Prognostic Index Model Research Articles

Prognostic biomarkers for predicting papillary thyroid carcinoma patients at high risk using nine genes of apoptotic pathway.

Aberrant expressions of apoptotic genes have been associated with papillary thyroid carcinoma (PTC) in the past, however, their prognostic role and utility as biomarkers remains poorly understood. In this study, we analysed 505 PTC patients by employing Cox-PH regression techniques, prognostic index models and machine learning methods to elucidate the relationship between overall survival (OS) of PTC patients and 165 apoptosis related genes. It was observed that nine genes (ANXA1, TGFBR3, CLU, PSEN1, TNFRSF12A, GPX4, TIMP3, LEF1, BNIP3L) showed significant association with OS of PTC patients. Five out of nine genes were found to be positively correlated with OS of the patients, while the remaining four genes were negatively correlated. These genes were used for developing risk prediction models, which can be utilized to classify patients with a higher risk of death from the patients which have a good prognosis. Our voting-based model achieved highest performance (HR = 41.59, p = 3.36x10-4, C = 0.84, logrank-p = 3.8x10-8). The performance of voting-based model improved significantly when we used the age of patients with prognostic biomarker genes and achieved HR = 57.04 with p = 10-4 (C = 0.88, logrank-p = 1.44x10-9). We also developed classification models that can classify high risk patients (survival ≤ 6 years) and low risk patients (survival > 6 years). Our best model achieved AUROC of 0.92. Further, the expression pattern of the prognostic genes was verified at mRNA level, which showed their differential expression between normal and PTC samples. Also, the immunostaining results from HPA validated these findings. Since these genes can also be used as potential therapeutic targets in PTC, we also identified potential drug molecules which could modulate their expression profile. The study briefly revealed the key prognostic biomarker genes in the apoptotic pathway whose altered expression is associated with PTC progression and aggressiveness. In addition to this, risk assessment models proposed here can help in efficient management of PTC patients.

Suppression of Extracellular Vesicle CD47 Induces Systemic Anti-DLBCL Immunity

Purpose:Tumor cells evade the immune surveillance by up-regulating surface expression of CD47, which interacts with SIRPa on macrophages to elicit the immune checkpoint response. Anti-CD47 antibody (IBI188) has shown promise in treating tumors, including Diffuse large B cell lymphoma (DLBCL). The objective response rate among DLBCL was 73% (ClinicalTrials.gov number, NCT02953509.). The basis of differential therapeutic success between patients remains unknown. Extracellular vesicles (EVs) carry bioactive molecules that influence the immune system. CD47 has be found on surface of EVs. The purpose of this study is to explore whether EV CD47 contributes to immunosuppression and is associated with anti-CD47 response in DLBCL.Methods: 1.Kaplan-Meier curves and Cox regression models were used to analyze PFS and OS. The area under the ROC curve (AUC) was used to assess the predicted validity of the National Comprehensive Cancer Network-International Prognostic Index (NCCN-IPI) model and the NCCN-IPI+ CD47 model.2.Immunofluorescence experiment was used to detect the infiltration of M1 and M2 subtype macrophages in DLBCL tumor tissue. M1 and M2 macrophages were generated from peripheral blood mononuclear cells obtained from healthy donors. Immunoelectron microscopy, flow cytometry and western blot were performed to detect CD47 on the surface of the EVs derived from DLBCL cells. The activity of macrophage phagocytosis of DLBCL cells was detected by confocal-based phagocytosis assay and flow cytometry-based phagocytosis assay.3.In DLBCL xenografts, the IBI188 antibody was a macrophage immune checkpoint inhibitor blocking CD47 that induces tumor-cell phagocytosis.4.Peripheral blood mononuclear cells from DLBCL patients and healthy donors was collected for EVs purification and subsequent detection of human CD47 proteins by ELISA.Results 1.The elevated expression of CD47 in DLBCL is significantly correlated with poor PFS and OS in a univariate analysis, and is statistically significant after adjusting for the NCCN-IPI in the univariate and multivariate analysis (Figure 1A, B). AUC analysis with cross-validation showed that the combination of the CD47 signature and NCCN-IPI had a better prediction of PFS and OS than without the CD47 signature (Figure 1C). Thus, CD47 in DLBCL can be used as a biomarker of prognosis and developed the prognostic stratification of DLBCL patients.2.EV CD47 derived from DLBCL cells has the same membrane topology as the cell surface CD47, with its extracellular domain exposed on the surface of the EVs (Figure 1D-G). Moreover, EV CD47 could function similarly to cell-surface CD47 in the suppression of macrophage phagocytosis of DLBCL cells (Figure 1H, I). DLBCL cells that had higher levels of cellular CD47 protein packaged greater amounts of CD47 into EVs, and these EVs displayed an increased binding to SIRPa of macrophages, thus inhibiting macrophage-mediated phagocytosis of DLBCL cells (Figure 1J-O).3.Antibodies against human CD47 specifically identified human CD47 on the circulating exosomes from mice bearing DLBCL xenografts but not the control mice. The level of circulating EV CD47 positively correlated with tumor size. Injection of EVs derived from DLBCL cells promoted the growth of tumors, whereas pre-treatment of the EVs with anti-CD47 antibodies (IBI188), but not IgG isotype or CD63-blocking antibodies, inhibited the effect (Figure 1P, Q).4.The presence of CD47 in the EVs isolated from the plasma of DLBCL patients, and the level of CD47 on the circulating EVs was significantly higher in DLBCL patients than that in healthy donors (Figure 1R, S). The receiver ROC curve shows that the level of circulating EV CD47 could distinguish DLBCL patients from healthy donors.ConclusionOur studies suggest that the EV CD47-SIRPa interaction may represent a critical mechanism by which DLBCL cells escape immune-mediated clearance. Our study also raises the possibility that disrupting the interaction between the EV CD47 and macrophage SIRPa is a mechanism in the CD47-SIRPa blockade-based therapies. Moreover, high levels of circulating EV CD47 would follow and correlate positively with the phagocytic activity of macrophages, and reflect the presence of a successful anti-tumor immunity elicited by the anti-CD47 therapy. Together, these findings show that EV CD47 represents an unexplored therapeutic target, which could overcome resistance to current CD47 antibody approaches. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

