Prognostic Value of the Albumin-to-γ-glutamyltransferase Ratio for Gallbladder Cancer Patients and Establishing a Nomogram for Overall Survival

Uncovering the ceRNA Network Related to the Prognosis of Stomach Adenocarcinoma Among 898 Patient Samples

Stomach adenocarcinoma (STAD) patients are often associated with significantly high mortality rates and poor prognoses worldwide. Among STAD patients, competing endogenous RNAs (ceRNAs) play key roles in regulating one another at the post-transcriptional stage by competing for shared miRNAs. In this study, we aimed to elucidate the roles of lncRNAs in the ceRNA network of STAD, uncovering the molecular biomarkers for target therapy and prognosis. Specifically, a multitude of differentially expressed lncRNAs, miRNAs, and mRNAs (i.e., 898 samples in total) was collected and processed from TCGA. Cytoplasmic lncRNAs were kept for evaluating overall survival (OS) time and constructing the ceRNA network. Differentially expressed mRNAs in the ceRNA network were also investigated for functional and pathological insights. Interestingly, we identified one ceRNA network including 13 lncRNAs, 25 miRNAs, and 9 mRNAs. Among them, 13 RNAs were found related to the patient survival time; their individual risk score can be adopted for prognosis inference. Finally, we constructed a comprehensive ceRNA regulatory network for STAD and developed our own risk-scoring system that can predict the OS time of STAD patients by taking into account the above.

An MRI-based radiomics nomogram to predict progression-free survival in patients with endometrial cancer

Aim: This study aimed to explore the importance of an MRI-based radiomics nomogram in predicting the progression-free survival (PFS) of endometrial cancer.Methods: Based on clinicopathological and radiomic characteristics, we established three models (clinical, radiomics and combined model) and developed a nomogram for the combined model. The Kaplan-Meier method was utilized to evaluate the association between nomogram-based risk scores and PFS.Results: The nomogram had a strong predictive ability in calculating PFS with areas under the curve (ROC) of 0.905 and 0.901 at 1 and 3 years, respectively. The high-risk groups identified by the nomogram-based scores had shorter PFS compared with the low-risk groups.Conclusion: The radiomics nomogram has the potential to serve as a noninvasive imaging biomarker for predicting individual PFS of endometrial cancer.

The predictive value of γ-glutamyl transferase to serum albumin ratio in hepatocellular carcinoma patients after liver transplantation

Background

Elevated preoperative γ-glutamyl transferase (GGT) levels or reduced serum albumin levels have been established as negative prognostic factors for patients with hepatocellular carcinoma (HCC) and various other tumors. Nonetheless, the prognostic significance of the GGT to serum albumin ratio (GAR) in liver transplantation (LT) therapy for HCC is still not well-defined.

Methods

A retrospective analysis was conducted on the clinical data of 141 HCC patients who underwent LT at Shulan (Hangzhou) Hospital from June 2017 to November 2020. Using the receiver operating characteristic (ROC) curve, the optimal GAR cutoff value to predict outcomes following LT was assessed. Univariate and multivariate Cox proportional hazards regression analyses were used to identify independent risk factors associated with both overall survival (OS) and recurrence-free survival (RFS).

Results

A GAR value of 2.04 was identified as the optimal cutoff for predicting both OS and RFS, with a sensitivity of 63.2% and a specificity of 74.8%. Among these patients, 80 (56.7%) and 90 (63.8%) met the Milan and the University of California San Francisco (UCSF) criteria, respectively. Univariate Cox regression analysis showed that microvascular invasion (MVI), maximum tumor size (>5 cm), total tumor size (>8 cm), liver cirrhosis, TNM stage (III), and GAR (≥2.04) were significantly associated with both postoperative OS and RFS in patients with HCC (all p < 0.05). Multivariate Cox regression analysis indicated that GAR (≥2.04) was independently linked with RFS and OS.

Conclusion

Pre-transplant GAR ≥2.04 is an independent correlate of prognosis and survival outcomes after LT for HCC and can be used as a prognostic indicator for both mortality and tumor recurrence following LT.

A prognostic model using FIGO 2018 staging and MRI-derived tumor volume to predict long-term outcomes in patients with uterine cervical squamous cell carcinoma who received definitive radiotherapy

BACKGROUND

Uterine cervical carcinoma is a severe health threat worldwide, especially in China. The International Federation of Gynecology and Obstetrics (FIGO) has revised the staging system, emphasizing the strength of magnetic resonance imaging (MRI). We aimed to investigate long-term prognostic factors for FIGO 2018 stage II-IIIC2r uterine cervical squamous cell carcinoma following definitive radiotherapy and establish a prognostic model using MRI-derived tumor volume.

METHODS

Patients were restaged according to the FIGO 2018 staging system and randomly grouped into training and validation cohorts (7:3 ratio). Optimal cutoff values of squamous cell carcinoma antigen (SCC-Ag) and tumor volume derived from MRI were generated for the training cohort. A nomogram was constructed based on overall survival (OS) predictors, which were selected using univariate and multivariate analyses. The performance of the nomogram was validated and compared with the FIGO 2018 staging system. Risk stratification cutoff points were generated, and survival curves of low-risk and high-risk groups were compared.

