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Background: The overall survival (OS) for patients with recurrent glioma is meager. Also, the effect of radionecrosis and prognostic factors for recurrent glioma remains controversial. In this regard, developing effective predictive models and guiding clinical care is crucial for these patients. Methods: We screened patients with recurrent glioma after radiotherapy and those who received surgery between August 1, 2013, and December 31, 2020. Univariate and multivariate Cox regression analyses determined the independent prognostic factors affecting the prognosis of recurrent glioma. Moreover, nomograms were constructed to predict recurrent glioma risk and prognosis. Statistical methods were used to determine the prediction accuracy and discriminability of the nomogram prediction model based on the area under the curve (AUC), the C-index, the decision curve analysis (DCA), and the calibration curve. In order to distinguish high-risk and low-risk groups for OS, the X-Tile and Kaplan-Meier (K-M) survival curves were employed, and the nomogram prediction model was further validated by the X-Tile and K-M survival curves. Results: According to a Cox regression analysis, independent prognostic factors of recurrent glioma after radiotherapy with radionecrosis were World Health Organization (WHO) grade and gliosis percentage. We utilized a nomogram prediction model to analyze results visually. The C-index was 0.682 (95% CI: 0.616–0.748). According to receiver operating characteristic (ROC) analysis, calibration plots, and DCA, the nomogram prediction model was found to have a high-performance ability, and all patients were divided into low-risk and high-risk groups based on OS (P <.001). Conclusion: WHO grade and gliosis percentage are prognostic factors for recurrent glioma with radionecrosis, and a nomogram prediction model was established based on these two variables. Patients could be divided into high- and low-risk groups with different OS by this model, and it will provide individualized clinical decisions for future treatment. [ABSTRACT FROM AUTHOR]

Objectives: Despite the increasingly modern surgical techniques in the oncology field, the factors that influence postoperative prognosis in patients with hypopharyngeal and laryngeal carcinoma (HLC) remain unclear. The study aimed to evaluate the factors influencing the prognosis of HLC patients with pathological diagnosis of squamous cell carcinoma, and the findings are intended to direct follow-up management strategies. Methods: A retrospective cohort study was performed. The study population included 407 postoperative patients with HLC from 2011 to 2015. Univariate and multivariate analyses were used to examine the prognostic factors identified. Results: Based on univariate analysis results, smoking and alcohol history, tumor differentiation, preoperative radiotherapy, primary tumor sites, flap reconstruction, lymph node invasion (LNI), and preoperative albumin levels (PAL) significantly affects the prognosis of HLC patients (P <.05). Meanwhile, multivariate analysis revealed that smoking pack-year (OR = 1.002, 95% CI = 1.001 ∼ 1.003), primary tumor sites (OR = 6.241, 95% CI = 1.715 ∼ 18.433), LNI (OR = 2.869, 95% CI = 1.095 ∼ 8.743), and PAL (OR =.020, 95% CI =.004 ∼ 0.104) were associated with complications. Tumor differentiation (OR = 0.650, 95% CI =.383 ∼ 0.855), primary tumor sites (OR = 12.392, 95% CI = 3.290 ∼ 26.679), LNI (OR = 16.323, 95% CI = 2.726 ∼ 47.729), preoperative radiotherapy (OR = 9.300, 95% CI = 3.182 ∼ 27.181), and PAL (OR =.321, 95% CI =.141 ∼.732) were associated with overall survival rates. Conclusion: Smoking and alcohol history, tumor differentiation, LNI, primary tumor sites, flap reconstruction, PAL, and preoperative radiotherapy are crucial factors that influence the postoperative prognosis of patients with HLC. In addition, a monogram of five factors was established to predict the survival rates of HLC patients. [ABSTRACT FROM AUTHOR]

