Nomograms for prognosis prediction in esophageal adenocarcinoma: realities and challenges.

Prognostic assessment is of great significance for individualized treatment and care of cancer patients. Although the TNM staging system is widely used as the primary prognostic classifier for solid tumors in clinical practice, the complexity of tumor occurrence and development requires more personalized probability prediction models than an ordered staging system. By integrating clinical, pathological, and molecular factors into digital models through LASSO and Cox regression, a nomogram could provide more accurate personalized survival estimates, helping clinicians and patients develop more appropriate treatment and care plans. Esophageal adenocarcinoma (EAC) is a common pathological subtype of esophageal cancer with poor prognosis. Here, we screened and comprehensively reviewed the studies on EAC nomograms for prognostic prediction, focusing on performance evaluation and potential prognostic factors affecting survival. By analyzing the strengths and limitations of the existing nomograms, this study aims to provide assistance in constructing high-quality prognostic models for EAC patients.

A Comprehensive Prognostic Analysis of Tumor-Related Blood Group Antigens in Pan-Cancers Suggests That SEMA7A as a Novel Biomarker in Kidney Renal Clear Cell Carcinoma.

Blood group antigen is a class of heritable antigenic substances present on the erythrocyte membrane. However, the role of blood group antigens in cancer prognosis is still largely unclear. In this study, we investigated the expression of 33 blood group antigen genes and their association with the prognosis of 30 types of cancers in 31,870 tumor tissue samples. Our results revealed that blood group antigens are abnormally expressed in a variety of cancers. The high expression of these antigen genes was mainly related to the activation of the epithelial-mesenchymal transition (EMT) pathway. High expression of seven antigen genes, i.e., FUT7, AQP1, P1, C4A, AQP3, KEL and DARC, were significantly associated with good OS (Overall Survival) in six types of cancers, while ten genes, i.e., AQP1, P1, C4A, AQP3, BSG, CD44, CD151, LU, FUT2, and SEMA7A, were associated with poor OS in three types of cancers. Kidney renal clear cell carcinoma (KIRC) is associated with the largest number (14 genes) of prognostic antigen genes, i.e., CD44, CD151, SEMA7A, FUT7, CR1, AQP1, GYPA, FUT3, FUT6, FUT1, SLC14A1, ERMAP, C4A, and B3GALT3. High expression of SEMA7A gene was significantly correlated with a poor prognosis of KIRC in this analysis but has not been reported previously. SEMA7A might be a putative biomarker for poor prognosis in KIRC. In conclusion, our analysis indicates that blood group antigens may play functional important roles in tumorigenesis, progression, and especially prognosis. These results provide data to support prognostic marker development and future clinical management.

Nomograms for prognostic risk assessment in glioblastoma multiforme: Applications and limitations.

Glioblastoma multiforme (GBM) is the most common and aggressive form of brain cancer. Prognosis evaluation is of great significance in guiding individualized treatment and monitoring of GBM. By integrating different prognostic variables, nomograms simplify the statistical risk prediction model into numerical estimates for death or recurrence, and are hence widely applied in prognosis prediction. In the past two decades, the application of high-throughput profiling technology and the establishment of TCGA database and other public data deposits have provided opportunities to identify cancer-related molecules and prognostic biomarkers. As a result, both molecular features and clinical characteristics of cancer have been reported to be the key factors in nomogram model construction. This article comprehensively reviewed 35 studies of GBM nomograms, analyzed the present situation of GBM nomograms, and discussed the role and significance of nomograms in personalized risk assessment and clinical treatment decision-making. To facilitate the application of nomograms in the prognostic prediction of GBM patients, a website has been established for the online access of nomograms based on the studies of this review, which is called Consensus Nomogram Spectrum for Glioblastoma (CNSgbm) and is accessible through https://bioinfo.henu.edu.cn/nom/NomList.jsp.

OShnscc: a novel user-friendly online survival analysis tool for head and neck squamous cell carcinoma based on RNA expression profiles and long-term survival information.