An Accurate Prognostic Index Model for Adult HLH (HHLWG-HPI): A Multicenter Retrospective Analysis in China

Hemophagocytic lymphohistiocytosis (HLH) is an immune disorder with rapid progression and poor survival. Individual treatment strategy is restricted, due to the absence of precise stratification criteria. In this multicenter retrospective study, we aimed to develop a feasible prognostic model for adult HLH in China. A total of 270 newly diagnosed patients of adult HLH were retrieved from Huaihai Lymphoma Working Group (HHLWG), of whom 184 from 5 medical centers served as derivation cohort, and 86 cases from 3 other centers served as validation cohort. X-Tile program and Maxstat analysis were used to identify optimal cut-off points of continuous variables; univariate and multivariate Cox analysis were used for variables selection, and Kaplan‐Meier curve was used to analyze the value of variables on prognosis. The C-index, brier score and calibration curve were used for model validation. Multivariate analysis showed that age, creatinine, albumin, platelet, lymphocyte ratio and alanine aminotransferase were independent prognostic factors. By rounding up the hazard ratios from 6 significant variables, a maximum of 9 points was assigned. The final scoring model of HHLWG-HPI was identified with four risk groups: low risk (≤3 pts), low-intermediate risk (4 pts), high-intermediate risk (5-6 pts), and high risk (≥7 pts), with 5-year overall survival rates of 68.5%, 35.2%, 21.3%, and 10.8%, respectively. The C-indexes were 0.796 and 0.758 in derivation and validation cohort by using bootstrap resampling program. In conclusion, the HHLWG-HPI model provides a feasible and accurate stratification system for individualized treatment strategy in adult HLH. DisclosuresNo relevant conflicts of interest to declare.

A prognostic index model for predicting long-term recurrence of uterine leiomyoma after myomectomy.

IntroductionUterine leiomyoma (UL) is a common benign pelvic tumor in women that has a high recurrence rate. Our aim is to propose a prognostic index (PI) model for predicting the long-term recurrence risk of uterine leiomyoma (UL).MethodsA total of 725 women who underwent myomectomy were enrolled in this retrospective multicenter study. Patients were contacted for follow-up. A PI model was proposed based on the multivariate Cox regression analysis in the model group. The predictive value of this model was tested in both internal and external validation group.ResultsPI formula = 1.5(if 3–5 leiomyomas) or 2(if >5 leiomyomas)+1(if residue)+1(if not submucosal)+1(if combined endometriosis). The PI value was divided into low-risk, intermediate-risk, and high-risk group by cut-off values 1.25 and 3.75. In the model group, the high-risk group had a significantly 4.55 times greater recurrence risk of UL than that in the low-risk group [cumulative recurrence rate (CR): 82.1% vs 29.5%, HR = 4.55, 95% CI 2.821–7.339]; the intermediate-risk group had a significantly 2.81 times greater recurrence risk of UL than that in the low-risk group (CR: 62.3% vs 29.5%, HR = 2.81, 95% CI 2.035–3.878). The differences between any two risk groups were also significant (P< 0.05) in both internal and external validation groups.ConclusionThe model was proved to be effective in predicting recurrence of UL after myomectomy.

