Prognostic Tool For Prediction Research Articles

A tumour-associated macrophage-based signature for deciphering prognosis and immunotherapy response in prostate cancer.

For the multistage progression of prostate cancer (PCa) and resistance to immunotherapy, tumour-associated macrophage is an essential contributor. Although immunotherapy is an important and promising treatment modality for cancer, most patients with PCa are not responsive towards it. In addition to exploring new therapeutic targets, it is imperative to identify highly immunotherapy-sensitive individuals. This research aimed to establish a signature risk model, which derived from the macrophage, to assess immunotherapeutic responses and predict prognosis. Data from the UCSC-XENA, GEO and TISCH databases were extracted for analysis. Based on both single-cell datasets and bulk transcriptome profiles, a macrophage-related score (MRS) consisting of the 10-gene panel was constructed using the gene set variation analysis. MRS was highly correlated with hypoxia, angiogenesis, and epithelial-mesenchymal transition, suggesting its potential as a risk indicator. Moreover, poor immunotherapy responses and worse prognostic performance were observed in the high-MRS group of various immunotherapy cohorts. Additionally, APOE, one of the constituent genes of the MRS, affected the polarisation of macrophages. In particular, the reduced level of M2 macrophage and tumour progression suppression were observed in PCa xenografts which implanted in Apolipoprotein E-knockout mice. The constructed MRS has the potential as a robust prognostic prediction tool, and can aid in the treatment selection of PCa, especially immunotherapy options.

ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions

IntroductionBreast cancer, a heterogeneous disease, is influenced by multiple genetic and epigenetic factors. The majority of prognostic models for breast cancer focus merely on the main effects of predictors, disregarding the crucial impacts of gene-gene interactions on prognosis. ObjectivesUsing DNA methylation data derived from nine independent breast cancer cohorts, we developed an independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions (ARTEMIS) with an innovative 3-D modeling strategy. ARTEMIS was evaluated for discrimination ability using area under the receiver operating characteristics curve (AUC), and calibration using expected and observed (E/O) ratio. Additionally, we conducted decision curve analysis to evaluate its clinical efficacy by net benefit (NB) and net reduction (NR). Furthermore, we conducted a systematic review to compare its performance with existing models. ResultsARTEMIS exhibited excellent risk stratification ability in identifying patients at high risk of mortality. Compared to those below the 25th percentile of ARTEMIS scores, patients with above the 90th percentile had significantly lower overall survival time (HR=15.43, 95 % CI: 9.57–24.88, P=3.06 × 10-29). ARTEMIS demonstrated satisfactory discrimination ability across four independent populations, with pooled AUC3-year = 0.844 (95 % CI: 0.805–0.883), AUC5-year = 0.816 (95 % CI: 0.775–0.857), and C-index = 0.803 (95 % CI: 0.776–0.830). Meanwhile, ARTEMIS had well calibration performance with pooled E/O ratio 1.060 (95 % CI: 1.038–1.083) and 1.090 (95 % CI: 1.057–1.122) for 3- and 5-year survival prediction, respectively. Additionally, ARTEMIS is a clinical instrument with acceptable cost-effectiveness for detecting breast cancer patients at high risk of mortality (Pt = 0.4: NB3-year = 0.019, NB5-year = 0.062; NR3-year = 69.21 %, NR5-year = 56.01 %). ARTEMIS has superior performance compared to existing models in terms of accuracy, extrapolation, and sample size, as indicated by the systematic review. ARTEMIS is implemented as an interactive online tool available at http://bigdata.njmu.edu.cn/ARTEMIS/. ConclusionARTEMIS is an efficient and practical tool for breast cancer prognostic prediction.

Unraveling the relationship between anoikis-related genes and cancer-associated fibroblasts in liver hepatocellular carcinoma

This study intended to determine the molecular subtypes of liver hepatocellular carcinoma (LIHC) on the strength of anoikis-related genes (ARGs) and to assess their prognostic value and prospective relationship with immune cell infiltration and cancer-associated fibroblasts (CAFs). Univariate Cox regression analysis yielded 66 prognosis-related ARGs and classified LIHC into two distinct subtypes, with subtype A demonstrating overexpression of most prognosis-related ARGs and a significant survival disadvantage. Furthermore, a reliable prediction model was developed using ARGs to evaluate the risk of LIHC patients. This model served as an independent prognostic indicator and a quantitative tool for clinical prognostic prediction. Additionally, subtype-specific differences in immune cell infiltration were observed, and the risk score was potentially linked to immune-related characteristics. Moreover, the study identified a significant association between CAF score and LIHC prognosis, with a low CAF score indicating a favorable patient prognosis. In conclusion, this study reveals the molecular mechanisms underlying the development and progression of LIHC and identifies potential therapeutic targets for the disease.