Head and neck squamous cell carcinoma (HNSCC), as the most common type (>90%) of head and neck cancer, includes various epithelial malignancies that arise in the nasal cavity, oral cavity, pharynx, and larynx. In 2020, approximately 878 | 000 new cases and 444 000 deaths linked to HNSCC occurred worldwide (Sung et al., 2021). Due to the associated frequent recurrence and metastasis, HNSCC patients have poor prognosis with a five-year survival rate of 40%-50% (Jou and Hess, 2017). Therefore, novel prognostic biomarkers need to be developed to identify high-risk HNSCC patients and improve their disease outcomes.

Identification and Validation of an Annexin-Related Prognostic Signature and Therapeutic Targets for Bladder Cancer: Integrative Analysis.

Abnormal expression and dysfunction of Annexins (ANXA1-11, 13) have been widely found in several types of cancer. However, the expression pattern and prognostic value of Annexins in bladder cancer (BC) are currently still unknown. In this study, survival analysis by our in-house OSblca web server revealed that high ANXA1/2/3/5/6 expression was significantly associated with poor overall survival (OS) in BC patients, while higher ANXA11 was associated with increased OS. Through Oncomine and GEPIA2 database analysis, we found that ANXA2/3/4/13 were up-regulated, whereas ANXA1/5/6 were down-regulated in BC compared with normal bladder tissues. Further LASSO analysis built an Annexin-Related Prognostic Signature (ARPS, including four members ANXA1/5/6/10) in the TCGA BC cohort and validated it in three independent GEO BC cohorts (GSE31684, GSE32548, GSE48075). Multivariate COX analysis demonstrated that ARPS is an independent prognostic signature for BC. Moreover, GSEA results showed that immune-related pathways, such as epithelial-mesenchymal transition and IL6/JAK/STAT3 signaling were enriched in the high ARPS risk groups, while the low ARPS risk group mainly regulated metabolism-related processes, such as adipogenesis and bile acid metabolism. In conclusion, our study comprehensively analyzed the mRNA expression and prognosis of Annexin family members in BC, constructed an Annexin-related prognostic signature using LASSO and COX regression, and validated it in four independent BC cohorts, which might help to improve clinical outcomes of BC patients, offer insights into the underlying molecular mechanisms of BC development and suggest potential therapeutic targets for BC.

Identification of Distinct Molecular Subtypes of Endometrioid Adenocarcinoma.

Endometrial carcinoma (EC) is one of the most common gynecological cancers worldwide. Endometrioid adenocarcinoma (EAC) is the major form of EC, accounting for 75-80% of cases. Currently, there is no molecular classification system for EAC, so there are no corresponding targeted treatments. In this study, we identified two distinct molecular subtypes of EAC with different gene expression patterns and clinicopathologic characteristics. Subtype I EAC cases, accounting for the majority of cases (56%), were associated with an earlier stage, a more well-differentiated grade, a lower tumor invasion rate, and a more favorable prognosis, and the median tumor necrosis percent (15%) was also significantly higher in subtype I EAC. In contrast, subtype II EAC represents high-grade EAC, with a higher tumor invasion rate and tumor weight. The up-regulated genes in subtype I EAC were associated with the immune response, defense response, cell motion, and cell motility pathway, whereas the up-regulated genes in subtype II EAC were associated with the cell cycle, DNA replication, and RNA processing pathways. Additionally, we identified three potential subtype-specific biomarkers, comprising MDM2 (MDM2 proto-oncogene) for subtype I, and MSH2 (mutS homolog 2) and MSH6 (mutS homolog 6) for subtype II.

OSchol: an online consensus survival web server for cholangiocarcinoma prognosis analysis.

BACKGROUND: As the most common biliary ducts, cholangiocarcinoma (CHOL) is an aggressive malignancy with complex pathological context, high mortality and relapse rate. The current therapy of CHOL is mainly performed with surgery followed by chemoradiotherapy. Due to the high metastasis and relapse rate of CHOL, the prognosis of CHOL is still poor, and the molecular prognostic system is to be constructed. METHODS: In this study, we have established an online prognostic analysis web server named OSchol to evaluate the correlation between candidate genes and survival for CHOL. RESULTS: The prognostic values of previous published biomarkers in OSchol, including ITIH4, PTEN and DACH1, have been validated by OSchol. In addition, we have identified novel potential prognostic biomarker for CHOL using OSchol, that E2F1 has significant prognostic ability in OSchol (both TCGA and GSE107943 cohorts). CONCLUSION: Our study provides a platform for researchers and clinicians to screen, develop and validate their genes of interest to be potential prognostic biomarkers for CHOL and may also help guide the targeted therapies for CHOL.