Background: Gastric cardia adenocarcinoma (GCA) is a highly fatal form of cancer in humans. The aim of this study was to extract clinicopathological data of postoperative patients with GCA from the Surveillance, Epidemiology, and End Results database, analyze prognostic risk factors, and build a nomogram. Methods: In this study, the clinical information of 1448 patients with GCA who underwent radical surgery and were diagnosed between 2010 and 2015 was extracted from the SEER database. The patients were then randomly divided into training (n = 1013) and internal validation (n = 435) cohorts at a 7:3 ratio. The study also included an external validation cohort (n = 218) from a Chinese hospital. The study used the Cox and LASSO models to pinpoint the independent risk factors linked to GCA. The prognostic model was constructed according to the results of the multivariate regression analysis. To assess the predictive accuracy of the nomogram, four methods were used: C‐index, calibration curve, time‐dependent ROC curve, and DCA curve. Kaplan–Meier survival curves were also generated to illustrate the differences in cancer‐specific survival (CSS) between the groups. Results: The results of the multivariate Cox regression analysis showed that age, grade, race, marital status, T stage, and log odds of positive lymph nodes (LODDS) were independently associated with cancer‐specific survival in the training cohort. Both the C‐index and AUC values depicted in the nomogram were greater than 0.71. The calibration curve revealed that the nomogram's CSS prediction was consistent with the actual outcomes. The decision curve analysis suggested moderately positive net benefits. Based on the nomogram risk score, significant differences in survival between the high‐ and low‐risk groups were observed. Conclusions: Race, age, marital status, differentiation grade, T stage, and LODDS are independent predictors of CSS in patients with GCA after radical surgery. Our predictive nomogram constructed based on these variables demonstrated good predictive ability. The nomogram for postoperative patients with GCA we constructed has good predictive power and can help clinicians accurately assess patient prognosis and identify high‐risk patients to develop more personalized treatment plans. [ABSTRACT FROM AUTHOR]

Purpose: Our objective is to examine the independent prognostic risk factors for patients with Esophageal Cancer with Liver Metastasis (ECLM) and to develop a predictive model. Methods: In this study, clinical data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Cox regression analysis was employed to identify independent prognostic factors and construct nomograms based on the results of multivariate regression. The predictive performance of the nomograms was assessed using several methods, including the consistency index (C-index), calibration curve, time-dependent receiver-operating characteristic curve (ROC), and decision curve analysis (DCA). Additionally, Kaplan–Meier survival curves were generated to demonstrate the variation in overall survival between groups. Results: A total of 1163 ECLM patients were included in the study. Multivariate Cox analysis revealed that age, tumor differentiation grade, bone metastasis, therapy, and income were independently associated with overall survival (OS) in the training set. Subsequently, a prognostic nomogram was constructed based on these independent predictors. The C-index values were 0.739 and 0.715 in the training and validation sets, respectively. The area under the curve (AUC) values at 0.5, 1, and 2 years were all higher than 0.700. Calibration curves indicated that the nomogram accurately predicted OS. Decision curve analysis (DCA) showed moderately positive net benefits. Kaplan–Meier survival curves demonstrated significant differences in survival between high- and low-risk groups, which were divided based on the nomogram risk score. Conclusions: The nomogram we developed for ECLM patients has demonstrated good predictive capability, allowing clinicians to accurately evaluate patient prognosis and identify those at high risk, thereby facilitating the development of personalized treatment plans. [ABSTRACT FROM AUTHOR]

Purpose: Colon cancer is the most common malignant tumor in the intestine. Abnormal Carboxylesterases 3 (CES3) expression had been reported to be correlated to multiple tumor progression. However, the association among CES3 expression and prognostic value and immune effects in colonic adenocarcinoma (COAD) were unclear. Patients and methods: The transcription and expression data of CES3 and corresponding clinical information was downloaded from The Cancer Genome Atlas (TCGA). The CES3 protein expression and the prognostic value were verified based on tissue microarray data. The Cancer immune group Atlas (TCIA), Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and the GSE78220 immunotherapy cohort were used to forecast immunotherapy efficacy. Finally, a prognostic immune signature was constructed and verified. Results: Compared with normal colon tissues, the expression of mRNA and protein levels of CES3 were downregulated in tumor tissues. CES3 expression was associated with TIICs. Hihg-CES3 COAD patients had better efficacy of concurrent immunotherapy. CES3-related immune genes (CRIs) were identified and were then used to construct prognostic immune signature and had been successfully verified in GES39582. Conclusion: CES3 might be a potential immune-related gene and promising prognostic biomarker in COAD. [ABSTRACT FROM AUTHOR]