Identification of a ferroptosis-related gene signature for the prognosis of pediatric neuroblastoma.

Ferroptosis-related genes are correlated with the prognosis of patients with neuroblastoma (NB) remains unknown. This study aims to establish a prognostic ferroptosis-related gene model for predicting prognostic value in pediatric NB patients. The gene expression array and clinical characteristics of NB were downloaded from a public database. Correlations between ferroptosis-related genes and drug responses were analyzed by Childhood Cancer Therapeutics. The prognostic model was constructed by least absolute shrinkage and selection operator (LASSO) Cox regression and was validated in NB patients from the ICGC cohort. The survival analysis was performed by Cox regression analysis. single-sample gene set enrichment analysis (ssGSEA) was used to quantify the immune cell infiltration correlation. Overall, 70 genes were identified as ferroptosis-related differentially expressed genes (DEGs) from 247 samples. Then, 13 ferroptosis-related genes were correlated with OS in the univariate Cox regression analysis. Five prognostic ferroptosis-related DEGs (pFR-DEGs) (STEAP3, MAP1LC3A, ULK2, MTOR and TUBE1), which were defined as the intersection of DEGs and prognostic ferroptosis-related genes, were identified and utilized to construct the prognostic signature. The correlation between five pFR-DEGs and drug responses was analyzed, and the box plots indicated that MTOR gene expression was highest, suggesting that MTOR expression is related to progressive NB disease. The receiver operating characteristic (ROC) curve showed that the model had moderate predictive power. The survival analysis indicated that the high-risk group had poor overall survival (OS) (P=2.087×10-06). Univariate and multivariate analyses identified the risk score as a significant prognostic risk factor [P=0.003, hazard ratio (HR) =1.933]. Immune cell infiltration correlation analysis showed that the high-risk group was related to more immune cells. The present study indicated a difference in ferroptosis-related gene expression between low- and high-risk NB patients. The ferroptosis-related signature could serve as a prognostic prediction tool. Additionally, immune infiltration might play an important role in different risk groups for NB patients.

Radiomic score for lung nodules as a prognostic biomarker in locally advanced rectal cancer patients: A bi-institutional study.

Undetermined lung nodules are common in locally advanced rectal cancer (LARC) and lack precise risk stratification. This study aimed to develop a radiomic-based score (Rad-score) to distinguish metastasis and predict overall survival (OS) in patients with LARC and lung nodules. Retrospective data from two institutions (July 10, 2006-September 24, 2015) was used to develop and validate the Rad-score for distinguishing lung nodule malignancy. The prognostic value of the Rad-score was investigated in LARC cohorts, leading to the construction and validation of a clinical and radiomic score (Cli-Rad-score) that incorporates both clinical and radiomic information for the purpose of improving personalized clinical prognosis prediction. Descriptive statistics, survival analysis, and model comparison were performed to assess the results. The Rad-score demonstrated great performance in distinguishing malignancy, with C-index values of 0.793 [95% CI: 0.729-0.856] in the training set and 0.730 [95% CI: 0.666-0.874] in the validation set. In independent LARC cohorts, Rad-score validation achieved C-index values of 0.794 [95% CI: 0.737-0.851] and 0.747 [95% CI: 0.615-0.879]. Regarding prognostic prediction, Rad-score effectively stratified patients. Cli-Rad-score outperformed the clinicopathological information alone in risk stratification, as evidenced by significantly higher C-index values (0.735 vs. 0.695 in the internal set and 0.618 vs. 0.595 in the external set). CT-based radiomics could serve as a reliable and powerful tool for lung nodule malignancy distinction and prognostic prediction in LARC patients. Rad-score predicts prognosis independently. Incorporation of Cli-Rad-score significantly enhances the persionalized clinical prognostic capacity in LARC patients with lung nodules.

Validation of MELD 3.0 in patients with alcoholic liver cirrhosis using prospective KACLiF cohort.