Systematic Analysis of microRNA Biomarkers for Diagnosis, Prognosis, and Therapy in Patients With Clear Cell Renal Cell Carcinoma.

The ever-increasing morbidity and mortality of clear cell renal cell carcinoma (ccRCC) urgently demands updated biomarkers. MicroRNAs (miRNAs) are involved in diverse biological processes such as cell proliferation, differentiation, apoptosis by regulating their target genes' expression. In kidney cancers, miRNAs have been reported to be involved in tumorigenesis and to be the diagnostic, prognostic, and therapeutic response biomarkers. Here, we performed a systematic analysis for ccRCC-related miRNAs as biomarkers by searching keywords in the NCBI PubMed database and found 118 miRNAs as diagnostic biomarkers, 28 miRNAs as prognostic biomarkers, and 80 miRNAs as therapeutic biomarkers in ccRCC. miRNA-21, miRNA-155, miRNA-141, miRNA-126, and miRNA-221, as significantly differentially expressed miRNAs between cancer and normal tissues, play extensive roles in the cell proliferation, differentiation, apoptosis of ccRCC. GO and KEGG enrichment analysis of these miRNAs' target genes through Metascape showed these target genes are enriched in Protein Domain Specific Binding (GO:0019904). In this paper, we identified highly specific miRNAs in the pathogenesis of ccRCC and explored their potential applications for diagnosis, prognosis, and treatment of ccRCC.

OSucs: An Online Prognostic Biomarker Analysis Tool for Uterine Carcinosarcoma.

BACKGROUND: Uterine carcinosarcoma (UCS) is a type of rare and aggressive tumor. The standard treatment for UCS involves surgical treatment followed by radiochemotherapy. Clinical outcomes of UCS patients are poor due to high metastasis and relapse rate. Therefore, new targeted therapy strategies for UCS are needed. Because UCS is highly heterogenous, it is critical to identify and develop prognostic biomarkers to distinguish molecular subtypes of UCS for better treatment guidance. METHODS: Using gene expression profiles and clinical follow-up data, we developed an online consensus survival analysis tool named OSucs. This web tool allows researchers to conveniently analyze the prognostic abilities of candidate genes in UCS. RESULTS: To test the reliability of this server, we analyzed five previously reported prognostic biomarkers, all of which showed significant prognostic impacts. In addition, ETV4 (ETS variant transcription factor 4), ANGPTL4 (Angiopoietin-like protein 4), HIST1H1C (Histone cluster 1 H1 family member c) and CTSV (Cathepsin V) showed prognostic potential in a molecular subtype-specific manner. CONCLUSION: We built a platform for researchers to analyze if genes have prognostic potentials in UCS.

OSlgg: An Online Prognostic Biomarker Analysis Tool for Low-Grade Glioma.

Glioma is the most frequent primary brain tumor that causes high mortality and morbidity with poor prognosis. There are four grades of gliomas, I to IV, among which grade II and III are low-grade glioma (LGG). Although less aggressive, LGG almost universally progresses to high-grade glioma and eventual causes death if lacking of intervention. Current LGG treatment mainly depends on surgical resection followed by radiotherapy and chemotherapy, but the survival rates of LGG patients are low. Therefore, it is necessary to use prognostic biomarkers to classify patients into subgroups with different risks and guide clinical managements. Using gene expression profiling and long-term follow-up data, we established an Online consensus Survival analysis tool for LGG named OSlgg. OSlgg is comprised of 720 LGG cases from two independent cohorts. To evaluate the prognostic potency of genes, OSlgg employs the Kaplan-Meier plot with hazard ratio and p value to assess the prognostic significance of genes of interest. The reliability of OSlgg was verified by analyzing 86 previously published prognostic biomarkers of LGG. Using OSlgg, we discovered two novel potential prognostic biomarkers (CD302 and FABP5) of LGG, and patients with the elevated expression of either CD302 or FABP5 present the unfavorable survival outcome. These two genes may be novel risk predictors for LGG patients after further validation. OSlgg is public and free to the users at http://bioinfo.henu.edu.cn/LGG/LGGList.jsp.