Development of a novel autophagy-related gene prognostic signature for gastric cancer.

BackgroundA growing number of evidence has revealed the vital role of autophagy in pathological processes of cancer, including gastric cancer (GC). However, many previous studies only focused on exploring single pathway or limited genes of interest in GC, which only reflected partial functions of autophagy. The present study aimed to construct an autophagy-related risk signature for GC.MethodsDifferentially expressed autophagy-related genes (ARGs) in GC and non-tumor samples were screened through The Cancer Genome Atlas (TCGA) database, followed by bioinformatics analysis using the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) platforms. Prognosis-related ARGs were generated by univariate and multivariate Cox regression test.ResultsA total of seven prognosis-related ARGs (HSPB8, NRG2, GABARAPL1, TMEM74, DLC1, MAP1LC3C and NRG3) were determined to establish a prognostic index (PI) model, which was demonstrated to be an independent prognostic indicator for patients with GC. More importantly, it was successfully validated in an external cohort of patients from the GSE15460 dataset, indicating the useful reproducibility of this signature. In addition, the PI model was associated with immune cell infiltration estimates in GC.ConclusionsTaken together, the present study suggested that the seven ARGs-related signature could serve as an independent prognostic indicator for patients with GC.

Autophagy-related genes are potential diagnostic and prognostic biomarkers in prostate cancer.

BackgroundRecently, autophagy was found related to several malignances.MethodsTo explore the diagnostic and prognostic values of autophagy in prostate cancer (PCa), we first identified differentially expressed autophagy-related genes (DEARGs) based on The Cancer Genome Atlas (TCGA) Prostate Adenocarcinoma (PRAD) dataset. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were applied to perform gene functional annotation. Then, multivariate logistic regression analysis was applied to construct the risk index (RI). Receiver operating characteristic (ROC), calibration and decision curve analysis (DCA) curves were performed to identify the effectiveness of RI. Next, multivariate Cox regression analyses were performed to construct the prognostic index (PI) and autophagy-clinical prognostic index (ACPI).ResultsWe identified 16 DEARGs and functional annotation demonstrated the relevance of these genes to autophagy and revealed the association of these DEARGs with digestive system, drug resistance and apoptosis. Then, the RI was constructed based on 5 DEARGs and the area under the ROC curve (AUC) was 0.9858. Validation based on Gene Expression Omnibus (GEO) datasets suggested that the RI was effective. Next, 7 ARGs were identified associated with overall survival (OS) and the PI was developed composed of 3 ARGs. Finally, ACPI was constructed based on PI and the M stage.ConclusionsThis study provided potential models for predicting the risk and prognosis of PCa and indicated the molecular insights of autophagy in PCa. While no other dataset was applied to test the effectiveness of the PI and ACPI models attribute to the well prognosis of PCa.

Transcriptome profiling reveals an integrated mRNA-lncRNA signature with predictive value for long-term survival in diffuse large B-cell lymphoma.

For patients with diffuse large B-cell lymphoma (DLBCL), survival at 24 months is a milestone for long-term survival. The purpose of this study was to develop a multigene risk score (MGRS) to refine the International Prognostic Index (IPI) model to identify patients with DLBCL at high risk of death within 24 months. Using a robust statistical strategy, we built a MGRS incorporating nine mRNAs and two lncRNAs. Stratification and multivariable Cox regression analysis confirmed the MGRS as an independent risk factor. A nomogram based on IPI+MGRS model was constructed and its calibration plot showed close agreement between predicted 2-year survival rate and observed rate. The 2-year AUC was bigger with the IPI+MGRS model (ΔAUC=0.162; 95%CI 0.1295–0.1903) than with the IPI model, and the IPI+MGRS model more accurately predicted the prognostic risk of DLBCL. The 2-year survival decision curve revealed the IPI+MGRS model was more useful clinically than the IPI model. Functional enrichment analysis showed that the MGRS correlated with cell cycle, DNA replication and repair. The results were validated using an independent external dataset. In conclusion, we successfully developed an integrated mRNA–lncRNA signature to refine the IPI model for predicting long-term survival of patients with DLBCL.