The Model for End-Stage Liver Disease (MELD) is a reliable prognostic tool for short-term outcome prediction in patients with end-stage liver disease. MELD 3.0 was introduced to enhance the predictive accuracy. This study assessed the performance of MELD 3.0, in comparison to MELD and MELD-Na, in patients with alcoholic liver cirrhosis. This multicenter prospective cohort study comprised patients with alcoholic cirrhosis admitted for acute deterioration of liver function in the Republic of Korea between 2015 and 2019. This study compared the predictive abilities of MELD, MELD-Na, and MELD 3.0, for 30-day and 90-day outcomes, specifically death or liver transplantation, and explored the factors influencing these outcomes. A total of 1096 patients were included in the study, with a mean age of 53.3±10.4years, and 82.0% were male. The mean scores for MELD, MELD-Na, and MELD 3.0 at the time of admission were 18.7±7.2, 20.6±7.7, and 21.0±7.8, respectively. At 30 and 90days, 7.2% and 14.1% of patients experienced mortality or liver transplantation. The areas under the receiver operating characteristic curves for MELD, MELD-Na, and MELD 3.0 at 30days were 0.823, 0.820, and 0.828; and at 90days were 0.765, 0.772, and 0.776, respectively. Factors associated with the 90-day outcome included concomitant chronic viral hepatitis, prolonged prothrombin time, elevated levels of aspartate transaminase, bilirubin, and creatinine, and low albumin levels. MELD 3.0 demonstrated improved performance compared to previous models, although the differences were not statistically significant.

Abstract PO1-08-05: Using the CTS5 Score to Predict Late Recurrence in Male and Female Estrogen Receptor-Positive Breast Cancer Patients: a SEER database analysis of 65,729 cases

Abstract Purpose: The Clinical Treatment Score post-5 years (CTS5) score is a prognostic prediction tool to predict the risk of late distant recurrence in postmenopausal women with estrogen receptor-positive (ER+) breast cancer. Here we aimed to evaluate the prediction role of CTS5 in male breast cancer patients using the Surveillance, Epidemiology, and End Results (SEER) database. Methods: ER+ early male and female breast cancer patients from the SEER database 2010-2013 were included. Clinicopathological features and the CTS5 score between the gender groups were compared using chi-square test. Overall survival (OS) and breast cancer-specific survival (BCSS) were estimated by Kaplan-Meier plots and compared between the groups with Log-rank test and multivariate cox proportional hazard regression models was used to determine the relationship between the CTS5 score and breast cancer survival outcomes in different gender cohorts. Results: A total of 65,729 ER+ breast cancer patients were included in the study and 611 were men. Male breast cancer was more likely to be diagnosed later in life (>50 years old, 87.1% vs 73.6%, p < 0.001), with more aggressive biological features (grade Ⅱ 51.6% vs 47.2%, p < 0.001; grade Ⅲ 37.8% vs 28.3%, p < 0.001) , and higher tumor burden (stage Ⅱ 45.8% vs 32.9%, p < 0.001, stage Ⅲ 17.5% vs 10.5%, p < 0.001) when compared to female counterparts. More male patients were CTS5 intermediate- or high-risk than female patients (high 7.9% vs 6.3%, intermediate 41.1% vs 24.6%, low 51.1% vs 69.1%, p < 0.001). Patients were divided into 0-5 year and >5 year cohorts based on whether they had a BCSS event within 5 years. In the >5 year cohort, patients with CTS5 high- or intermediate- risk had worse survival outcomes compared with low-risk cases in male and postmenopausal female patients but not in premenopausal female patients. In the 0-5 year cohort, the CTS5 score was not predictive for disease recurrence in male or postmenopausal patients. Conclusion: Male breast cancer patients were more likely to have aggressive tumors and worse survival outcomes. The CTS5 score could be applied to predict breast cancer-specific mortality risk beyond 5 years in male ER+ breast cancer patients. Citation Format: Fenhua Wang, Fang Ren, Jian Qian, Yunxia Xu, Jiayi Wu, Yu Zong, Xidong Gu. Using the CTS5 Score to Predict Late Recurrence in Male and Female Estrogen Receptor-Positive Breast Cancer Patients: a SEER database analysis of 65,729 cases [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-08-05.

Prognostic significance of a pathological response in metastatic lymph nodes of patients with gastric cancer who underwent neoadjuvant chemotherapy followed by surgery.

