Fudan University Shanghai Cancer Center Cohort Research Articles

A ten long noncoding RNA-based prognostic risk model construction and mechanism study in the basal-like immune-suppressed subtype of triple-negative breast cancer.

According to the Fudan University Shanghai Cancer Center (FUSCC) system, triple-negative breast cancer (TNBC) is divided into four stable subtypes: (I) luminal androgen receptor, (II) immunomodulatory, (III) basal-like immune-suppressed (BLIS), and (IV) mesenchymal-like. However, the treatment outcomes of the corresponding targeted therapies are unsatisfactory, especially for the BLIS subtype. Therefore, we aimed to identify the key long noncoding RNAs (lncRNAs) to construct a prognostic model for BLIS subtype and discover potential targets to explore potential therapeutic strategies in this study. The FUSCC cohort was used to establish a prognostic risk model via least absolute shrinkage and selection operator (LASSO) and Cox regression analysis. The Cancer Genome Atlas (TCGA) cohort was then used to evaluate and verify the model. To understand the functional aspects of the model, functional, immune landscape, mutation, and drug sensitivity analyses were performed between high- and low-risk groups. Ten prognostic-related lncRNAs identified, including C5ORF66-AS2, DIO3OS, FZD10-DT, LINC00393, LNC-ERI1-32, LNC-FOXO1-2, LNC-SPARCL1-1, HCG23, LNC-MMD-4 and LNC-TMEM106C-6, were selected for risk score system construction. The results showed that the model constructed could divide the patients with BLIS subtype into two groups of high and low risk, and patients with higher risk scores had shorter recurrence-free survival. In addition, drug sensitivity analysis identified 3 compounds, including BMS-754807, cytochalasin b, and linifanib, that could have a potential therapeutic effect on patients with the BLIS subtype. The risk prognosis model showed good prognostic value for the BLIS subtype patients, and the ten lncRNAs may be potential therapeutic targets.

Predicting Lymph Node Metastasis From Primary Cervical Squamous Cell Carcinoma Based on Deep Learning in Histopathologic Images

We developed a deep learning framework to accurately predict the lymph node status of patients with cervical cancer based on hematoxylin and eosin–stained pathological sections of the primary tumor. In total, 1524 hematoxylin and eosin–stained whole slide images (WSIs) of primary cervical tumors from 564 patients were used in this retrospective, proof-of-concept study. Primary tumor sections (1161 WSIs) were obtained from 405 patients who underwent radical cervical cancer surgery at the Fudan University Shanghai Cancer Center (FUSCC) between 2008 and 2014; 165 and 240 patients were negative and positive for lymph node metastasis, respectively (including 166 with positive pelvic lymph nodes alone and 74 with positive pelvic and para-aortic lymph nodes). We constructed and trained a multi-instance deep convolutional neural network based on a multiscale attention mechanism, in which an internal independent test set (100 patients, 228 WSIs) from the FUSCC cohort and an external independent test set (159 patients, 363 WSIs) from the Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma cohort of the Cancer Genome Atlas program database were used to evaluate the predictive performance of the network. In predicting the occurrence of lymph node metastasis, our network achieved areas under the receiver operating characteristic curve of 0.87 in the cross-validation set, 0.84 in the internal independent test set of the FUSCC cohort, and 0.75 in the external test set of the Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma cohort of the Cancer Genome Atlas program. For patients with positive pelvic lymph node metastases, we retrained the network to predict whether they also had para-aortic lymph node metastases. Our network achieved areas under the receiver operating characteristic curve of 0.91 in the cross-validation set and 0.88 in the test set of the FUSCC cohort. Deep learning analysis based on pathological images of primary foci is very likely to provide new ideas for preoperatively assessing cervical cancer lymph node status; its true value must be validated with cervical biopsy specimens and large multicenter datasets.

Abstract P1-13-15: Elevated TCEAL9 Expression Is Correlated With Trastuzumab-based Neoadjuvant Chemotherapy Resistance In HER2-positive Breast Cancer

