Prognostic Stratification System Research Articles

Latest Insights and Therapeutic Advances in Myelodysplastic Neoplasms.

Myelodysplastic syndromes/neoplasms (MDSs) encompass a range of hematopoietic malignancies, commonly affecting elderly individuals. Molecular alterations in the hematopoietic stem cell compartment drive disease pathogenesis. Recent advancements in genomic profiling have provided valuable insights into the biological underpinnings of MDSs and have expanded therapeutic options, particularly for specific molecularly defined subgroups. This review highlights the diagnostic principles, classification updates, prognostic stratification systems, and novel treatments, which could inform future clinical trials and enhance the management of adult MDS patients, particularly for specific molecularly defined subgroups.

Predicting survival of patients with advanced hepatocellular carcinoma receiving combination targeted immunotherapy: an evaluation of volumetric imaging parameters.

To evaluate the potential of volumetric imaging in predicting survival of advanced hepatocellular carcinoma (HCC) patients receiving immunotherapy. Retrospective analysis included 40 patients with advanced HCC who received targeted immunotherapy. Baseline and follow-up contrast-enhanced abdominal computed tomography (CT) scans were analyzed. The largest tumor was chosen as the index lesion. Viable tumor volume (qViable) and percentage tumor viability (%Viability) were calculated. Response Evaluation Criteria in Solid Tumors (RECIST) and Tumor volume change after treatment (qRECIST) were measured. Associations with overall survival (OS) were assessed. Cox regression analysis assessed the association between variables and overall survival (OS). A new prognostic stratification system was attempted to categorize patients based on significant predictors of OS. Patients with a baseline %viability > 69% and %viability reduction ≥ 8% were classified as better prognosis. Patients were stratified into better, intermediate and worse prognosis groups based on baseline %viability > 69% and ≥ 8% %viability reduction (better prognosis); baseline %viability ≤ 69% and < 8% %viability reduction (worse prognosis); remainder were intermediate prognosis. Patients with baseline %Viability > 69% and %Viability reduction ≥ 8% showed significantly higher OS. Multivariate analysis confirmed %Viability and %Viability reduction as significant predictors of OS. A prognostic stratification system using these parameters stratified patients into better, intermediate and worse prognosis groups, with the better prognosis showing highest OS. Most patients (97.5%) had stable disease by RECIST while the prognostic model re-classified 47.5% as better prognosis, 37.5% intermediate prognosis, and 15% worse prognosis. Volumetric parameters of %Viability and %Viability reduction predict OS in HCC patients undergoing immunotherapy.

Clinical outcomes of allogeneic hematopoietic stem cell transplantation for chronic myelomonocytic leukemia

Chronic myelomonocytic leukemia (CMML) is a clonal disease derived from bone marrow hematopoietic stem cells, with a poor prognosis. Allogeneic hematopoietic stem cell transplantation (allo- HSCT) is one of the curable methods for CMML. The outcome of patient transplantation is influenced by various factors such as disease characteristics and comorbidities. Based on the existing prognostic stratification system, screening suitable CMML patients for transplantation and early transplantation is beneficial for their long-term survival. Doctors can evaluate the survival status of CMML patients after transplantation based on the newly developed transplant prognosis model and make targeted medical decisions.

A Novel Scoring System Using Geriatric Nutritional Risk Index, C-Reactive Protein, and Disease Status Predicts Survival after Second Allogeneic Stem Cell Transplantation