To grade the pathological response of lymph nodes (LNs) to neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC) and investigate its prognostic significance. This retrospective study included 196 patients who underwent NAC, followed by radical gastrectomy for LAGC between January 2010 and October 2019. Pathological responses were evaluated based on the proportion of residual tumor cells within the tumor area in the primary tumor (PT) and LNs and included the following categories: 1a (0%), 1b (< 10%), 2 (10-50%), and 3 (> 50%). Among 166 patients with clinically node-positive disease, 38/27/39/62 were classified as having LN regression grade (LRG) 1a/1b/2/3, respectively. Compared to LN non-responders (LRG 2 or 3), LN responders (LRG 1a or 1b) had significantly higher 5-year overall survival (72.5% vs. 19.0%, P < 0.001) and recurrence-free survival rates (67.8% vs. 22.2%, P < 0.001), irrespective of PT response. Furthermore, a multivariate analysis revealed that the LN response was an independent risk factor for the overall survival (hazard ratio [HR] 0.417, 95% confidence interval [CI] 0.181-0.962, P = 0.040) and recurrence-free survival (HR 0.490, 95% CI 0.242-0.991, P = 0.047), but not the PT response (P > 0.05). The pathological LN response may be a reliable prognostic prediction tool in patients with LAGC who received NAC.

Integrated clinical and prognostic analyses of mTOR/Hippo pathway core genes in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most aggressive and dismal cancers globally. Emerging evidence has established that mTOR and Hippo pathways are oncogenic drivers of HCC. However, the prognostic value of these pathways in HCC remains unclear. In this study, we aimed to develop a gene signature utilizing the mTOR/Hippo genes for HCC prognostication. A multiple stage strategy was employed to screen, and a 12-gene signature based on mTOR/Hippo pathways was constructed to predict the prognosis of HCC patients. The risk scores calculated by the signature were inversely correlated with patient prognosis. Validation of the signature in independent cohort confirmed its predictive power. Further analysis revealed molecular differences between high and low-risk groups at genomic, transcriptomic, and protein-interactive levels. Moreover, immune infiltration analysis revealed an immunosuppressive state in the high-risk group. Finally, the gene signature could predict the sensitivity to current chemotherapeutic drugs. This study demonstrated that combinatorial mTOR/Hippo gene signature was a robust and independent prognostic tool for survival prediction of HCC. Our findings not only provide novel insights for the molecular understandings of mTOR/Hippo pathways in HCC, but also have important clinical implications for guiding therapeutic strategies.

Clinical and prognostic significance analysis of glycolysis-related genes in HNSCC.

Head and neck squamous cell carcinoma (HNSCC) represents one of the most malignant cancers worldwide, with poor survival. Experimental evidence implies that glycolysis/hypoxia is associated with HNSCC. In this study, we aimed to construct a novel glycolysis-/hypoxia-related gene (GHRG) signature for survival prediction of HNSCC. A multistage screening strategy was used to establish the GHRG prognostic model by univariate/least absolute shrinkage and selection operator (LASSO)/step multivariate Cox regressions from The Cancer Genome Atlas cohort. A nomogram was constructed to quantify the survival probability. Correlations between risk score and immune infiltration and chemotherapy sensitivity were explored. We established a 12-GHRG mRNA signature to predict the prognosis in HNSCC patients. Patients in the high-risk score group had a much worse prognosis. The predictive power of the model was validated by external HNSCC cohorts, and the model was identified as an independent factor for survival prediction. Immune infiltration analysis showed that the high-risk score group had an immunosuppressive microenvironment. Finally, the model was effective in predicting chemotherapeutic sensitivity. Our study demonstrated that the GHRG model is a robust prognostic tool for survival prediction of HNSCC. Findings of this work provide novel insights for immune infiltration and chemotherapy of HNSCC, and may be applied clinically to guide therapeutic strategies.

Monocyte-to-High-Density Lipoprotein-Cholesterol Ratio Predicts Prognosis of Hepatocellular Carcinoma in Patients with Metabolic-Associated Fatty Liver Disease.

The incidence of non-B and non-C hepatocellular carcinoma (NBNC-HCC) is increasing globally. Metabolically associated fatty liver disease (MAFLD) has been a contributing factor to this rising trend in NBNC-HCC incidence. The monocyte-to-high-density lipoprotein-cholesterol ratio (MHR) is a new prognostic marker that connects systemic inflammation with disorders of lipid metabolism. Therefore, MHR may be a potential prognostic predictor of patients with MAFLD-related HCC (MAFLD-HCC). This study aims to investigate the relationship between the MHR and prognosis of patients with MAFLD-HCC and construct a novel prognostic prediction tool for MAFLD-HCC. This retrospective study of patients with MAFLD-HCC included training (n = 112) and internal validation (n = 37) cohorts. Univariate and multivariate Cox proportional hazard regression analysis was conducted to identify independent risk factors of survival. A visual nomogram was constructed to assess the performance of the two groups. Furthermore, receiver operating characteristic (ROC) curves and calibration curves were used to verify the prognostic discriminative ability of this nomogram, even in the MHR, ALBI grade, and MHR-ALBI model. Univariate and multivariate analyses revealed that extrahepatic metastases, Vascular invasion, Barcelona staging B, C, D, elevated ALBI Grade 3, C-reactive protein (CRP), and MHR were independent risk factors for the prognosis of MAFLD-HCC. Moreover, calibration plots showed good discrimination and consistency when the significant factors were entered into the nomogram. Meanwhile, the MHR strongly correlated with the prognosis of cancer under a background of MAFLD-HCC, with a sensitivity of 88.89% and a specificity of 79.61%. Importantly, the performance of the MHR alone (AUC = 86.2) was not only superior to the ALBI grade (AUC = 63.8) but was comparable to the combination of MHR and ALBI (AUC = 88.5). The novel nomogram demonstrated good value in predicting the overall survival of patients with MAFLD-HCC. The MHR may be a potential predictor of prognosis.