Abstract Background: -Trastuzumab-based neoadjuvant chemotherapy has shown to have remarkable clinical benefits for HER2-positive breast cancer patients who had higher tumor burden. -Patients who achieved pathological complete response (pCR) are known to have better prognosis. -However, certain patients have little response or are not sensitive to trastuzumab-based treatment regimens. -Understanding the mechanism of trastuzumab resistance is crucial for the development of new therapeutic strategy. Objectives: To investigate the role of TCEAL9 in developing trastuzumab resistance in HER2-positive breast cancer Methods: A total of 83 patients who received paclitaxel, carboplatin and trastuzumab neoadjuvant chemotherapy in Fudan University Shanghai Cancer Center(FUSCC) from 2016 to 2018 were enrolled in this study. After completed neoadjuvant chemotherapy and surgery, gene expressions were compared between the pCR and non-pCR groups. Total RNA from formalin-fixed paraffin-embedded tissue sections was isolated and RNA-sequencing was performed. Gene sets from GEO dataset GSE52707 were used to analyze TCEAL9 expression in resistant and non-resistant cell lines. Gene expression levels were converted into log2 values and row-wised standardized. BT-474 and SK-BR-3 cell lines were transduced with each expression lentivirus, followed by selection with puromycin for stable expression. TCEAL9 mRNA and protein level evaluation was evaluated by qPCR and western blot. The influence of TCEAL9 expression on proliferation and sensitivity to HER2-targeted therapy was evaluated by CCK8. BT-474 and SK-BR-3 transfected cells were plated in 96-well plates with 4,000 cells per well. After 3 or 5 days of incubation with trastuzumab, pertuzumab or lapatinib, the viability of cells was measured using Cell Proliferation Assay. Comparisons between Kaplan-Meier curves were performed using the long-rank test. Results: TCEAL9 was elevated significantly (P< 0.05) in non-pCR patients in the FUSCC cohort and was associated with lapatinib resistance in GSE52707 from GEO datasets. Patients with elevated TCEAL9 expression had worse recurrence-free survival (RFS), distant metastasis-free survival (DMFS) and progression-free survival (PPS) (all P< 0.05)by using KM-plotter. Overexpression of TCEAL9 was associated with lapatinib(IC50= 5.56 vs 10.90nM) and trastuzumab + pertuzumab(IC50= 745 vs 635nM) resistance in BT-474 and SK-BR-3 respectively, but has no influence in proliferation. In this study, we found that TCEAL9 could induce HER2-positive breast cancer cells resistance to HER2-targeted therapy through the activation of mTOR signaling pathway. After EGFR stimulation, TCEAL9 has a higher mTOR phosphorylation level in BT-474 cells. TCEAL9 elevation also increased HER2 and mTOR phosphorylation after lapatinib treatment in SK-BR-3 cells. In addition, the elevation of TCEAL9 has a positive correlation with HER2 signaling pathways such as EGFR, PIK3R1, FOXO1 and AKT3 in TCGA datasets. Conclusions: TCEAL9 expression correlates with trastuzumab resistance and high TCEAL9 expression is associated with poor prognosis in HER2-positive breast cancer patients. Citation Format: Chih Wan Goh, Wei-Ru Chi, Liyi Zhang, Qi Zhang, Ming Chen, Min Xiong, Douwaner Liu, Hengyu Ren, Bingqiu Xiu, Jingyan Xue, Yayun Chi, Jiong Wu. Elevated TCEAL9 Expression Is Correlated With Trastuzumab-based Neoadjuvant Chemotherapy Resistance In HER2-positive Breast Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-13-15.

Aberrant APOBEC3C expression induces characteristic genomic instability in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a well-known lethal and heterogeneous disease. Apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like (APOBEC) is an important mutagenic driver that has seldom been investigated in PDAC. Therefore, this study investigated the significance of APOBEC3C in PDAC. First, cytosine deamination-associated mutation signatures were identified in the PDAC cohorts from TCGA and Fudan University Shanghai Cancer Center (FUSCC) datasets, and C > X-enriched kataegis regions were identified in the FUSCC cohort (12 to 27 counts per sample). Patients were stratified according to APOBEC3C expression, and high APOBEC3C expression was found to correlate with a higher motif enrichment score of 5’-CC-3’ and an elevated kataegis count within PCSK5 and NES genes. Second, we compared APOBEC expression in PDAC and normal pancreatic tissues and found that APOBEC3C was substantially upregulated in PDAC. APOBEC3C-overexpressing cell lines were generated to substantiate the effects of APOBEC3C on PDAC genome, including alterations in single-nucleotide variant (SNV) classes (higher proportion of C > T conversions) and the formation of kataegis regions (newly occurring kataegis regions detected in ACHE and MUC6 genes). Three different PDAC cohorts (FUSCC, TCGA, and QCMG) were analysed to evaluate the prognostic value of APOBEC3C, and APOBEC3C overexpression predicted shorter survival. Finally, the APOBEC3C overexpression correalted with the PDAC tumour microenvironment (TME) remodelling, APOBEC3C expression was associated with the invasion of CD4 + T lymphocytes and CD8 + T lymphocytes (cytotoxic T lymphocytes, CTLs), indicating enhanced immune activity and validating the practicality of APOBEC3C for guiding immunotherapy.

Predictive factors, preventive implications, and personalized surgical strategies for bone metastasis from lung cancer: population-based approach with a comprehensive cancer center-based study.