RESULTS

We enrolled 396 patients (training set, 277; validation set, 119). The SCC-Ag and MRI-derived tumor volume cutoff values were 11.5 ng/mL and 28.85 cm³, respectively. A nomogram was established based on significant prognostic factors, including SCC-Ag, poor differentiation, tumor volume, chemotherapy, and FIGO 2018 stage. Decision curve analysis indicated that the net benefits of our model were higher. The high-risk group had significantly shorter OS than the low-risk group in both the training (p < 0.0001) and validation sets (p = 0.00055).

CONCLUSIONS

Our nomogram predicted long-term outcomes of patients with FIGO 2018 stage II-IIIC2r uterine cervical squamous cell carcinoma. This tool can assist gynecologic oncologists and patients in treatment planning and prognosis.

Integrating machine learning and bioinformatics analysis to m6A regulator-mediated methylation modification models for predicting glioblastoma patients' prognosis and immunotherapy response

BACKGROUND

Epigenetic regulations of immune responses are essential for cancer development and growth. As a critical step, comprehensive and rigorous explorations of m6A methylation are important to determine its prognostic significance, tumor microenvironment (TME) infiltration characteristics and underlying relationship with glioblastoma (GBM).

METHODS

To evaluate m6A modification patterns in GBM, we conducted unsupervised clustering to determine the expression levels of GBM-related m6A regulatory factors and performed differential analysis to obtain m6A-related genes. Consistent clustering was used to generate m6A regulators cluster A and B. Machine learning algorithms were implemented for identifying TME features and predicting the response of GBM patients receiving immunotherapy.

RESULTS

It is found that the m6A regulatory factor significantly regulates the mutation of GBM and TME. Based on Europe, America, and China data, we established m6Ascore through the m6A model. The model accurately predicted the results of 1206 GBM patients from the discovery cohort. Additionally, a high m6A score was associated with poor prognoses. Significant TME features were found among the different m6A score groups, which demonstrated positive correlations with biological functions (i.e., EMT2) and immune checkpoints.

CONCLUSIONS

m6A modification was important to characterize the tumorigenesis and TME infiltration in GBM. The m6Ascore provided GBM patients with valuable and accurate prognosis and prediction of clinical response to various treatment modalities, which could be useful to guide patient treatments.

Multi-clinical index classifier combined with AI algorithm model to predict the prognosis of gallbladder cancer

Objectives

It is significant to develop effective prognostic strategies and techniques for improving the survival rate of gallbladder carcinoma (GBC). We aim to develop the prediction model from multi-clinical indicators combined artificial intelligence (AI) algorithm for the prognosis of GBC.

Methods

A total of 122 patients with GBC from January 2015 to December 2019 were collected in this study. Based on the analysis of correlation, relative risk, receiver operator characteristic curve, and importance by AI algorithm analysis between clinical factors and recurrence and survival, the two multi-index classifiers (MIC1 and MIC2) were obtained. The two classifiers combined eight AI algorithms to model the recurrence and survival. The two models with the highest area under the curve (AUC) were selected to test the performance of prognosis prediction in the testing dataset.

Results

The MIC1 has ten indicators, and the MIC2 has nine indicators. The combination of the MIC1 classifier and the "avNNet" model can predict recurrence with an AUC of 0.944. The MIC2 classifier and "glmet" model combination can predict survival with an AUC of 0.882. The Kaplan-Meier analysis shows that MIC1 and MIC2 indicators can effectively predict the median survival of DFS and OS, and there is no statistically significant difference in the prediction results of the indicators (MIC1: χ² = 6.849, P = 0.653; MIC2: χ² = 9.14, P = 0.519).

Conclusions

The MIC1 and MIC2 combined with avNNet and mda models have high sensitivity and specificity in predicting the prognosis of GBC.

Preoperative Fibrinogen Albumin Ratio is an Effective Biomarker for Prognostic Evaluation of Gallbladder Carcinoma After Radical Resection: A 10-Year Retrospective Study at a Single Center

Background

To explore and screen preoperative serum immune response level-related biomarkers with better prognostic ability and developed a prognostic model for decision-making in clinical practice for gallbladder carcinoma (GBC) patients.

Methods

A total of 427 patients who underwent radical resection for GBC in the Department of Hepatobiliary Surgery of the First Affiliated Hospital of Xi'an Jiaotong University from January 2011 to December 2020 were retrospectively analyzed. Time-dependent receiver operating characteristic (time-ROC) was performed to determine the prognostic predictive power of preoperative biomarkers. A nomogram survival model was established and validated.

Results

Time-ROC indicated that the preoperative fibrinogen-to-albumin ratio (FAR) had a better predictive ability for overall survival among preoperative serum immune response level-related biomarkers. Multivariate analysis indicated that FAR was an independent risk factor (P<0.05). The proportion of clinicopathological characteristics of poor prognosis (such as advanced T stage, and N1-2 stage) was significantly higher in high FAR group (P<0.05). Subgroup analyses indicate the prognostic discrimination ability of FAR depended on CA19-9, CA125, liver involvement, major vascular invasion, perineural invasion, T stage, N stage, and TNM stage (all P <0.05). A nomogram model was established based on the prognostic independent risk factors with the C-index of 0.803 (95% CI:0.771~0.835) and 0.774 (95% CI:0.696~0.852) in the training and testing sets, respectively. The decision curve analysis indicated the nomogram model had a better predictive ability than the FAR and TNM staging system in the training and testing sets.