Effective prognostic risk model with cuproptosis-related genes in laryngeal cancer

ObjectiveLaryngeal cancer, characterized by high recurrence rates and a lack of effective biomarkers, has been associated with cuproptosis, a regulated cell death process linked to cancer progression. In this study, we aimed to explore the roles of cuproptosis-related genes in laryngeal cancer and their potential as prognostic markers and therapeutic targets. MethodsWe collected comprehensive data from The Cancer Genome Atlas and Gene Expression Omnibus databases, including gene expression profiles and clinical data of laryngeal cancer patients. Using clustering and gene analysis, we identified cuproptosis-related genes with prognostic significance. A risk model was constructed based on these genes, categorizing patients into high- and low-risk groups for outcome comparison. Univariate and multivariate analyses were conducted to identify independent prognostic factors, which were then incorporated into a nomogram. Gene Set Enrichment Analysis was employed to explore pathways distinguishing high- and low-risk groups. ResultsOur risk model, based on four genes, including transmembrane 2, dishevelled binding antagonist of β-catenin 1, stathmin 2, and G protein-coupled receptor 173, revealed significant differences in patient outcomes between high- and low-risk groups. Independent prognostic factors were identified and integrated into a nomogram, providing a valuable tool for prognostic prediction. Gene Set Enrichment Analysis uncovered up-regulated pathways specifically associated with high-risk patient samples. ConclusionThis study highlights the potential of cuproptosis-related genes as valuable prognostic markers and promising therapeutic targets in the context of laryngeal cancer. This research sheds light on new avenues for understanding and managing this challenging disease. Level of evidenceLevel 4.

Impact of disulfidptosis-associated clusters on breast cancer survival rates and guiding personalized treatment.

Breast cancer (BC) poses a serious threat to human health. Disulfidptosis is a recently discovered form of cell death associated with cancer prognosis and progression. However, the relationship between BC and disulfidptosis remains unclear. We integrated single-cell sequencing and transcriptome sequencing in BC to assess the abundance and mutation status of disulfidptosis-associated genes (DAGs). Subsequently, we clustered the samples based on DAGs and constructed a prognostic model associated with disulfidptosis. Additionally, we performed pathway enrichment, immune response, and drug sensitivity analyses on the model. Finally, we validated the prognostic genes through Immunohistochemistry (IHC). The single-cell analysis identified 21 cell clusters and 8 cell types. By evaluating the abundance of DAGs in different cell types, we found specific expression of the disulfidoptosis core gene SLC7A11 in mesenchymal stem cells (MSCs). Through unsupervised clustering of DAGs, we identified two clusters. Utilizing differentially expressed genes from these clusters, we selected 7 genes (AFF4, SLC7A11, IGKC, IL6ST, LIMD2, MAT2B, and SCAND1) through Cox and Lasso regression to construct a prognostic model. External validation demonstrated good prognostic prediction of our model. BC patients were stratified into two groups based on riskscore, with the high-risk group corresponding to a worse prognosis. Immune response analysis revealed higher TMB and lower TIDE scores in the high-risk group, while the low-risk group exhibited higher CTLA4/PD-1 expression. This suggests that both groups may respond to immunotherapy, necessitating further research to elucidate potential mechanisms. Drug sensitivity analysis indicated that dasatinib, docetaxel, lapatinib, methotrexate, paclitaxel, and sunitinib may have better efficacy in the low-risk group. Finally, Immunohistochemistry (IHC) validated the expression of prognostic genes, demonstrating higher levels in tumor tissue compared to normal tissue. Our study has developed an effective disulfidptosis-related prognostic prediction tool for BC and provides personalized guidance for the clinical management and immunotherapy selection of BC patients.