Bone metastasis (BM) and skeletal-related events (SREs) happen to advanced lung cancer (LC) patients without warning. LC-BM patients are often passive to BM diagnosis and surgical treatment. It is necessary to guide the diagnosis and treatment paradigm for LC-BM patients from reactive medicine toward predictive, preventive, and personalized medicine (PPPM) step by step. Two independent study cohorts including LC-BM patients were analyzed, including the Surveillance, Epidemiology, and End Results (SEER) cohort (n = 203942) and the prospective Fudan University Shanghai Cancer Center (FUSCC) cohort (n = 59). The epidemiological trends of BM in LC patients were depicted. Risk factors for BM were identified using a multivariable logistic regression model. An individualized nomogram was developed for BM risk stratification. Personalized surgical strategies and perioperative care were described for FUSCC cohort. The BM incidence rate in LC patients grew (from 17.53% in 2010 to 19.05% in 2016). Liver metastasis was a significant risk factor for BM (OR = 4.53, 95% CI = 4.38-4.69) and poor prognosis (HR = 1.29, 95% CI = 1.25-1.32). The individualized nomogram exhibited good predictive performance for BM risk stratification (AUC = 0.784, 95%CI = 0.781-0.786). Younger patients, males, patients with high invasive LC, and patients with other distant site metastases should be prioritized for BM prevention. Spine is the most common site of BM, causing back pain (91.5%), pathological vertebral fracture (27.1%), and difficult walking (25.4%). Spinal surgery with personalized spinal reconstruction significantly relieved pain and improved daily activities. Perioperative inflammation, immune, and nutrition abnormities warrant personalized managements. Radiotherapy needs to be recommended for specific postoperative individuals. The presence of liver metastasis is a strong predictor of LC-BM. It is recommended to take proactive measures to prevent BM and its SREs, particularly in young patients, males, high invasive LC, and LC with liver metastasis. BM surgery and perioperative management are personalized and required. In addition, adjuvant radiation following separation surgery must also be included in PPPM-guided management. The online version contains supplementary material available at 10.1007/s13167-022-00270-9.

PRDM16 Inhibits Cell Proliferation and Migration via Epithelial-to-Mesenchymal Transition by Directly Targeting Pyruvate Carboxylase in Papillary Thyroid Cancer

PRDM16 (known as MEL1), a member of the PR domain zinc finger family, has been implicated in multiple biological processes, including cancers. It is not clear yet whether PRDM16 is involved in tumor progress of papillary thyroid cancer (PTC). We identified the PRDM16 expression level in PTC tissues by qRT-PCR and analyzed its relationship with clinical characteristics in both Fudan University Shanghai Cancer Center (FUSCC) and TCGA cohorts. We tested the function of PRDM16 in PTC cells both in vivo and in vitro. We found a direct downstream target of PRDM16, pyruvate carboxylase (PC), by RNA-sequencing, rescue experiments, luciferase assay, and chromatin immunoprecipitation assay. PRDM16 was downregulated in papillary thyroid cancer tissues and was significantly related with lymph node metastases and extrathyroidal extension in both FUSCC and TCGA cohorts. Overexpression of PRDM16 could attenuate proliferation and migration of PTC cells via inhibiting the epithelial-to-mesenchymal transition process. PC was upregulated in papillary thyroid cancer tissues. Knockdown of PC could inhibit proliferation and migration in TPC-1 and K1 cells. The repression effect on cell proliferation and migration from PRDM16 was PC dependent. PRDM16 could directly bind to the PC promoter and inhibit its expression at the transcription level. Moreover, the mRNA expression level of PRDM16 and PC was negatively related in human PTC tissues. In conclusion, PRDM16 exhibited an antitumor effect and EMT inhibition function in PTC by directly binding with the PC promoter. PRDM16 may be a novel therapeutic target in papillary thyroid cancer.

Identification of Key Functional Gene Signatures Indicative of Dedifferentiation in Papillary Thyroid Cancer.

Background: Differentiated thyroid cancer (DTC) is the most common type of thyroid cancer. Many of them can relapse to dedifferentiated thyroid cancer (DDTC) and exhibit different gene expression profiles. The underlying mechanism of dedifferentiation and the involved genes or pathways remained to be investigated.Methods: A discovery cohort obtained from patients who received surgical resection in the Fudan University Shanghai Cancer Center (FUSCC) and two validation cohorts derived from Gene Expression Omnibus (GEO) database were used to screen out differentially expressed genes in the dedifferentiation process. Weighted gene co-expression network analysis (WGCNA) was constructed to identify modules highly related to differentiation. Gene Set Enrichment Analysis (GSEA) was used to identify pathways related to differentiation, and all differentially expressed genes were grouped by function based on the GSEA and literature reviewing data. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to control the number of variables in each group. Next, we used logistic regression to build a gene signature in each group to indicate differentiation status, and we computed receiver operating characteristic (ROC) curve to evaluate the indicative performance of each signature.Results: A total of 307 upregulated and 313 downregulated genes in poorly differentiated thyroid cancer (PDTC) compared with papillary thyroid cancer (PTC) and normal thyroid (NT) were screened out in FUSCC cohort and validated in two GEO cohorts. WGCNA of 620 differential genes yielded the seven core genes with the highest correlation with thyroid differentiation score (TDS). Furthermore, 395 genes significantly correlated with TDS in univariate logistic regression analysis were divided into 11 groups. The areas under the ROC curve (AUCs) of the gene signature of group transcription and epigenetic modification, signal and substance transport, extracellular matrix (ECM), and metabolism in the training set [The Cancer Genome Atlas (TCGA) cohort] and validation set (combined GEO cohort) were both >0.75. The gene signature based on group transcription and epigenetic modification, cilia formation and movement, and proliferation can reflect the patient's disease recurrence state.Conclusion: The dedifferentiation of DTC is affected by a variety of mechanisms including many genes. The gene signature of group transcription and epigenetic modification, signal and substance transport, ECM, and metabolism can be used as biomarkers for DDTC.