OSlihc: An Online Prognostic Biomarker Analysis Tool for Hepatocellular Carcinoma.

Liver hepatocellular carcinoma (LIHC) is one of the most common malignant tumors in the world with an increasing number of fatalities. Identification of novel prognosis biomarker for LIHC may improve treatment and therefore patient outcomes. The availability of public gene expression profiling data offers the opportunity to discover prognosis biomarkers for LIHC. We developed an online consensus survival analysis tool named OSlihc using gene expression profiling and long-term follow-up data to identify new prognosis biomarkers. OSlihc consists of 637 cases from four independent cohorts. As a risk assessment tool, OSlihc generates the Kaplan-Meier survival plot with hazard ratio (HR) and p value to evaluate the prognostic value of a gene of interest. To test the reliability of OSlihc, we analyzed 65 previous reported prognostic biomarkers in OSlihc and showed that all of which have significant prognostic values. Furthermore, we identified four novel potential prognostic biomarkers (ATG9A, WIPI1, CXCL1, and CSNK2A2) for LIHC, the elevated expression of which predict the unfavorable survival outcomes. These genes (ATG9A, WIPI1, CXCL1, and CSNK2A2) may be potentially new biomarkers to identify at-risk LIHC patients when further validated. By OSlihc, users can evaluate the prognostic abilities of genes of their interest, which provides a platform for researchers to identify prognostic biomarkers to further develop targeted therapy strategies for LIHC patients. OSlihc is public and free to the users at http://bioinfo.henu.edu.cn/LIHC/LIHCList.jsp.

OSskcm: an online survival analysis webserver for skin cutaneous melanoma based on 1085 transcriptomic profiles.

BACKGROUND: Cutaneous melanoma is one of the most aggressive and lethal skin cancers. It is greatly important to identify prognostic biomarkers to guide the clinical management. However, it is technically challenging for untrained researchers to process high dimensional profiling data and identify potential prognostic genes in profiling datasets. METHODS: In this study, we developed a webserver to analyze the prognostic values of genes in cutaneous melanoma using data from TCGA and GEO databases. The webserver is named Online consensus Survival webserver for Skin Cutaneous Melanoma (OSskcm) which includes 1085 clinical melanoma samples. The OSskcm is hosted in a windows tomcat server. Server-side scripts were developed in Java script. The database system is managed by a SQL Server, which integrates gene expression data and clinical data. The Kaplan-Meier (KM) survival curves, Hazard ratio (HR) and 95% confidence interval (95%CI) were calculated in a univariate Cox regression analysis. RESULTS: In OSskcm, by inputting official gene symbol and selecting proper options, users could obtain KM survival plot with log-rank P value and HR on the output web page. In addition, clinical characters including race, stage, gender, age and type of therapy could also be included in the prognosis analysis as confounding factors to constrain the analysis in a subgroup of melanoma patients. CONCLUSION: The OSskcm is highly valuable for biologists and clinicians to perform the assessment and validation of new or interested prognostic biomarkers for melanoma. OSskcm can be accessed online at: http://bioinfo.henu.edu.cn/Melanoma/MelanomaList.jsp.

OSeac: An Online Survival Analysis Tool for Esophageal Adenocarcinoma.