A novel prognostic and therapeutic target biomarker based on complement-related gene signature in gastric cancer.

Gastric cancer (GC) is one of the most prevalent cancer types that reduce human life expectancy. The current tumor-node-metastasis (TNM) staging system is inadequate in identifying higher or lower risk of GC patients because of tumor heterogeneity. Research shows that complement plays a dual role in the tumor development and progression of GC. We downloaded GC data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A complement-related risk signature was constructed through bioinformatics analysis. Subsequently, the predictive ability of this signature was validated with GSE84437 dataset, and a nomogram integrating risk score and common clinical factors was established. Besides, we evaluated the association of risk score with the immune and stromal cell infiltration in TCGA. Furthermore, immunotherapy response prediction and drug susceptibility analysis were conducted to access the ability of the risk signature in predicting the therapeutic effect. A complement-related gene (CRG) signature, based on six genes (SPLG, C9, ITIH1, ZFPM2, CD36, and SERPINE1), was established. In both the training and validation sets, the overall survival of GC patients in the high-risk group was lower than that of the low-risk group, and the nomogram to predict the 1-, 2-, and 3-year survival rates of GC patients was developed. In addition, CIBERSORT algorithm showed the high-risk patients had higher levels of immune cell infiltration than low-risk patients, and the ESTIMATE results implied that the high-risk group had more stromal component in tumor microenvironment. Besides, compared to the low-risk group, there were higher expressions of most immune checkpoint genes and HLA genes in the high-risk group, and the high-risk patients showed higher sensitivity to the chemotherapy and targeted drugs (axitinib, dasatinib, pazopanib, saracatinib, sunitinib and temsirolimus). The novel CRG signature may act as a reliable, efficient tool for prognostic prediction and treatment guidance in future clinical practice.

Deep learning radiomics analysis based on computed tomography for survival prediction in gastric neuroendocrine neoplasm: a multicenter study.

Survival prediction is crucial for patients with gastric neuroendocrine neoplasms (gNENs) to assess the treatment programs and may guide personalized medicine. This study aimed to develop and evaluate a deep learning (DL) radiomics model to predict the overall survival (OS) in patients with gNENs. The retrospective analysis included 162 consecutive patients with gNENs from two hospitals, who were divided into a training cohort, internal validation cohort (The First Affiliated Hospital of Zhengzhou University; n=108), and an external validation cohort (The Henan Cancer Hospital; n=54). DL radiomics analysis was applied to computed tomography (CT) images of the arterial phase and venous phase, respectively. Based on pretreatment CT images, two DL radiomics signatures were developed to predict OS. The combined model incorporating the radiomics signatures and clinical factors was built through the multivariable Cox proportional hazards (CPH) method. The combined model was visualized into a radiomics nomogram for individualized OS estimation. Prediction performance was assessed with the concordance index (C-index) and the Kaplan-Meier (KM) estimator. The DL-based radiomics signatures based on two phases were significantly correlated with OS in the training (C-index: 0.79-0.92; P<0.01), internal validation (C-index: 0.61-0.86; P<0.01), and external validation (C-index: 0.56-0.75; P<0.01) cohorts. The combined model integrating radiomics signatures with clinical factors showed a significant improvement in predictive performance compared to the clinical model in the training (C-index: 0.86 vs. 0.80; P<0.01), internal validation (C-index: 0.77 vs. 0.71; P<0.01), and external validation (C-index: 0.71 vs. 0.66; P<0.01) cohorts. Moreover, the combined model classified patients into high-risk and low-risk groups, and the high-risk group had a shorter OS compared to the low-risk group in the training cohort [hazard ratio (HR) 3.12, 95% confidence interval (CI): 2.34-3.93; P<0.01], which was validated in the internal (HR 2.51, 95% CI: 1.57-3.99; P<0.01) and external validation cohort (HR 1.77, 95% CI: 1.21-2.59; P<0.01). DL radiomics analysis could serve as a potential and noninvasive tool for prognostic prediction and risk stratification in patients with gNENs.