Construction of a robust prognostic model for adult adrenocortical carcinoma: Results from bioinformatics and real-world data.

This study aims to construct a robust prognostic model for adult adrenocortical carcinoma (ACC) by large‐scale multiomics analysis and real‐world data. The RPPA data, gene expression profiles and clinical information of adult ACC patients were obtained from The Cancer Proteome Atlas (TCPA), Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Integrated prognosis‐related proteins (IPRPs) model was constructed. Immunohistochemistry was used to validate the prognostic value of the IPRPs model in Fudan University Shanghai Cancer Center (FUSCC) cohort. 76 ACC cases from TCGA and 22 ACC cases from GSE10927 in NCBI’s GEO database with full data for clinical information and gene expression were utilized to validate the effectiveness of the IPRPs model. Higher FASN (P = .039), FIBRONECTIN (P < .001), TFRC (P < .001), TSC1 (P < .001) expression indicated significantly worse overall survival for adult ACC patients. Risk assessment suggested significantly a strong predictive capacity of IPRPs model for poor overall survival (P < .05). IPRPs model showed a little stronger ability for predicting prognosis than Ki‐67 protein in FUSCC cohort (P = .003, HR = 3.947; P = .005, HR = 3.787). In external validation of IPRPs model using gene expression data, IPRPs model showed strong ability for predicting prognosis in TCGA cohort (P = .005, HR = 3.061) and it exhibited best ability for predicting prognosis in GSE10927 cohort (P = .0898, HR = 2.318). This research constructed IPRPs model for predicting adult ACC patients’ prognosis using proteomic data, gene expression data and real‐world data and this prognostic model showed stronger predictive value than other biomarkers (Ki‐67, Beta‐catenin, etc) in multi‐cohorts.

Fatty Acid Synthase Correlates With Prognosis-Related Abdominal Adipose Distribution and Metabolic Disorders of Clear Cell Renal Cell Carcinoma.

Purpose: Lipid metabolism reprogramming is a major pathway in tumor evolution. This study investigated fatty acid synthase (FASN) mRNA expression in anthropometric adipose tissue and elucidated the prognostic value and potential mechanism of clear cell renal cell carcinoma (ccRCC).Materials and Methods: Transcription profiles were obtained from 533 ccRCC samples in The Cancer Genome Atlas (TCGA) cohorts. Real-time quantitative PCR (RT-qPCR) and immunohistochemistry were performed to detect FASN expression in 380 paired ccRCC and normal tissues from the Fudan University Shanghai Cancer Center (FUSCC). Visceral adipose tissue (VAT) and subcutaneous adipose tissue were at the level of the umbilicus as measured by magnetic resonance imaging (MRI). Non-targeted metabolomics and in vitro experiments were used to reveal the biological functions of FASN.Results: Increased FASN expression was significantly relevant to advanced T, N, and American Joint Committee on Cancer (AJCC) stages (p < 0.01) and significantly correlated to poor progression-free survival (PFS) and overall survival (OS) of 913 ccRCC patients in FUSCC and TCGA cohorts. Pearson's correlation coefficient indicated that FASN amplification was positively correlated to VAT% (r = 0.772, p < 0.001), which significantly correlated to poor PFS (HR = 2.066, p = 0.028) and OS (HR = 2.773, p = 0.023) in the FUSCC cohort. Transient inhibition or overexpression of FASN significantly regulated A498 and 786O cell proliferation and migration by regulating epithelial–mesenchymal transition. Inhibition of FASN led to a higher apoptotic rate and decreased lipid droplet formation compared with normal control in ccRCC cells. Non-targeted metabolomics showed that decreased de novo lipogenesis might be required to sustain an elevation of glycolytic activity in 786O cells by regulating galactinol, dl-lactate, N-acetylaspartylglutamate, and sucrose, thereby participating in carcinogenesis and progression of ccRCC.Conclusion: This study demonstrated that FASN expression is positively related to aggressive cell proliferation, migration, apoptosis, and lipid droplet formation and regulates metabolic disorders of the ccRCC microenvironment. Additionally, elevated FASN mRNA expression is significantly correlated to the abdominal obesity distribution, especially VAT%, which is a significant predictor of a poor prognosis for ccRCC patients.

A Novel Three-lncRNA Signature Predicts the Overall Survival of HNSCC Patients.