An interactive network of alternative splicing events with prognostic value in geriatric lung adenocarcinoma via the regulation of splicing factors.

Alternative splicing (AS), an essential process for the maturation of mRNAs, is involved in tumorigenesis and tumor progression, including angiogenesis, apoptosis, and metastasis. AS changes can be frequently observed in different tumors, especially in geriatric lung adenocarcinoma (GLAD). Previous studies have reported an association between AS events and tumorigenesis but have lacked a systematic analysis of its underlying mechanisms. In the present study, we obtained splicing event information from SpliceSeq and clinical information regarding GLAD from The Cancer Genome Atlas. Survival-associated AS events were selected to construct eight prognostic index (PI) models. We also constructed a correlation network between splicing factors (SFs) and survival-related AS events to identify a potential molecular mechanism involved in regulating AS-related events in GLAD. Our study findings confirm that AS has a strong prognostic value for GLAD and sheds light on the clinical significance of targeting SFs in the treatment of GLAD.

Prognostic model of 10 immune-related genes and identification of small molecule drugs in bladder urothelial carcinoma (BLCA).

BackgroundWe aimed to establish an immune-related gene (IRG) based signature that could provide guidance for clinical bladder cancer (BC) prognostic surveillance.MethodsDifferentially expressed IRGs and transcription factors (TFs) between BCs and normal tissues were extracted from transcriptome data downloaded from the TCGA database. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to identify related pathways based on differently expressed IRGs. Then, univariate Cox regression analysis was performed to investigate IRGs with prognostic values and LASSO penalized Cox regression analysis was utilized to develop the prognostic index (PI) model.ResultsA total of 411 BC tissue samples and 19 normal bladder tissues in the TCGA database were enrolled in this study and 259 differentially expressed IRGs were identified. Networks between TFs and IRGs were also provided to seek the upstream regulators of differentially expressed IRGs. By means of univariate Cox regression analysis, 57 IRGs were analyzed with prognostic values and 10 IRGs were finally identified by LASSO penalized Cox regression analysis to construct the PI model. This model could significantly classified BC patients into high-risk group and low-risk group in terms of OS (P=9.923e-07) and its AUC reached 0.711. By means of univariate and multivariate COX regression analysis, this PI was proven to be a valuable independent prognostic factor (HR =1.119, 95% CI =1.066–1.175, P<0.001). CMap database analysis was also utilized to screen out 10 small molecules drugs with the potential for the treatment of BC.ConclusionsOur study successfully provided a novel PI based on IRGs with the potential to predict the prognosis of BC and screened out 10 small molecules drugs with the potential to treat BC. Besides, networks between TFs and IRGs were also displayed to seek its upstream regulators for future researches.

Relationship between a 7-mRNA signature of the pancreatic adenocarcinoma microenvironment and patient prognosis (a STROBE-compliant article).

The potential association between the prognosis of the pancreatic adenocarcinoma (PAAD) and its microenvironment is unclear. This study aims to construct a prognostic index (PI) model of the PAAD microenvironment to predict PAAD patient survival outcomes.The mRNA sequencing and the clinical parameters data were obtained from The Cancer Genome Atlas. Immune and stromal scores were computed using the expression data algorithm to capture infiltration of immune and stromal cells in the PAAD tissue, where patients were categorized as high and low score groups according to these scores. Differentially expressed genes were identified using the R package LIMMA. Univariate and multivariate Cox regression analysis were conducted to select candidate survival-correlated gene signatures from the tumor microenvironment for constructing a model. The Kaplan-Meier method was used to access overall survival of the primary and validation cohorts. The immunological features of the PI model was explored using the Tumor Immune Estimation Resource (TIMER) database. Bioinformatic analyses were conducted based on the DAVID database.A total of 1266 overlapping differentially expressed genes and 49 prognosis-associated genes were identified. A 7-mRNA signature (GBP5, BICC1, SLC7A14, CYSLTR1, P2RY6, VENTX, and RAB39B) was screened for the construction of a PI model (area under the curve = 0.791). In both the primary and validation cohorts, Kaplan Meier analysis revealed that the overall survival of the high-risk group was significantly worse compared to the low-risk group (P < .0001, P = .0028 respectively). The TIMER database described that the 7 signature genes were correlated with immune infiltrating cells and tumor purity. Bioinformatic analyses revealed that these prognosis-associated genes were significantly enriched during inflammation, the defense response, would response, calcium ion transport, and plasma membrane part.A list of the prognosis-correlated genes was generated based on the PAAD microenvironment. A 7-mRNA PI model may be used for predicting the prognosis of PAAD patients.