• NEK2 expression was upregulated in GC and was a predictor of a poor prognosis. • Multiple tumor pathways were hyperactivated in patients with high NEK2 expression. • NEK2 expression was negatively correlated with immune cell infiltration. • Patients with high NEK2 responded to drugs targeting cell cycle/DNA replication. • NEK2-derived genomic model and nomogram can better estimate survival probability. Gastric cancer (GC) is the most common form of gastrointestinal cancer, with a high mortality rate and limited treatment options. High levels of NEK2 are associated with malignant progression and a poor prognosis in several tumors; however, the role of NEK2 in GC remains unclear. We aimed to explore the potential role of NEK2 in the oncogenesis of GC and in the shaping of the tumor microenvironment (TME). The expression levels of NEK2 were analyzed using immunohistochemistry and real-time quantitative polymerase chain reaction. We found that NEK2 expression was upregulated in GC and was a predictor of a poor prognosis. Based on Kyoto Encyclopedia of Genes and Genomes pathway enrichment and gene set enrichment analyses, multiple tumor pathways were hyperactivated in patients with high NEK2 mRNA expression. Immunological characteristics indicated that NEK2 upregulation might lead to decreased immune cell infiltration and weakened immune activity in the cancer immunity cycle. Additionally, higher frequencies of amplifications and deletions were observed in the high NEK2 expression subpopulation. Based on the TME classification, patients with high expression of NEK2 were more susceptible to targeted therapy with drugs targeting the cell cycle and DNA replication. Following verification, a NEK2-derived genomic model reliably predicted the patient prognosis; A nomogram (radiation therapy, tumor/node/metastasis staging, and the NEK2-derived risk score) was used to better estimate an individual's survival probability. In summary, our findings indicate that NEK2 plays a vital role in the tumorigenesis of GC. [ABSTRACT FROM AUTHOR]

Purpose: This study was conducted to assess the relationship and prognostic significance between preoperative serum albumin to globulin ratio (AGR) and high-resolution computed tomography (HRCT) features of non-small cell lung cancer (NSCLC).Materials and Methods: Demographic parameters, laboratory values including AGR and other clinical variables were evaluated in 180 patients with NSCLC, and 72 of these patients had results of radiology parameters detected with HRCT [including emphysema, tumor disappearance rate (TDR), CT values and CT enhanced values of the tumor mass] were assessed for survival analyses. The 72 patients were divided into two groups: normal lung group and emphysema group. The discriminatory values for AGR between these two groups were assessed by Mann-Whitney U test The relationship between TDR and AGR in NSCLC patients was evaluated by Pearson correlation analysis.Results: In multivariate analysis, TDR (p = 0.033), AGR (p = 0.038), emphysema (p = 0.009), and N stage (P = 0.026) were independent predictors of overall survival (OS). AGR was higher in NSCLC patients without emphysema than NSCLC patients with emphysema (z = -2.979, P = 0.003). TDR demonstrated that there was a positive relationship with AGR (r = 0.307, p = 0.009). A nomogram with AGR, TDR, emphysema, and N stage was established to predict 5-year survival.Conclusions: There is a relationship between CT features and AGR in NSCLC. The integrative nomogram combined with CT images, clinical and hematologic features improved survival prediction in NSCLC patients, which offers a non-invasive, comprehensive, and convenient evaluation for individualized management of NSCLC patients. [ABSTRACT FROM AUTHOR]