A number of long non-coding RNAs (lncRNAs) have been found to be involved in tumor progression and associated with disease prognoses in various types of cancer. Our study identified a novel three-lncRNA signature to predict survival of head and neck squamous cell caner (HNSCC) patients. We utilized The Cancer Genome Atlas (TCGA) cohort to screen out overall survival (OS)-associated lncRNAs in HNSCC and further developed a model to identify a lncRNA signature for evaluating disease status and prognosis. The lncRNA signature was then validated in HNSCC patients from our Fudan University Shanghai Cancer Center (FUSCC) cohort. LINC02434, AL139327.2, and AC126175.1 were identified by multivariable Cox regression analyses of independent risk factors for deceased status. We built a risk score model based on the three-lncRNA signature using coefficient of multivariable Cox regression and expression value of the three lncRNAs. The high-risk signature score was significantly associated with decreased OS in both the TCGA cohort and the FUSCC cohort. The high-risk group had worse overall survival than the low-risk group in TCGA cohort. To further validate the robustness of three-lncRNA signature risk score model developed in the TCGA dataset, the performance of risk score also evaluated in our institute FUSCC cohort. Additionally, the signature score showed a positive correlation with aggressive outcomes of HNSCC, such as III/IV stage, TP53 mutation, and PI3KCA mutation. The gene set enrichment analysis indicates that the risk score is associated with cancer metastasis-related pathways. Several cancer-related pathways, such as epithelial mesenchymal transition, TNFα signaling via NF-κB, MYC targets, and angiogenesis. The three-lncRNA signature could provide a novel prediction insight into the prognosis of HNSCC patients. The three-lncRNA signature was identified as a predictor of poor prognoses in HNSCC, which may serve as a potential therapeutic target.

MP50-16 PROGNOSTIC VALUE OF EPITHELIAL-MESENCHYMAL TRANSITION MARKERS IN CLEAR CELL RENAL CELL CARCINOM

You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging/Surveillance I (MP50)1 Apr 2020MP50-16 PROGNOSTIC VALUE OF EPITHELIAL-MESENCHYMAL TRANSITION MARKERS IN CLEAR CELL RENAL CELL CARCINOM Hua Xu* and Ding-wei Ye, Shanghai Hua Xu*Hua Xu* More articles by this author and Ding-wei Ye, ShanghaiDing-wei Ye, Shanghai More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000912.016AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Epithelial-to-mesenchymal transition (EMT) plays a crucial role in tumor invasiveness and metastasis. We aimed to determine prognostic value of six key EMT markers (CDH1, CDH2, SNAI1, SNAI2, VIM, TWIST1) in clear cell renal cell carcinoma (ccRCC). METHODS: A total of 533 ccRCC patients with RNASeq data from The Cancer Genome Atlas (TCGA) cohort were included for analysis. Gene expression of these EMT markers was compared between tumor and normal tissues based on Oncomine database and TCGA cohort. Their correlations with progression-free survival (PFS) and overall survival (OS) were also examined in both TCGA cohort and FUSCC (Fudan University Shanghai Cancer Center) cohort. Cox proportional hazards regression model and Kaplan-Meier plot were used to assess the relative factors. Functional enrichment analyses were utilized to describe biologic function annotations and significantly involved hallmarks pathways of each gene. RESULTS: We found that Epithelial marker, CDH1 expression was lower, while mesenchymal markers (CDH2, SNAI1, VIM, TWIST1) expression was higher in ccRCC primary tumors. In the TCGA cohort, we found that patients with higher expression of VIMTWIST1 or lower expression of CDH1 had worse prognosis. Further, in the FUSCC cohort, we confirmed the predictive ability of mesenchymal markers and epithelial marker expression in PFS and OS of ccRCC patients. After generating Cox regression models, EMT markers (CDH1, SNAI1, VIM, and TWIST1) were independent prognostic factors of both PFS and OS in ccRCC patients. CONCLUSIONS: Our preliminary EMT prediction model can facilitate further screening of EMT biomarkers and cast a better understanding of EMT gene function in ccRCC. Source of Funding: NA © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e759-e760 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Hua Xu* More articles by this author Ding-wei Ye, Shanghai More articles by this author Expand All Advertisement PDF downloadLoading ...

Prognostic value of epithelial-mesenchymal transition markers in clear cell renal cell carcinoma.