Establishment of a Genomic-Clinicopathologic Nomogram for Predicting Early Recurrence of Hepatocellular Carcinoma After R0 Resection

BackgroundA high rate of postoperative recurrence, especially early recurrence (ER) occurring within 1 year, seriously impedes patients with hepatocellular carcinoma (HCC) from achieving long-term survival. This study aimed to establish a genomic-clinicopathologic nomogram for precisely predicting ER in HCC patients after R0 resection. MethodsTwo reliable datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were selected as the training and validation cohorts, respectively. The prognostic genes related to ER were screened out by univariate Cox regression analysis and differential expression analysis. The gene-based prognostic index was constructed using LASSO and Cox regression analyses, and its independent prognostic value was assessed by Kaplan-Meier and multivariate Cox analyses. Gene set enrichment analysis (GSEA) was performed to explore the biological pathways related to the prognostic index. Finally, the nomogram integrating all the independent prognostic factors was established and comprehensively evaluated by calibration plots, the C-index, receiver operating characteristic curves, and decision curve analysis. ResultsNine dysregulated and prognostic genes related to ER (ZNF131, TATDN2, TXN, DDX55, KPNA2, ZNF30, TIMELESS, SFRP1, and COLEC11) were identified (all P < 0.05). The prognostic index model based on the 9 genes was successfully constructed using the TCGA cohort and showed a certain capability to discriminate the ER group from the non-ER group (P < 0.05) and good independent prognostic value in terms of predicting poor early recurrence-free survival (P < 0.05). Eight biological pathways significantly related to ER were identified by GSEA, such as “cell cycle”, “homologous recombination” and “p53 signaling pathway.” The genomic-clinicopathologic nomogram integrating the 9-gene-based prognostic index and TNM stage displayed significantly higher predictive accuracy and clinical application value than that of TNM stage model both in the training and validation cohorts (all P < 0.05). ConclusionsThe novel genomic-clinicopathologic nomogram may be a convenient and powerful tool for accurately predicting ER in HCC patients after R0 resection.

Poor prognostic impact of high serum ferritin levels in patients with a lower risk of diffuse large B cell lymphoma.

The International Prognostic Index (IPI) and other prognostic models cannot accurately classify risks in patients with lower-risk diffuse large B cell lymphoma (DLBCL). This study retrospectively analyzed serum levels of ferritin (500 and ≥ 500ng/mL) and other reported risk factors for survival in 312 patients. High and high-intermediate risk IPI scores (hazard ratio (HR) [95% confidence interval (CI)] 2.22 [1.33, 3.72], P = 0.002 and 2.29 [1.33, 3.93], P = 0.003, respectively) and ferritin concentration ≥ 500ng/mL (HR [95% CI] 2.22 [1.37, 3.60], P = 0.001 and 2.18 [1.31, 3.63], P = 0.003, respectively) were independent poor prognostic factors for 5-year progression-free survival (PFS) and overall survival (OS) rates in all patients. Additionally, high ferritin level (≥ 500ng/mL) was an independent poor prognostic factor for PFS and OS in patients with lower-risk IPI (HR [95% CI] 3.37 [1.36, 8.33], P = 0.009 and 3.29 [1.23, 8.83], P = 0.0018, respectively). In conclusion, serum ferritin levels may be helpful in predicting survival in patients with DLBCL, especially in those with lower-risk IPI scores.