PTPRC functions as a prognosis biomarker in the tumor microenvironment of cutaneous melanoma

Cutaneous melanoma is one of the most malignant types of skin cancer, with an extremely poor prognosis. Immune cells infiltrated in the tumor microenvironment (TME) affects melanoma initiation, progression, prognosis and immunotherapy strategies in melanoma. The potential utility of TME-related genes as a prognostic model for melanoma and as a predictor of immunotherapeutic response merits further exploration. In this study, we determined that an immune-related gene, protein tyrosine phosphatase receptor type C (PTPRC), was positively correlated with the positive prognosis of melanoma patients. Integration of this gene with TNM classification created a predictive model that showed better performance in determining overall survival than others. PTPRC expression was positively correlated with the levels of immune checkpoint molecules, and PTPRC knockdown significantly enhanced the migration, invasion, and proliferation of melanoma cells. Finally, immunohistochemical results from HPA and Real-time quantitative PCR of clinical tissues confirmed that PTPRC expression was higher in melanoma than in normal skin. In conclusion, PTPRC served as a potential predictor of survival and response to immunotherapy in melanoma patients. The risk model combining the PTPRC and TNM classifications holds the potential to be a promising tool for prognostic prediction of cutaneous melanoma. This will help in the effective clinical management of melanoma patients.

Progression of Disease within 2 Years Predicts Poor Survival in Patients with Classic Hodgkin Lymphoma

Background: Early progression within 24 months (POD24) of initial immunochemotherapy was repeatedly confirmed to be associated with poor survival in follicular, diffuse large B-cell and mantle-cell lymphomas, identifying its high-risk subgroup with different disease biology (Casulo C, JCO 2015; Maurer JM, JCO 2015; Bond DA, Blood Adv 2021). Little is known about the POD24 incidence and clinical impact in Hodgkin lymphoma (HL) patients, and current prognostic schemes have been designed with longer survival endpoints (Rodday AM, JCO 2022). Methods: This single-center study comprised patients (pts) with classic HL (cHL) treated with frontline chemotherapy prospectively enrolled in the Czech Hodgkin Lymphoma Study Group database in 1993-2020 (database lock was done on 15. June 2023). Early event was defined as progression, relapse, or death related to progressive HL within 24 months after the date of diagnosis. Overall (OS) and progression-free survival (PFS) were calculated from the date of diagnosis. To evaluate the association between early POD and OS from a risk-defining event, that is, survival from the time of POD for early progressors (POD24 group) or from 2 years after diagnosis for the reference group (noPOD24 group), was calculated. In total, we have identified 417 pts with cHL diagnosed from 1993 to the end of 2020. Ten pts have been excluded from the analysis because of early death (&amp;lt;24 months) with no recorded disease progression. Results: The analyzed cohort consisted of 407 pts: 205 females and 202 males with a median age of 35 years (min 16, max 78) with limited disease (stage I-II) in 58% and advanced disease (stage III-IV) in 42%. Histological subtypes were as follows: nodular sclerosis in 53.1%, mixed cellularity in 35.6%, lymphocyte-rich in 3.7%, and lymphocyte-depleted in 0.3%. Subtype was not available in 7.3% of the pts. Extranodal disease and B symptoms were present in 25% and 49%, respectively. 42% of patients had high-risk IPS of 3 or more points. Patients received BEACOPP in 55.5%, Stanford V in 23%, ABVD in 12%, and COPP in 7%. Other treatments were used in 2,5% of the cases. The complete and partial remission rates after frontline treatment were 88.7% and 8.6%, respectively, and only 3% of the pts did not respond to therapy. After a median follow-up of 12.1 years, a cumulative incidence of progression/relapse is 0.14 (95%CI 0,10-0.18). The POD24-event occurred in 36 pts (8.8%) and 38 (9.3%) patients relapsed after 2 years. The entire group's 5-y PFS and 5-y OS reached 81.0% (95% CI 0.77-0.85) and 91.4% (95% CI 88.6-0.94), respectively. The 5-y OS since the risk-defining event was 48.1% (95% CI 31.4-64.8) and 94.2% (95% CI 0.92-96.7) in the POD24 vs noPOD24 group (p&amp;lt;0.001, HR=5.35), Fig 1. When comparing 12 baseline clinical and laboratory characteristics between POD24 (n=35) with noPOD24 controls (n=371), we have found no major differences (Tab 1). There was a trend in more frequent POD24 in the patients with massive mediastinal tumor (p=0.07) and those with advanced clinical stage (p=0.047). The spectrum of the induction regimes was distributed equally (p=0.26). Conclusions: Early progression of the disease is rare; however, a catastrophic event in HL resulting in more than five times higher risk of death. Baseline disease characteristics - besides the clinical stage - do not predict the risk of later POD24 events. Large independent validation may enhance the validity of these data and help create a prognostic tool for POD24 prediction. This event should be considered an important endpoint in Hodgkin lymphoma clinical trials in the future. Acknowledgements: Supported by MH CZ - DRO (FNOL, 00098892), IGA_LF_2023_005 and AZV NU22-03-00182.