739 Background: Epithelial-to-mesenchymal transition (EMT) in important in tumor invasiveness and metastasis. We aimed to determine prognostic value of six key EMT markers (CDH1, CDH2, SNAI1, SNAI2, VIM, TWIST1) in clear cell renal cell carcinoma (ccRCC). Methods: A total of 533 ccRCC patients with RNASeq data from The Cancer Genome Atlas (TCGA) cohort were included for analysis. Gene expression of these EMT markers was compared between tumor and normal tissues based on Oncomine database and TCGA cohort. Their correlations with progression-free survival (PFS) and overall survival (OS) were also examined in both TCGA cohort and FUSCC (Fudan University Shanghai Cancer Center) cohort. Cox proportional hazards regression model and Kaplan-Meier plot were used to assess the relative factors. Functional enrichment analyses were utilized to describe biologic function annotations and significantly involved hallmarks pathways of each gene. Results: We found that Epithelial marker, CDH1 expression was lower, while mesenchymal markers (CDH2, SNAI1, VIM, TWIST1) expression was higher in ccRCC primary tumors. In the TCGA cohort, we found that patients with higher expression of VIM, TWIST1 or lower expression of CDH1 had worse prognosis. Further, in the FUSCC cohort, we confirmed the predictive ability of mesenchymal markers and epithelial marker expression in PFS and OS of ccRCC patients. After generating Cox regression models, EMT markers (CDH1, SNAI1, VIM, and TWIST1) were independent prognostic factors of both PFS and OS in ccRCC patients. Conclusions: Our preliminary EMT prediction model can facilitate further screening of EMT biomarkers and cast a better understanding of EMT gene function in ccRCC.

Prognostic value of epithelial-mesenchymal transition markers in clear cell renal cell carcinoma.

Epithelial-to-mesenchymal transition (EMT) is important in tumor invasiveness and metastasis. We aimed to determine prognostic value of six key EMT markers (CDH1, CDH2, SNAI1, SNAI2, VIM, TWIST1) in clear cell renal cell carcinoma (ccRCC). A total of 533 ccRCC patients with RNASeq data from The Cancer Genome Atlas (TCGA) cohort were included for analysis. Gene expression of these EMT markers was compared between tumor and normal tissues based on Oncomine database and TCGA cohort. Their correlations with progression-free survival (PFS) and overall survival (OS) were also examined in both TCGA cohort and FUSCC (Fudan University Shanghai Cancer Center) cohort. Cox proportional hazards regression model and Kaplan-Meier plot were used to assess the relative factors. Functional enrichment analyses were utilized to describe biologic function annotations and significantly involved hallmarks pathways of each gene. We found that Epithelial marker, CDH1 expression was lower, while mesenchymal markers (CDH2, SNAI1, VIM, TWIST1) expression was higher in ccRCC primary tumors. In the TCGA cohort, we found that patients with higher expression of VIM, TWIST1 or lower expression of CDH1 had worse prognosis. Further, in the FUSCC cohort, we confirmed the predictive ability of mesenchymal markers and epithelial marker expression in PFS and OS of ccRCC patients. After generating Cox regression models, EMT markers (CDH1, SNAI1, VIM, and TWIST1) were independent prognostic factors of both PFS and OS in ccRCC patients. Our preliminary EMT prediction model can facilitate further screening of EMT biomarkers and cast a better understanding of EMT gene function in ccRCC.

The unique prognostic characteristics of tumor deposits in colorectal cancer patients.

Both AJCC 7th and 8th TNM systems have included tumor deposits (TDs) in nodal staging when lymph nodes metastases (LNMs) are negative in colorectal cancer (CRC). However, the prognostic role of TDs has not been determined in the presence of positive LNMs. Two independent large-scale cohorts of CRC patients from the Surveillance Epidemiology and End Results (SEER) database (n=69,178) [2010-2013] and Fudan University Shanghai Cancer Center (FUSCC) (n=3,137) [2010-2014] were retrospectively analyzed. Kaplan-Meier method was used to estimate survival curves and univariate and multivariate analyses were performed by Cox proportional hazard model. TDs were observed in 12.3% (n=8,480) and 14.8% (n=463) of patients in the SEER and FUSCC cohorts, respectively. Multivariate analysis suggested TDs were an independent adverse prognostic factors for overall survival (OS) (P<0.001). Remarkably, both cohorts showed the presence of TDs was significantly associated with OS, but not was the number of TDs (P=0.982 and P=0.252 for the SEER and FUSCC cohorts, respectively). In the presence of LNMs, positive TDs were associated with a shorter OS [hazard ratio (HR): 2.69, 95.0% confidence interval (CI): 2.597-2.778; P<0.001]. Further analysis combining TDs with LNMs demonstrated that the prognosis of patients with N1TD (N1 with positive TDs) was same as the N2 patients, and N2TD (N2 with positive TDs) patients had much worse prognosis than N2 (P<0.001). Our results have shown the unique features of TDs in patients with CRC, different from LNMs. In the presence of LNMs, TDs should also be considered in TMN system.