Esophageal Adenocarcinoma (EAC) is one of the most common gastrointestinal tumors in the world. However, molecular prognostic systems are still lacking for EAC. Hence, we developed an Online consensus Survival analysis web server for Esophageal Adenocarcinoma (OSeac), to centralize published gene expression data and clinical follow up data of EAC patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). OSeac includes 198 EAC cases with gene expression profiling and relevant clinical long-term follow-up data, and employs the Kaplan Meier (KM) survival plot with hazard ratio (HR) and log rank test to estimate the prognostic potency of genes of interests for EAC patients. Moreover, we have determined the reliability of OSeac by using previously reported prognostic biomarkers such as DKK3, CTO1, and TXNIP. OSeac is free and publicly accessible at http://bioinfo.henu.edu.cn/EAC/EACList.jsp.

Comprehensive Review of Web Servers and Bioinformatics Tools for Cancer Prognosis Analysis.

Prognostic biomarkers are of great significance to predict the outcome of patients with cancer, to guide the clinical treatments, to elucidate tumorigenesis mechanisms, and offer the opportunity of identifying therapeutic targets. To screen and develop prognostic biomarkers, high throughput profiling methods including gene microarray and next-generation sequencing have been widely applied and shown great success. However, due to the lack of independent validation, only very few prognostic biomarkers have been applied for clinical practice. In order to cross-validate the reliability of potential prognostic biomarkers, some groups have collected the omics datasets (i.e., epigenetics/transcriptome/proteome) with relative follow-up data (such as OS/DSS/PFS) of clinical samples from different cohorts, and developed the easy-to-use online bioinformatics tools and web servers to assist the biomarker screening and validation. These tools and web servers provide great convenience for the development of prognostic biomarkers, for the study of molecular mechanisms of tumorigenesis and progression, and even for the discovery of important therapeutic targets. Aim to help researchers to get a quick learning and understand the function of these tools, the current review delves into the introduction of the usage, characteristics and algorithms of tools, and web servers, such as LOGpc, KM plotter, GEPIA, TCPA, OncoLnc, PrognoScan, MethSurv, SurvExpress, UALCAN, etc., and further help researchers to select more suitable tools for their own research. In addition, all the tools introduced in this review can be reached at http://bioinfo.henu.edu.cn/WebServiceList.html.

High TXLNA Expression Predicts Favourable Outcome for Pancreatic Adenocarcinoma Patients.

TXLNA (taxilin alpha), a binding partner of the syntaxin family, was identified as a key factor in the coordination of intracellular vesicle trafficking and highly expressed in various tumor cells. However, the accurate relation between TXLNA and tumorigenesis and progression of pancreatic adenocarcinoma (PAAD) is still unclear. The present study was designed to examine the expression profile of TXLNA and explore its prognostic significance in PAAD patients and the possible molecular regulatory mechanism by analyzing a series of data from databases, including GEPIA, LOGpc, STRING, and GeneMANIA. The results indicate that TXLNA mRNA and protein were remarkably increased in PAAD tissues compared with normal pancreatic tissues. The high TXLNA expression was significantly correlated with superior overall survival (OS), disease-free interval (DFI), disease specific survival (DSS), and progression-free interval (PFI) for PAAD patients. In summary, high TXLNA expression could predict favourable OS, DFI, DSS, and PFI for PAAD patients, and it might be as potential prognostic biomarkers and targets for PAAD.

OSdlbcl: An online consensus survival analysis web server based on gene expression profiles of diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL) and is a clinical, pathological, and molecular heterogeneous disease with highly variable clinical outcomes. Currently, valid prognostic biomarkers in DLBCL are still lacking. To optimize targeted therapy and improve the prognosis of DLBCL, the performance of proposed biomarkers needs to be evaluated in multiple cohorts, and new biomarkers need to be investigated in large datasets. Here, we developed a consensus Online Survival analysis web server for Diffuse Large B-Cell Lymphoma, abbreviated OSdlbcl, to assess the prognostic value of individual gene. To build OSdlbcl, we collected 1100 samples with gene expression profiles and clinical follow-up information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. In addition, DNA mutation data were also collected from the TCGA database. Overall survival (OS), progression-free survival (PFS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI) are important endpoints to reflect the survival rate in OSdlbcl. Moreover, clinical features were integrated into OSdlbcl to allow data stratifications according to the user's special needs. By inputting an official gene symbol and selecting desired criteria, the survival analysis results can be graphically presented by the Kaplan-Meier (KM) plot with hazard ratio (HR) and log-rank p value. As a proof-of-concept demonstration, the prognostic value of 23 previously reported survival associated biomarkers, such as transcription factors FOXP1 and BCL2, was evaluated in OSdlbcl and found to be significantly associated with survival as reported (HR = 1.73, P < .01; HR = 1.47, P = .03, respectively). In conclusion, OSdlbcl is a new web server that integrates public gene expression, gene mutation data, and clinical follow-up information to provide prognosis evaluations for biomarker development for DLBCL. The OSdlbcl web server is available at https://bioinfo.henu.edu.cn/DLBCL/DLBCLList.jsp.