Conclusion

Preoperative serum FAR has a better predictive ability for overall survival among preoperative serum immune response level-related biomarkers, and it can be used for survival assessment of GBC and guide clinical decision-making.

Development and validation of a nomogram prediction model based on albumin-to-alkaline phosphatase ratio for predicting the prognosis of gallbladder carcinoma

Gallbladder carcinoma (GBC) is a rare biliary tract cancer with a high recurrence rate and a poor prognosis. Albumin-alkaline phosphatase ratio (AAPR) has been demonstrated to be a prognostic predictor for several cancers, but its predictive value for GBC patients remains unknown. The aim of this study was to investigate the predictive role of AAPR in GBC patients and to develop a novel nomogram prediction model for GBC patients. We retrospectively collected data from 80 patients who underwent surgery at the Hospital of 81st Group Army PLA as a training cohort. Data were collected from 70 patients with the same diagnosis who underwent surgery at the First Affiliated Hospital of Hebei North University as an external verification cohort. The optimal cut-off value of AAPR was determined using X-tile software. A nomogram for the overall survival (OS) based on multivariate Cox regression analysis was developed and validated using calibration curves, Harrell's concordance index, the receiver operating characteristic curves, and decisive curve analyses. The optimal cut-off value of AAPR was .20. Univariate and multivariate Cox regression analyses demonstrated that BMI (p = .043), R0 resection (p = .001), TNM stage (p = .005), and AAPR (p = .017) were independent risk factors for GBC patients. In terms of consistency, discrimination, and net benefit, the nomogram incorporating these four independent risk factors performed admirably. AAPR is an independent predictor of GBC patients undergoing surgery, and a novel nomogram prediction model based on AAPR showed superior predictive ability.

High systemic inflammation score is associated with adverse survival in skull base chordoma

Background

The systemic inflammation score (SIS), based on preoperative lymphocyte to monocyte ratio (LMR) and albumin (ALB), was recently developed and is demonstrated to be a novel prognostic indicator in several cancers. However, data discussing the utility of SIS in chordoma are lacking. We aimed to investigate the distribution and the prognostic role of SIS in primary skull base chordoma patients undergoing surgery.

Material and methods

Preoperative SIS was retrospectively collected from 183 skull base chordoma patients between 2008 and 2014 in a single center. Its associations with clinical features and overall survival (OS) were further analyzed. The SIS-based nomogram was developed and evaluated by the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

The numbers of patients in the SIS 2, 1, and 0 group were 29 (15.8%), 60 (32.8%), 94 (51.4%), respectively. High SIS was associated with older age (p = 0.008), brainstem involvement of tumors (p = 0.039), and adverse OS (p < 0.001). Importantly, multivariate Cox analysis showed that high SIS independently predicts adverse OS. Furthermore, the nomogram based on SIS and clinical variables showed eligible performance for OS prediction in both training and validation cohorts.

Conclusions

The SIS is a promising, simple prognostic biomarker, and the SIS-based nomogram serves as a potential risk stratification tool for outcome in skull base chordoma patients.

The Prognostic Value of the Albumin to Gamma-Glutamyltransferase Ratio in Patients with Hepatocellular Carcinoma Undergoing Radiofrequency Ablation

Albumin to gamma-glutamyltransferase ratio (AGR) is a newly developed biomarker for the prediction of patients' prognosis in solid tumors. The purpose of the study was to establish a novel AGR-based nomogram to predict tumor prognosis in patients with early-stage HCC undergoing radiofrequency ablation (RFA). 394 hepatocellular carcinoma (HCC) patients who had received RFA as initial treatment were classified into the training cohort and validation cohort. Independent prognostic factors were identified by univariate and multivariate analyses. The value of AGR was evaluated by the concordance index (C-index), receiver operating characteristic (ROC) curves, and likelihood ratio tests (LAT). Logistic regression and nomogram were performed to establish the pretreatment scoring model based on the clinical variables. As a result, AGR = 0.63 was identified as the best cutoff value to predict overall survival (OS) in the training cohort. According to the results of multivariate analysis, AGR was an independent indicator for OS and recurrence-free survival (RFS). In both training cohort and validation cohort, the high-AGR group showed better RFS and OS than the low-AGR group. What is more, the C-index, area under the ROC curves, and LAT χ 2 values suggested that AGR outperformed the Child-Pugh (CP) grade and albumin-bilirubin (ALBI) grade in terms of predicting OS. The AGR, AKP, and tumor size were used to establish the OS nomogram. Besides, the results of Hosmer-Lemeshow test and calibration curve analysis displayed that both nomograms in the training and validation cohorts performed well in terms of calibration. Therefore, the AGR-based nomogram can predict the postoperative prognosis of early HCC patients undergoing RFA.