Prognostic implications of Aquaporin 9 expression in clear cell renal cell carcinoma

BackgroundGrowing evidence has demonstrated immune reactivity as a confirmed important carcinogenesis and therapy efficacy for clear cell renal cell carcinoma (ccRCC). Aquaporin 9 (AQP9) is involved in many immune-related signals; however, its role in ccRCC remains to be elucidated. This study investigated AQP9 expression in tumor tissues and defined the prognostic value in ccRCC patients.MethodsA total of 913 ccRCC patients with available RNA-sequence data from the Cancer Genome Atlas (TCGA) database and Fudan University Shanghai Cancer Center (FUSCC) were consecutively recruited in analyses. Differential transcriptional and proteome expression profiles were obtained and validated using multiple datasets. A partial likelihood test from Cox regression analysis was developed to address the influence of independent factors on progression-free survival (PFS) and overall survival (OS). The Kaplan–Meier method and log-rank test were performed to assess survival. Receiver operating characteristic (ROC) curves were used to describe binary classifier value of AQP9 using area under the curve (AUC) score. Functional enrichment analyses and immune infiltration analysis were used to describe significantly involved hallmark pathways of hub genes.ResultsSignificantly elevated transcriptional and proteomic AQP9 expressions were found in ccRCC samples. Increased AQP9 mRNA expression was significantly associated with advanced clinicopathological parameters and correlated with shorter PFS and OS in TCGA and FUSCC cohorts (p < 0.001). ROC curves suggested the significant diagnostic and prognostic ability of AQP9 (PFS, AUC = 0.823; OS, AUC = 0.828). Functional annotations indicated that AQP9 is involved in the most significant hallmarks including complement, coagulation, IL6/JAK–STAT3, inflammatory response and TNF-alpha signaling pathways.ConclusionOur study revealed that elevated AQP9 expression was significantly correlated with aggressive progression, poor survival and immune infiltrations in ccRCC patients, and we validated its prognostic value in a real-world cohort. These data suggest that AQP9 may act as an oncogene and a promising prognostic marker in ccRCC.

Decreased SPTLC1 expression predicts worse outcomes in ccRCC patients

Serine palmitoyltransferase, long chain base subunit 1 (SPTLC1) catalyzes the first step in sphingolipid synthesis and has been implicated in the progression of various cancers. However, its role in clear cell renal cell carcinoma (ccRCC) remains unclear. Here, we investigated the expression and prognostic value of SPTLC1 in ccRCC. Three ccRCC patient cohorts were studied. ccRCC and adjacent normal kidney tissue samples were obtained from 183 patients at the Fudan University Shanghai Cancer Center (FUSCC) and subjected to immunohistochemical staining and quantitative reverse-transcription polymerase chain reaction to evaluate SPTLC1 protein and messenger RNA (mRNA) expression. Two validation cohorts consisting of mRNA and clinicopathological data sets from patients with ccRCC were obtained from the Cancer Genome Atlas (TCGA, n = 429) and Oncomine (n = 178) databases. Associations between low and high SPTLC1 mRNA and protein expression and survival were evaluated using the Kaplan-Meier method and log-rank test. Independent prognostic factors were identified using univariate and multivariate Cox regression analysis. SPTLC1 mRNA or protein were expressed at significantly lower levels in ccRCC tissues compared with normal kidney tissues in all three patient cohorts (P < .001). Low SPTLC1 expression was significantly associated with shorter overall survival in the FUSCC (P = .041) and Oncomine (P < .001) cohorts, and was significantly associated with shorter overall survival (P < .0001) and progression-free survival (P < .001) in the TCGA cohort. Bioinformatics analysis identified 10 genes significantly coregulated with SPTLC1 in ccRCC, most of which contributed to sphingomyelin metabolism (SPTLC2, SPTLC3, SPTSSA, SPTSSB, ORMDL1, ORMDL2, ORMDL3, ZDHHC9, GOLGA7B, and KDSR). Functional enrichment analysis predicted that SPTLC1 and its network play significant roles in inflammatory, hypoxia, and interferon gamma responses, and in allograft rejection pathways. Low SPTLC1 expression is significantly associated with disease progression and poor survival in patients with ccRCC, suggesting that SPTLC1 may function as a tumor suppressor. Thus, SPTLC1 could be a potential new biomarker and/or therapeutic target for ccRCC.

Development and Validation of an Autophagy Score Signature for the Prediction of Post-operative Survival in Colorectal Cancer.

Background: Survival rates for Colorectal cancer (CRC) patients who experienced early relapse have usually been relatively low. Our study aims at developing an autophagy signature that could help to detect early relapse cases in CRC.Methods: Propensity score matching analysis was carried out between patients from the early relapse group and the long-term survival group from GSE39582. For both groups, respectively, global autophagy expression changes were then analyzed to identify the differentially expressed prognostic autophagy related genes by conducting Linear Models for Microarray data method analysis. Then, the multi-gene signature was validated in TCGA and Fudan University Shanghai Cancer Center (FUSCC) cohorts. Time-dependent ROC were used to test the efficiency of this signature feature in predicting the prognosis of CRC patients.Results: 5 autophagy genes were finally identified to build an early relapse classifier. With specific risk score formula, patients were classified into low- or high-risk group. Time-dependent ROC analyses proved its prognostic accuracy, with AUC 0.841 and 0.803 at 1 and 3 years, respectively. Then, we validated its prognostic value in two external validation series (GSE17538 and GSE33113) and proved that the result is indeed significant irrespective of datasets in two external independent validation cohorts (TCGA and FUSCC cohorts). A nomogram was constructed to guide individualized treatment of patients with CRC.Conclusions: The identification of robust autophagy-related features can effectively classify CRC patients into groups with low and high risk of early relapse. This signature may be used to help select high-risk CRC patients who require more aggressive treatment interventions.