Personalized predictions of treatment outcome in patients with post-stroke depressive symptoms

Post-stroke depressive symptoms have a vast individual and societal impact. However, research into interventions for such symptoms show contradictory results; it is unclear what works for which patients. In addition, clinical prediction tools are lacking. This study aimed to develop a prognostic index model for treatment outcome in patients with post-stroke depressive symptoms. Data from a randomized controlled trial (n = 61) evaluating 2 interventions for post-stroke depressive symptoms were used to predict post-treatment post-stroke depressive symptoms and participation. From 18 pre-treatment variables of patients and caregivers, predictors were selected using elastic net regression. Based on this selection, prognostic index scores (i.e. predictions) for both out-comes were computed for each individual patient. The depression model included all pre-treatment variables, explaining 44% of the variance. The strongest predictors were: lesion location, employment, participation, comorbidities, mobility, sex, and pre-treatment depression. Six predictors of post-treatment participation were identified, explaining 51% of the variance: mobility, pre-treatment participation, age, satisfaction with participation, caregiver strain, and psychological distress of the spouse. The cross-validated prognostic index scores correlated highly with the actual outcome scores (depression: correlation = 0.672; participation: correlation = 0.718). Post-stroke depressive symptoms form a complex and multifactorial problem. Treatment outcome is influenced by the characteristics of the stroke, the patients, and their spouses. The results show that psychological distress is probably no obstacle to attempting to improve participation. The personalized predictions (prognostic index scores) of treatment outcome show promising results, which, after further replication and validation, could aid clinicians with treatment selection.

Impact of clinical versus radiographic progression on clinical outcomes in metastatic castration-resistant prostate cancer

ObjectivesUnequivocal clinical progression (UCP)—a worsening of clinical status with or without radiographic progression (RAD)—represents a distinct mode of disease progression in metastatic prostate cancer. We evaluated the prevalence, risk factors and the impact of UCP on survival outcomes.MethodsA post-hoc analysis of the COU-AA-302, a randomised phase 3 study of abiraterone plus prednisone (AAP) versus prednisone was performed. Baseline characteristics were summarised. Cox proportional-hazards model and Kaplan-Meier method were used for survival and time to event analyses, respectively. Iterative multiple imputation method was used for correlation between clinicoradiographic progression-free survival (crPFS) and overall survival (OS).ResultsOf 736 patients with disease progression, 280 (38%) had UCP-only and 124 (17%) had UCP plus RAD. Prognostic index model high-risk group was associated with increased likelihood of UCP (p<0.0001). Median OS was 25.7 months in UCP-only and 33.0 months for RAD-only (HR 1.39; 95% CI 1.16 to 1.66; p=0.0003). UCP adversely impacted OS in both treatment groups. Lowest OS was seen in patients with prostate specific antigen (PSA)-non-response plus UCP-only progression (median OS 22.6 months (95% CI 20.7 to 24.4)). Including UCP events lowered estimates of treatment benefit—median crPFS was 13.3 months (95% CI 11.1 to 13.8) versus median rPFS of 16.5 months (95% CI 13.8 to 16.8) in AAP group. Finally, crPFS showed high correlation with OS (r=0.67; 95% CI 0.63 to 0.71).ConclusionsUCP is a common and clinically relevant phenomenon in patients with metastaticcastration-resistant prostate cancer (mCRPC) treated with AAP or prednisone. UCP is prognostic and associated with inferior OS and post-progression survival. A combination of PSA-non-response and UCP identifies patients with poorest survival. When included in PFS analysis, UCP diminishes estimates of treatment benefit. Continued study of UCP in mCRPC is warranted.

A prognostic index model to individually predict clinical outcomes for colorectal cancer with synchronous bone metastasis

Background: The prognosis of synchronous bone metastasis (BM) in colorectal cancer (CRC) is poor and rarely concerned. A clinical tool to evaluate the prognosis and clinical outcomes for BM would be attractive in current clinical practice.Methods: A total of 342 CRC patients with synchronous BM were identified from Surveillance, Epidemiology, and End Results (SEER) database. The cancer specific survival (CSS) was estimated with the Kaplan-Meier method. Prognostic factors were identified from multivariate Cox model, and the final clinical nomogram was developed to predict the CSS. The concordance index (C-index) was used to assess the discriminative ability. Calibration curves were provided to internally validate the performance of the nomogram.Results: The nomogram finally consisted of 6 prognostic factors including age, tumor grade, AJCC N stage, carcinoembryonic antigen (CEA) levels, primary tumor resection and chemotherapy, which translated the effects of prognostic factors into certain scores to predict the 1-, 2- and 3-year CSS for the synchronous BM in CRC patients. The nomogram presented a good accuracy for predicting the CSS with the C-index of 0.742. The calibration of the nomogram predictions was also accurate.Conclusions: This nomogram was accurate enough to predict the CSS of CRC patients with synchronous BM using readily available clinicopathologic factors and could provide individualized clinical decisions for both physicians and patients.