OSpaad: An online tool to perform survival analysis by integrating gene expression profiling and long-term follow-up data of 1319 pancreatic carcinoma patients.

Pancreatic carcinoma (PC) is a type of highly lethal malignant tumor that has unfavorable outcomes. One major challenge in improving clinical outcomes is to identify novel biomarkers for prognosis. In this study, we developed an online consensus survival tool for pancreatic adenocarcinoma (OSpaad), which allows researchers and clinicians to analyze the prognostic value of selected genes in PC. OSpaad contains 1319 unique PC cases that have both gene expression data and correspondent clinical data from seven individual cohorts and provides four survival terms including overall survival, disease-specific survival, disease-free interval, progression-free interval for prognosis evaluation. To meet the different research needs, OSpaad allows users to limit survival analysis in subgroups by selecting different terms of clinical confounding factors such as TNM stage, sex, smoking time, lymph invasion, and race. Moreover, we showed that 97% (116 out of 120) previously reported prognostic biomarkers, including ERBB2, TP53, EGFR and so forth, were validated and confirmed their prognostic significance in OSpaad, demonstrating the well performance of survival analysis in OSpaad. OSpaad is a user-friendly online tool with a straightforward interface allowing clinicians and basic research scientists with even a limited bioinformatics background to easily screen and evaluate the prognostic value of genes in a large PC cohort. This online tool can be accessed at http://bioinfo.henu.edu.cn/PAAD/PAADList.jsp.

OSuvm: An interactive online consensus survival tool for uveal melanoma prognosis analysis.

Uveal melanoma (UM) is a rare, aggressive, but the most frequent primary intraocular malignancy in adults, and up to 50% of patients develop a tendency of liver metastases. Great efforts have been made to develop biomarkers that facilitate diagnosis, prediction of the risk, and response to treatment of UM. However, a biologically informative and highly accurate gold standard system for prognostic evaluation of UM remains to be established. To facilitate assessment of the prognosis of UM patients, we established a user-friendly Online consensus Survival tool for uveal melanoma, named OSuvm, by which users can easily estimate the prognostic values of genes of interest by the Kaplan-Meier survival plot with hazard ratio and log-rank test. OSuvm comprises four independent cohorts including 229 patients with both gene expression profiles and relevant clinical follow-up information, and it has shown great performance in evaluating the prognostic roles of previously reported biomarkers. Using OSuvm enables researchers and clinicians to rapidly and conveniently explore the prognostic value of genes of interest and develop new potential molecular biomarkers for UM. OSuvm can be accessed at http://bioinfo.henu.edu.cn/UVM/UVMList.jsp.

OSacc: Gene Expression-Based Survival Analysis Web Tool For Adrenocortical Carcinoma.

Gene expression profiling data with long-term clinical follow-up information are great resources to screen, develop, evaluate and validate prognostic biomarkers in translational cancer research. However, an easy-to-use interactive online tool is needed to analyze these profiling and clinical data. In the current work, we developed OSacc (Online consensus Survival analysis of ACC), a web tool that provides rapid and user-friendly survival analysis based on seven independent transcriptomic profiles with long-term clinical follow-up information of 259 ACC patients gathered from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. OSacc allows researchers and clinicians to evaluate the prognostic value of genes of interest by Kaplan-Meier (KM) survival plot with hazard ratio (HR) and log-rank test in ACC. OSacc is freely available at http://bioinfo.henu.edu.cn/ACC/ACCList.jsp.