A Nucleotide Metabolism-Related Gene Signature for Risk Stratification and Prognosis Prediction in Hepatocellular Carcinoma Based on an Integrated Transcriptomics and Metabolomics Approach.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide. The in-depth study of genes and metabolites related to nucleotide metabolism will provide new ideas for predicting the prognosis of HCC patients. This study integrated the transcriptome data of different cancer types to explore the characteristics and significance of nucleotide metabolism-related genes (NMGRs) in different cancer types. Then, we constructed a new HCC classifier and prognosis model based on HCC samples from TCGA and GEO, and detected the gene expression level in the model through molecular biology experiments. Finally, nucleotide metabolism-related products in serum of HCC patients were examined using untargeted metabolomics. A total of 97 NMRGs were obtained based on bioinformatics techniques. In addition, a clinical model that could accurately predict the prognostic outcome of HCC was constructed, which contained 11 NMRGs. The results of PCR experiments showed that the expression levels of these genes were basically consistent with the predicted trends. Meanwhile, the results of untargeted metabolomics also proved that there was a significant nucleotide metabolism disorder in the development of HCC. Our results provide a promising insight into nucleotide metabolism in HCC, as well as a tailored prognostic and chemotherapy sensitivity prediction tool for patients.

Personalized prognostic prediction tool for high-grade neuroendocrine cervical cancer: a SEER database analysis and single-center validation

PurposeCervical high-grade neuroendocrine carcinoma (CHGNEC) is a rare but highly aggressive cancer. The purpose of this study is to develop a prognostic nomogram that can accurately predict the outcomes for CHGNEC patients.MethodsWe analyzed clinical data from the Surveillance, Epidemiology, and End Results (SEER) database of CHGNEC patients, including small-cell neuroendocrine carcinoma (SCNEC) and large-cell neuroendocrine carcinoma (LCNEC). We investigated patient characteristics and prognosis, and developed a prognostic nomogram model for cancer-specific survival in CHGNEC patients. External validation was conducted using real clinical cases from our hospital.ResultsOur study included 306 patients from SEER database, with a mean age of 49.9 ± 15.5 years. Most of the patients had SCNEC (86.9%). Among them, 170 died from the disease, while 136 either survived or died from other causes. Our final predictive model identified age at diagnosis, stage 1 status, stage 4 status, T1, N0, and surgery of the primary site as independent prognostic factors for CHGNEC. We validated our model using a group of 16 CHGNEC patients who underwent surgery at our center. The external validation showed that the prognostic nomogram had excellent discriminative ability, with an area under the receiver operating characteristic curve (AUC) of 0.76 (95% CI 0.49–1.00) for the prediction of 3-year cancer-specific survival (CSS) and an AUC of 0.85 (95% CI 0.62–1.00) for the prediction of 5-years CSS. The random survival forest model achieved an AUC of 0.80 (95% CI 0.56–1.00) for 3-years CSS and 0.91 (95% CI 0.72–1.00) for 5-years CSS, indicating its adequacy in predicting outcomes for CHGNEC patients.ConclusionOur study provides an excellent nomogram for predicting the prognosis of CHGNEC patients. The prognostic nomogram can be a useful tool for clinicians in identifying high-risk patients and making personalized treatment decisions.

Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients.

Recent clinical research describes a subset of glioblastoma patients that exhibit REP prior to the start of radiation therapy. Current literature has thus far described this population using clinicopathologic features. To our knowledge, this study is the first to investigate the potential of conventional radiomics, sophisticated multi-resolution fractal texture features, and different molecular features (MGMT, IDH mutations) as a diagnostic and prognostic tool for prediction of REP from non-REP cases using computational and statistical modeling methods. The radiation-planning T1 post-contrast (T1C) MRI sequences of 70 patients are analyzed. An ensemble method with 5-fold cross-validation over 1000 iterations offers an AUC of 0.793 ± 0.082 for REP versus non-REP classification. In addition, copula-based modeling under dependent censoring (where a subset of the patients may not be followed up with until death) identifies significant features (p-value < 0.05) for survival probability and prognostic grouping of patient cases. The prediction of survival for the patients' cohort produces a precision of 0.881 ± 0.056. The prognostic index (PI) calculated using the fused features shows that 84.62% of REP cases fall under the bad prognostic group, suggesting the potential of fused features for predicting a higher percentage of REP cases. The experimental results further show that multi-resolution fractal texture features perform better than conventional radiomics features for prediction of REP and survival outcomes.