Genome-wide Analysis of the Alternative Splicing Profiles Revealed Novel Prognostic Index for Kidney Renal Cell Clear Cell Carcinoma.

Alternative splicing (AS) is a major mechanism that greatly enhanced the diversity of proteome. Mounting evidence demonstrated that aberration of AS are important steps for the initiation and progression of human cancers. Here, we comprehensively investigated the association between whole landscape of AS profiles and the survival outcome of renal cell carcinoma (RCC) patients using RNA-seq data from TCGA SpliceSeq. Because of the limited number size of deaths in kidney chromophobe renal cell carcinoma (KICH) and papillary renal cell carcinoma (KIRP) TCGA cohorts, we only conducted survival analysis in kidney clear renal cell carcinoma (KIRC). We further constructed prognostic index (PI) based on prognosis-related AS events and built correlation network for splicing factors and prognosis-related AS events. According to the results, a total of 5351 AS events in 3522 genes were significantly correlated with the overall survival (OS) of kidney clear cell renal cell carcinoma (KIRC) patients. Seven of the PI models exhibited preferable prognosis-predicting capacity for KIRC with PI-ALL reaching the highest area under curve value of 0.875. The splicing regulatory network between splicing factors and prognosis-related AS events depicted a tangled web of relationships between them. One of the splicing factors: KHDRBS3 was validated by immunohistochemistry to be down-regulated in KIRC tissues. In conclusion, the powerful efficiency of risk stratification of PI models indicated the potential of AS signature as promising prognostic markers for KIRC and the splicing regulation network provided possible genetic mechanism of KIRC.

251P - A prognostic index model for predicting long-term recurrence of uterine leiomyoma after initial myomectomy in women aged 18-44 years

BackgroundUterine leiomyoma (UL) is the most common female pelvic benign tumor with recurrence rates of 16.7%-52.8%. We aimed to propose a prognostic index (PI) model for predicting the long-term (>5 years) risk of UL recurrence. MethodsWomen aged 18–44 years who initially underwent myomectomy for UL at one of the three study hospitals between April 2012 and October 2013 were enrolled. The data collected included patients’ demographics, leiomyomas’ characteristics, relapses, subsequent contraceptive methods, and postoperative gravidity and parity. A PI model was proposed based on the β-coefficients in the results of multivariate Cox regression analysis in prediction model group. The differences among the PI-based risk groups were tested by Kaplan–Meier analysis (using paired log-rank test) and univariate Cox regression analysis (using the Forward: LR) in prediction model, internal validation and external validation group. ResultsThere were 725 patients included in this study. PI formula =1.5(if 3-5 leiomyomas)+2(if > 5 leiomyomas)+1(if residue)+1(if not submucosal)+1(if combined endometriosis). The PI value (0-5) was divided into low-risk group, intermediate-risk group, and high-risk group by cut-off values 1.25 and 3.75. In the prediction model group, the high-risk group had a significantly 4.55 times greater recurrence risk of UL than that in the low-risk group [cumulative recurrence rate (CR): 82.1% vs 29.5%, HR = 4.55, 95% CI; 2.821-7.339]; the intermediate-risk group had a significantly 2.81 times greater recurrence risk of UL than that in the low-risk group (CR: 62.3% vs 29.5%, HR = 2.81, 95% CI; 2.035-3.878). The differences between any two risk groups were also statistically significant (P < 0.05) in both internal and external validation groups. ConclusionsThe PI model was proved to be effective in distinguishing low-risk, intermediate-risk, and high-risk groups for long-term recurrence of UL after initial myomectomy in women aged 18-44 years, allowing it to be an objective tool aid in clinical decision making. In the future, prospective researches should be carried out to confirm the predictive ability of this PI model, and its practical value in clinical decision making. Legal entity responsible for the studyZheng Yu Li. FundingHas not received any funding. DisclosureAll authors have declared no conflicts of interest.

Construction of a model to predict the prognosis of patients with cholangiocarcinoma using alternative splicing events.