Background: In the absence of practical and reliable predictors for whether the endocervical curettage (ECC) procedure should be performed, decisions regarding patient selection are usually based on the colposcopists' clinical judgment instead of evidence. We aimed to develop and validate a practical prediction model that uses available information to reliably estimate the need to perform ECC in patients suspected of having cervical lesions.Methods: In this retrospective study, 2088 patients who underwent colposcopy, colposcopically directed biopsy (CDB) and ECC procedures between September 2019 and September 2020 at the Second Hospital of Shanxi Medical University were included. The data were analyzed with univariate and multivariable logistic regression. Least absolute shrinkage and selection operator (LASSO) was used to select predictors for ECC positivity. The ECC prediction model was presented as a nomogram and evaluated in terms of discrimination and calibration. Furthermore, this model was validated internally with cross-validation and bootstrapping.Results: Significant trends were found for ECC positivity with increasing age (P = 0.001), menopause (P = 0.003), Human papillomavirus (HPV) status (P < 0.001), severity of ThinPrep Cytological Test (TCT) (P < 0.001), original squamous epithelium ectopia (P = 0.037) and colposcopy impression (P < 0.001) by multivariable logistic regression analysis. The ECC prediction model was developed based on the following predictors: age, menopause, symptom of contact bleeding, severity of TCT, HPV status, cervix visibility, original squamous epithelium ectopia, acetowhite changes and colposcopic impression. This model had satisfactory calibration and good discrimination, with an area under the receiver operator characteristic curve (AUC) of 0.869 (95% confidence interval 0.849 to 0.889).Conclusions: A readily applicable clinical prediction model was constructed to reliably estimate the probability of ECC positivity in patients suspicious of having cervical lesions, which may help clinicians make decisions regarding the ECC procedure and possibly prevent adverse effects. [ABSTRACT FROM AUTHOR]

Background: This study aimed to establish a novel nomogram prognostic model to predict death probability for non-small cell lung cancer (NSCLC) patients who received surgery..Methods: We collected data from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute in the United States. A nomogram prognostic model was constructed to predict mortality of NSCLC patients who received surgery.Results: A total of 44,880 NSCLC patients who received surgery from 2004 to 2014 were included in this study. Gender, ethnicity, tumor anatomic sites, histologic subtype, tumor differentiation, clinical stage, tumor size, tumor extent, lymph node stage, examined lymph node, positive lymph node, type of surgery showed significant associations with lung cancer related death rate (P < 0.001). Patients who received chemotherapy and radiotherapy had significant higher lung cancer related death rate but were associated with significant lower non-cancer related mortality (P<0.001). A nomogram model was established based on multivariate models of training data set. In the validation cohort, the unadjusted C-index was 0.73 (95% CI, 0.72-0.74), 0.71 (95% CI, 0.66-0.75) and 0.69 (95% CI, 0.68-0.70) for lung cancer related death, other cancer related death and non-cancer related death.Conclusions: A prognostic nomogram model was constructed to give information about the risk of death for NSCLC patients who received surgery. [ABSTRACT FROM AUTHOR]

Background: Thyroid carcinoma (THCA) is a common type of cancer worldwide, and its incidence has been increasing in recent years. Disulfidptosis, a recently defined form of metabolic-related regulated cell death (RCD), has been shown to play a sophisticated role in antitumor immunity. However, its mechanisms and functions are still poorly understood and the association between disulfidptosis and the prognosis of patients with papillary thyroid carcinoma remains to be elucidated. This study aims to investigate the connection between disulfidptosis and the prognosis of thyroid cancer, while also developing a prognostic index based on disulfidptosis genes. Materials and methods: We utilized 24 genes associated with disulfidptosis to create the classification and model. To gather data, we sourced gene expression profiles, somatic mutation information, copy number variation data, and corresponding clinical data from the TCGA database for patients with thyroid cancer. Additionally, we obtained single-cell transcriptome data GSE184362 from the Gene Expression Omnibus (GEO) database for further analysis. Results: In this study, we utilized 24 genes associated with disulfidptosis to identify two distinct groups with different biological processes using non-negative matrix factorization (NMF). Our findings showed that Cluster 1 is associated with chemokines, interleukins, interferons, checkpoint genes, and other important components of the immune microenvironment. Moreover, cluster 1 patients with high IPS scores may be more sensitive to immunotherapy. We also provide drug therapeutic strategies for each cluster patients based on the IC50 of each drug. The Enet model was chosen as the optimal model with the highest C-index and showed that patients with high risk had a worse prognosis and weak cell-to-cell interactions in THCA. Finally, we established a nomogram model based on multivariable cox and logistic regression analyses to predict the overall survival of THCA patients. Conclusion: This research provides new insight into the impact of disulfidptosis on THCA. Through a thorough examination of disulfidptosis, a new classification system has been developed that can effectively predict the clinical prognosis and drug sensitivity of THCA patients. [ABSTRACT FROM AUTHOR]