Development and External Validation of a Novel 12-Gene Signature for Prediction of Overall Survival in Muscle-Invasive Bladder Cancer.

Purpose: We aimed to develop and validate a novel gene signature from published data and improve the prediction of survival in muscle-invasive bladder cancer (MIBC).Methods: We searched the published gene signatures associated with the overall survival (OS) of MIBC and compiled all 274 genes to develop a novel gene signature. RNAseq data of TCGA (the Cancer Genome Atlas) bladder cohort were downloaded. All genes were included in a univariate Cox hazard ratio model. We then used a reduced multivariate Cox regression model, which included only genes achieving P < 0.05 in the univariate model. A total of 172 patients at Fudan University Shanghai Cancer Center (FUSCC) and 61 patients from GEO datasets were used as an external validation set.Results: A total of 327 patients in the TCGA cohort were enrolled. We identified 274 genes from eight published papers on the OS of MIBC. Using the TCGA database, we identified 12 genes that correlated with OS (P < 0.05 in both univariate and multivariate analyses). By integrating these genes with the RT-qPCR data in our validation dataset and GEO datasets, we confirmed that the power for predicting OS of the 12-gene panel (AUC of 0.741 and 0.727, respectively) was higher than just clinical data (including gender, age, T stage, grade, and N stage) alone in the TCGA and FUSCC cohort (AUC of 0.667 and 0.631, respectively). Additionally, upon combining the clinical data and 12-gene panel together, the AUC increased to 0.768, 0.757, and 0.88 in the TCGA, FUSCC and GSE13507 cohorts, respectively.Conclusions: Applying published gene signatures and TCGA data, we successfully built and externally validated a novel 12-gene signature for the survival of MIBC.Brief ExplanationWe systemically reviewed all published prognostic gene signatures of muscle-invasive bladder cancer (MIBC) and integrated the genes in the TCGA MIBC cohort. This new gene panel was validated in a newly established MIBC cohort in GEO and FUSCC. This method can help update the previous established panels in a new way.

Causes of Death and Conditional Survival of Renal Cell Carcinoma.

Background: As conditional survival could provide more relevant prognostic information at each follow-up time, the present study aimed to assess conditional overall survival (COS) based on two cohorts and assess the risks of death due to renal cell carcinoma (RCC) vs. other causes.Methods: The Fudan University Shanghai Cancer Center (FUSCC) and Surveillance, Epidemiology, and End Results (SEER) database were used as the source of data for our analysis. COS and cancer-specific survival were evaluated using the Kaplan–Meier method.Results: A total of 90,927 patients (SEER cohort = 88,807, FUSCC cohort = 2,120) were enrolled. Our results suggest that hazards of other causes-related death were always higher than that of cancer-specific death in low-risk RCC patients, but lower in metastatic RCC patients. It exceeded that of cancer-specific death by 8 years in high-risk RCC patients. Only in metastatic RCC patients, the COS improved markedly with survivorship increasing. After surviving 1, 2, 3, 4, and 5 years, the 5 years COS increased by +10, +18, +23, +29, and 35% (the observed 5 years OS: 12%), respectively.Conclusions: COS can better help patients with metastatic RCC rather than other RCC patients. Additionally, COS brings optimism for metastatic RCC patients with expected poorer prognosis psychologically.

Proposed Modification of the 8th Edition of the AJCC Staging System for Pancreatic Ductal Adenocarcinoma.

The aim of this study was to improve the 8th edition (8th) of the American Joint Committee on Cancer (AJCC) staging system for pancreatic ductal adenocarcinoma (PDAC). The new 8th AJCC staging system for PDAC was released in October, 2016, and will be applied in clinical practice in 2018. Two large cohorts were included in this analysis. One consisted of 45,856 PDAC patients in the Surveillance, Epidemiology, and End Results (SEER) database (2004-2014), and the other consisted of 3166 PDAC patients in the Fudan University Shanghai Cancer Center (FUSCC) database (2005-2015). Using the 8th AJCC staging system, the median overall survival of the patients in the same stage varied widely among the different substages. We proposed a modified staging system based on median OS in which we maintained the T, N, and M definitions, but regrouped the substages. In the SEER cohort, the concordance index was higher for local disease with the modified staging system [0.637; 95% confidence interval (CI) 0.631-0.642] than with the 8th AJCC staging system (0.620, 95% CI 0.615-0.626). Similar findings were also observed in the FUSCC cohort. In addition, we verified the reliability of the modified staging system in an analysis of patients with different examined lymph node counts (≥15 or 1-14). The modified 8th AJCC staging system for PDAC proposed in this study provides improvements and may be evaluated for potential adoption in the next edition.