methylation prognostic signatures for clear cell renal cell carcinoma

DNA methylation was involved in the progress of many types of cancer including clear cell renal cell carcinomas (ccRCCs). This study aimed to identify the prognostic DNA methylation biomarkers for the ccRCCs by a large-scale RNA-seq analysis. The DNA methylation data and the corresponding clinical information of the patients with ccRCCs were extracted from TCGA database and randomly divided into the training group and the validation group. The differentially expressed CpG sites and the survival-related CpG sites were further identified, which was combined into CpG sites pair and followed by screening the survival-related pairs. The C-index and the forward search algorithms were constructed to identify the prognostic signatures for the patients with ccRCCs. The prognostic signatures were verified by the validation dataset and the protein–protein interactions (PPI) network analysis was performed on the CPG sites of the signature. A total of 9,861 differentially expressed CPG sites were identified and 567 CpG sites were found to relate to the overall survival (OS) of the patients with ccRCCs. Besides, 1,146 CPG sites pairs were found to be related to the OS of the ccRCCs samples and the signature composed of seven CpG sites pairs were obtained to predict the prognosis of patients with ccRCCs and the results were verified in the validation dataset. Besides, the PPI network analysis showed that ELANE and PRTN3 gene may be associated with the invasion and metastasis of ccRCCs and could function as potential prognostic and therapeutic signatures for ccRCCs.     

Combination of Pre-Treatment DWI-Signal Intensity and S-1 Treatment: A Predictor of Survival in Patients with Locally Advanced Pancreatic Cancer Receiving Stereotactic Body Radiation Therapy and Sequential S-1

OBJECTIVE: To identify whether the combination of pre-treatment radiological and clinical factors can predict the overall survival (OS) in patients with locally advanced pancreatic cancer (LAPC) treated with stereotactic body radiation and sequential S-1 (a prodrug of 5-FU combined with two modulators) therapy with improved accuracy compared with that of established clinical and radiologic risk models. METHODS: Patients admitted with LAPC underwent diffusion weighted imaging (DWI) scan at 3.0-T (b = 600 s/mm²). The mean signal intensity (SI_b = 600) of region-of-interest (ROI) was measured. The Log-rank test was done for tumor location, biliary stent, S-1, and other treatments and the Cox regression analysis was done to identify independent prognostic factors for OS. Prediction error curves (PEC) were used to assess potential errors in prediction of survival. The accuracy of prediction was evaluated by Integrated Brier Score (IBS) and C index. RESULTS: 41 patients were included in this study. The median OS was 11.7 months (2.8-23.23 months). The 1-year OS was 46%. Multivariate analysis showed that pre-treatment SI_b = 600 value and administration of S-1 were independent predictors for OS. The performance of pre-treatment SI_b = 600 and S-1 treatment in combination was better than that of SI_b = 600 or S-1 treatment alone. CONCLUSION: The combination of pre-treatment SI_b = 600 and S-1 treatment could predict the OS in patients with LAPC undergoing SBRT and sequential S-1 therapy with improved accuracy compared with that of established clinical and radiologic risk models.     

DDX39 as a predictor of clinical prognosis and immune checkpoint therapy efficacy in patients with clear cell renal cell carcinoma

DEAD-box protein 39 (DDX39) has been demonstrated to be a tumorigenic gene in multiple tumor types, but its role in the progression and immune microenvironment of clear cell renal cell cancer (ccRCC) remains unclear. The aim of the present study was to investigate the role of DDX39 in the ccRCC tumor progression, immune microenvironment and efficacy of immune checkpoint therapy.The DDX39 expression level was first detected in tumors in the public data and then verified in ccRCC samples from Changzheng Hospital. The prognostic value of DDX39 expression was assessed in the Cancer Genome Atlas (TCGA) and ccRCC patients from Changhai Hospital. The role of DDX39 in promoting ccRCC was analyzed by bioinformatic analysis and in vitro experiments. The association between DDX39 expression and immune cell infiltration and immune inhibitory markers was analyzed, and its value in predicting the immune checkpoint therapy efficacy in ccRCC were evaluated in the public database. DDX39 expression was elevated in Oncomine, GEO and TCGA ccRCC databases, as well as in Changzheng ccRCC samples. In TCGA ccRCC patients, increased DDX39 expression predicted worse overall survival (OS) (p<0.0001) and progression-free interval (PFI) (p<0.0001), and was shown as an independent predictive factor for OS (p=0.002). These findings were consistent with those from Changhai ccRCC patients. In addition, GO and GSEA analysis identified DDX39 as a pro-ccRCC gene. In vitro experiments confirmed the role of DDX39 in promoting ccRCC cell. Finally, DDX39 was found to be positively correlated with a variety of immune inhibitory markers, and could predict the adverse efficacy of immune checkpoint therapy in TIDE analysis. In conclusion, Increased DDX39 in ccRCC patients predicted worse clinical prognosis, promoted ccRCC cell proliferation, migration and invasion, and also predicted adverse efficacy of immune checkpoint therapy.