Cholangiocarcinoma (CCA) is a type of malignant tumor that originates in the mucosal epithelial cells of the biliary system. It is a highly aggressive cancer that progresses rapidly, has low surgical resection rates and a high recurrence. At present, no prognostic molecular biomarker for CCA has been identified. However, CCA progression is affected by mRNA precursors that modify gene expression levels and protein structures through alternative splicing (AS) events, which create molecular indicators that may potentially be used to predict CCA outcomes. The present study aimed to construct a model to predict CCA prognosis based on AS events. Using prognostic data available from The Cancer Genome Atlas, including the percent spliced index of AS events obtained from TCGASpliceSeq in 32 CCA cases, univariate and multivariate Cox regression analyses were performed to assess the associations between AS events and the overall survival (OS) rates of patients with CCA. Additional multivariate Cox regression analyses were used to identify AS events that were significantly associated with prognosis, which were used to construct a prediction model with a prognostic index (PI). A receiver operating characteristic (ROC) curve was used to determine the predictive value of the PI, and Pearson's correlation analysis was used to determine the association between OS-related AS events and splicing factors. A total of 38,804 AS events were identified in 9,673 CCA genes, among which univariate Cox regression analysis identified 1,639 AS events associated with OS (P<0.05); multivariate Cox regression analysis narrowed this list to 23 CCA AS events (P<0.001). The final PI model was constructed to predict the survival of patients with CCA; the ROC curve demonstrated that it had a high predictive power for CCA prognosis, with a highest area under the curve of 0.986. Correlations between 23 OS-related AS events and splicing factors were also noted, and may thus, these AS events may be used to improve predictions of OS. In conclusion, AS events exhibited potential for predicting the prognosis of patients with CCA, and thus, the effects of AS events in CCA required further examination.

Adjuvant chemoradiation in resected gallbladder cancer: a prognostic index model for predicting overall survival

Patients with gallbladder cancer (GBC) have a dismal prognosis. We investigated outcomes and risk factors for overall survival (OS) in patients treated with radical surgery and adjuvant chemoradiotherapy (CRT). A total of 212 patients with LAGC (⩾pT3 59% and/or pN+ 52%) were studied. The primary endpoint of the analysis was OS. We constructed a risk scoring system in which points were assigned to each risk factor by dividing each β coefficient in the final model by the lowest β coefficient and rounding to the nearest integer. A risk score was assigned to each subject by adding up the points for each risk factor present. Subjects were then divided into three risk groups based on their risk scores (0 points = low risk, 1–2 points = intermediate risk, 3–6 points = high risk). Median follow-up was 46.2 months (2–235). Five-year OS for the entire cohort was 53%. In multivariate analysis, higher pT stage [HR, 2.43 (1.29–3.68); p = 0.01], R1 resection [HR, 5.06 (3.12–8.19); p < 0.001], and number of surgical procedures [HR, 1.41 (1.01–2.16); p = 0.05] were associated with an increased risk of death. Five-year OS for patients with low (n = 63), intermediate (n = 94), and high (n = 55) risk was 79.1%, 51.2%, and 9.5%, respectively. Overall results after multimodality treatment of GBC are promising. A risk model was generated to determine a prognostic index for individual patients with GBC. Classification of risk factors for death has contributed to propose a prognostic index that could allow us to guide risk-adapted tailored treatment.

A novel alternative splicing-based prediction model for uteri corpus endometrial carcinoma.

Alternative splicing (AS) is crucial a mechanism by which the complexity of mammalian and viral proteom increased overwhelmingly. There lacks systematic and comprehensive analysis of the prognostic significance of AS profiling landscape for uteri corpus endometrial carcinoma (UCEC). In this study, univariate and multivariate Cox regression analyses were conducted to identify candidate survival-associated AS events curated from SpliceSeq for the construction of prognostic index (PI) models. A correlation network between splicing factor-related AS events and significant survival-associated AS events were constructed using Cytoscape 3.5. As consequence, 28281 AS events from 8137 genes were detected from 506 UCEC patients, including 2630 survival-associated AS events. Kaplan Meier survival analysis revealed that six of the seven PI models (AD, AP, AT, ME, RI and ALL) exhibited good performance in stratifying the prognosis of low risk and high risk group (P<0.05). Among the six PI models, PI-AT performed best with an area under curves (AUC) value of 0.758 from time-dependent receiver operating characteristic. Correlation network implicated potential regulatory mechanism of AS events in UCEC. PI models based on survival-associated AS events for UCEC in this study showed preferable prognosis-predicting ability and may be promising prognostic indicators for UCEC patients.Summary: This is the first study to systematically investigate the prognostic value of AS in UCEC. Findings in the presents study supported the clinical potential of AS for UCEC and shed light on the potential AS-associated molecular basis of UCEC.