Controlling nutritional status (CONUT) score as an original nutritional assessment tool can be used to assess the prognosis of patients with a variety of malignancies. However, the predictive power of CONUT in extranodal natural killer/T cell lymphoma (ENKTL) patients has never been demonstrated. Our retrospective multicenter study aimed to explore the prognostic value of CONUT in newly diagnosed ENKTL. A total of 1085 newly diagnosed ENKTL patients between 2003 and 2021 were retrospectively retrieved. Cox proportional hazard model was used to explore the prognostic factors of overall survival (OS). The survival rate of ENKTL was evaluated using Kaplan-Meier analysis, and log-rank test was applied to the difference between groups. We investigated the prognostic performance of CONUT, the International Prognostic Index (IPI), the Korean Prognostic Index (KPI), and the Prognostic Index of Natural Killer Cell Lymphoma (PINK) using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). The median age at diagnosis for the whole cohort was 47 years, and the male to female ratio was 2.2:1. The 5-year OS for all patients was 72.2%. Multivariable analysis showed that CONUT, age, bone marrow involvement, ECOG PS score, and Chinese Southwest Oncology Group and Asia Lymphoma Study Group ENKTL stage were identified as independent predictive factors for OS. Based on multivariable results, a prognostic nomogram was developed. Subgroup analysis demonstrated that patients with severe malnutrition had poorest clinical outcome. In addition, ROC curves and DCA analysis proved that compared with IPI, KPI, and PINK models, the CONUT score-based nomogram showed a better prognostic predictive efficiency of ENKTL. CONUT could effectively stratify the prognosis of ENKTL and the proposed nomogram based on CONUT was an effective prognostic model for prediction. [ABSTRACT FROM AUTHOR]

Background: Ribosomal RNA N6-methyltransferase METTL5 was reported to catalyze m6A in 18S rRNA. We aimed to investigate the expression and prognostic features of METTL5 in gastric cancer (GC). Methods: In this study, 168 GC patients and their corresponding adjacent tissues were collected. Immunohistochemical staining was used to detect the expression of METTL5 protein. Univariate and multivariate Cox analysis were used to dertermine the prognostic role of METTL5 protein in GC, and a nomogram was constructed to evaluate GC patients' prognosis based on METTL5 expression. Data from TCGA and GEO database were also used to validate the prognostic value of METTL5 in GC patients on mRNA level. We further performed GSEA enrichment analysis to explore the possible function and related pathways related to METTL5. Results: METTL5 protein in gastric cancer tissues (GCTs) was significantly decreased compared with adjacent normal tissues (ANTs) and adjacent intestinal metaplasia tissues (AIMTs) (P < 0.001, respectively). Meanwhile, METTL5 expression was negatively correlated with clinicopathologic stage. According to multivariate Cox proportional hazards model analysis, METTL5 protein expression was a good independent predictor of GC prognosis (p < 0.05). Patients with high METTL5 expression had better prognosis. The nomogram constructed based on METTL5 expression could predict the prognosis of GC patients well. GSEA analysis showed that genes of METTL5 low expression group were enriched in some oncogenic signaling pathways such as ERBB, MAPK, JAK-STAT, Wnt, and mTOR, as well as some immune pathways, including Fc-gamma R mediated phagocytosis, Fc-epsilon Ri, chemokine, T cell receptor and B cell receptor signaling pathway. While the high expression group of METTL5 was mainly related to oxidative phosphorylation, nucleotide excision repair and mismatch repair. Conclusions: METTL5 protein was decreased in GCTs compared with AIMTs and ANTs, and it may be a potential prognostic biomarker in GC. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the prognostic performance of the revised 2018 FIGO staging system for cervical cancer. Methods: This retrospective multicenter study enrolled cervical cancer patients with 2009 FIGO Stage IA1–IIA2 who underwent surgeries between January 2006 and December 2017 in four tertiary hospitals. Patients were restaged according to the 2018 FIGO staging system by reviewing their medical data. Results: Of 3238 cervical cancer patients included, 1841 (56.9%) patients were restaged: 641 (34.9%) due to tumor size, 544 (29.5%) due to lymph node metastasis, 614 (33.4%) due to the inconsistency between pre‐ and postoperative assessments, and 42 due to the cancellation of invasion width in Stage IA. After restaging, a clear tendency of decreased recurrence‐free survival (RFS) and overall survival (OS) with increasing stage was observed. Multivariate Cox analysis showed that 2018 FIGO stage, parametrial involvement, and histology were independent prognostic factors for both OS and RFS (P < 0.05). Based on these factors, we established predictive nomograms with c‐indexes of 0.735 and 0.721, showing good predictive ability for cervical cancer. Conclusion: The revised 2018 FIGO staging system can better reflect the survival of cervical cancer patients. Based on it, we established a nomogram that can predict the prognosis of cervical cancer patients more precisely. Synopsis: The 2018 FIGO staging system for cervical cancer can better reflect the survival of patients; based on it, a prognostic nomogram was established. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate a pre-transcatheter arterial chemoembolization (TACE) MRI-based radiomics model for predicting tumor response in intermediate-advanced hepatocellular carcinoma (HCC) patients. Materials: Ninety-nine intermediate-advanced HCC patients (69 for training, 30 for validation) treated with TACE were enrolled. MRI examinations were performed before TACE, and the efficacy was evaluated according to the mRECIST criterion 3 months after TACE. A total of 396 radiomics features were extracted from T2-weighted pre-TACE images, and least absolute shrinkage and selection operator (LASSO) regression was applied to feature selection and model construction. The performance of the model was evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: The AFP value, Child-Pugh score, and BCLC stage showed a significant difference between the TACE response (TR) and non-TACE response (nTR) patients. Six radiomics features were selected by LASSO and the radiomics score (Rad-score) was calculated as the sum of each feature multiplied by the non-zero coefficient from LASSO. The AUCs of the ROC curve based on Rad-score were 0.812 and 0.866 in the training and validation cohorts, respectively. To improve the diagnostic efficiency, the Rad-score was further integrated with the above clinical indicators to form a novel predictive nomogram. Results suggested that the AUC increased to 0.861 and 0.884 in the training and validation cohorts, respectively. Decision curve analysis showed that the radiomics nomogram was clinically useful. Conclusion: The radiomics and clinical indicator-based predictive nomogram can well predict TR in intermediate-advanced HCC and can further be applied for auxiliary diagnosis of clinical prognosis. Key Points: • The therapeutic outcome of TACE varies greatly even for patients with the same clinicopathologic features. • Radiomics showed excellent performance in predicting the TACE response. • Decision curves demonstrated that the novel predictive model based on the radiomics signature and clinical indicators has great clinical utility. [ABSTRACT FROM AUTHOR]

Background: Malignant pleural mesothelioma (MPM) is a rare but aggressive tumor that originates from the pleura and has a poor prognosis. Eligible patients can benefit from surgery, but their survival is affected by many factors. Therefore, we created a graphic model that could predict the prognosis of surgically treated patients. Methods: We retrospectively analyzed data from the Surveillance, Epidemiology, and End Results database from 2004 to 2014 to identify the key factors affecting the prognosis of surgically treated MPM patients. On this basis we built a nomogram to predict survival. We then evaluated the performance of the nomogram in a validation cohort. Results: In a training cohort of 828 cases, independent prognostic factors, including age, gender, histological type, differentiation, N stage, chemotherapy, type of surgery, and lymph node dissection, were identified. We then developed a nomogram to evaluate individual patient survival. In Kaplan–Meier analysis, a higher score in the nomogram was associated with a worse prognosis. We also used a validation cohort consisting of 312 patients to evaluate the performance of the nomogram, which was well calibrated and had good discrimination ability, with concordance indices of 0.715 and 0.656 for the training and validation cohorts, respectively. Conclusion: This study has improved our understanding of resected MPM and shown that key factors, including age and histological type, are associated with overall survival. The nomogram is a reliable tool that can help clinicians turn individualized prediction into reality and maximize patient benefit by identifying the most beneficial treatment approach. [ABSTRACT FROM AUTHOR]