Introduction: Second allogeneic stem cell transplantation (SCT2) is generally associated with poorer overall survival (OS) and non-relapse mortality (NRM) than first SCT (SCT1). However, some patients survive long after SCT2, and the number of SCT2 cases increases yearly. Thus, a prognostic stratification system is required to select optimally eligible patients for SCT2. We have previously shown that the geriatric nutritional risk index (GNRI) predicts OS and NRM for SCT2 (Ann Haematol. 2020;99:1655). In this study, using a multicenter database, we aimed to validate the importance of the GNRI and establish a scoring system that can more accurately stratify the prognosis after SCT2. Methods: We retrospectively analyzed patients with hematopoietic malignancies who underwent SCT2 between 2004 and 2020 at transplant centers of the Kanto Study Group for Cell Therapy (KSGCT), using the KSGCT database. The primary endpoint was 3-year OS. The secondary endpoints were disease-free survival (DFS), NRM, and cumulative incidence of relapse (CIR). The GNRI was defined as 1.489×albumin(g/L) + 41.7×(weight/ideal weight) (Am J Clin Nutr. 2005;82:777). We chose a GNRI cutoff value of 92.0 according to our previous report. We defined acute leukemia and malignant lymphoma in the first or second complete remission, CML in the 1st or 2nd chronic phase, and MDS with refractory anemia as standard risk, and other malignant diseases as high risk. Results: Among 722 patients who underwent SCT2, we were able to calculate the GNRI and perform a detailed analysis for 376. The median patient age was 44 years (range: 16-69). The underlying diseases were AML (n=207, 55.1%), ALL/LBL (n=107, 28.5%), MDS (n=33, 8.8%), CML (n=11, 2.9%), and other (n=18, 4.8%). The median follow-up period for survivors was 1375 days (range: 223-4213). Three years after SCT2, the OS, DFS, NRM, and CIR were 26.6%, 21.7%, 40.4%, and 37.9%, respectively. Based on previous research, we divided patients into two groups: low GNRI ( ≤92) (n=162, 43.1%) and high GNRI (&amp;gt;92) (n=214, 56.9%). In the low GNRI group, OS was better, NRM was lower, and CIR was comparable to that in the high GNRI group (3y-OS 36.0% vs. 14.3%, p&amp;lt;0.001; 3y-NRM 29.5% vs. 55.2%, p&amp;lt;0.001; 3y-CIR 33.4% vs. 41.3%, p=0.146). Through univariate analysis and stepwise multivariate regressions on the dependent variables for OS, low GNRI (hazard ratio [HR] 1.39, 95%CI 1.05-1.85, p=0.024), high C-reactive protein (CRP) (&amp;gt;0.5 mg/dl; HR 1.96, 95%CI 1.16-2.63, p&amp;lt;0.001), and disease status at SCT2 (HR 1.54, 95%CI 1.16-2.04, p=0.003) were associated with poor OS. Likewise, low GNRI (HR 1.71, 95%CI 1.18-2.47, p=0.0045), high CRP (HR 1.69, 95%CI 1.20-2.38, p=0.0029), high HCT-CI (≥3) (HR 1.58, 95%CI 1.12-2.23, p=0.0092) were unfavorable factors for NRM, while the shorter interval from SCT1 to SCT2 (HR 1.53, 95%CI 1.10-2.13 p=0.011) and high-risk disease status at SCT2 (HR 1.62, 95%CI 1.16-2.27, p=0.0046) were associated with high CIR. To predict OS after SCT2, we developed a scoring system consisting of three factors: low GNRI, high CRP level, and high-risk disease status. As the HRs for OS were comparable, a score of 1 was assigned to each factor. OS at 3 years was 50.7%, 29.5%, and 11.1% for patients with low (score 0, n=89), intermediate (score 1, n=106), and high (scores 2 and 3, n=174), respectively (p&amp;lt;0.001, Fig 1). Differences between groups were revealed using the Holm-Bonferroni correction. Conclusion: Low GNRI, high CRP level, and high-risk disease status were significantly associated with inferior OS after SCT2. The scoring system with these factors stratified the entire cohort into three risk groups including low risk with over 50% OS, and high risk with a very limited chance of survival.

Risk stratification for overall survival and recurrence-free survival after R0 resection for solitary intrahepatic mass-forming cholangiocarcinoma based on preoperative MRI and clinical features

PurposeThis study aimed to establish two nomograms for predicting overall survival (OS) and recurrence-free survival (RFS) in patients with solitary intrahepatic mass-forming cholangiocarcinoma (IMCC) based on preoperative magnetic resonance imaging (MRI) features. MethodsThis retrospective study included 120 consecutive patients who were diagnosed with solitary IMCC. Preoperative MRI and clinical features were collected. Based on the univariate and multivariate Cox regression analyses, two nomograms were constructed to predict OS and RFS, respectively. The effective performance of the nomograms was evaluated using concordance index (C-index). The prognostic stratification systems for OS and RFS were developed and used to classify patients into high- and low-risk groups. ResultsSuspicious lymph nodes, arterial phase (AP) enhancement patterns, and bile duct dilatation were independent predictors of OS, while suspicious lymph nodes, AP enhancement patterns, and necrosis were independent predictors of RFS. The nomograms achieved the C-index values of 0.705/0.710 for OS and 0.721/0.759 for RFS in the development/validation cohorts, which were significantly higher than those of the T and TNM stages (P < 0.05). Patients were stratified into high- and low-risk groups, the 1-year OS and RFS rates of high-risk patients were poorer than those of patients with low-risk in the development cohort (OS: 93.5% vs 76.3%, P < 0.001; RFS: 74.5% vs 22.4%, P < 0.001). Similar results were observed in the validation cohort. ConclusionsTwo nomograms were constructed based on preoperative MRI features in patients with solitary IMCC for predicting the OS and RFS and facilitate further prognostic stratification.

Estimating Microvascular Invasion in Patients with Resectable Multinodular Hepatocellular Carcinoma by Using Preoperative Contrast-Enhanced MRI: Establishment and Validation of a Risk Score.

To determine the preoperative clinicoradiological factors to predict microvascular invasion (MVI) in patients with resectable multinodular hepatocellular carcinoma (mHCC), and further to establish and validate a stratified risk scoring system. Two hundred and seventy-three patients with pathologically confirmed mHCC (≥2 lesions) without major vascular invasion and biliary tract tumor thrombosis, who underwent preoperative contrast-enhanced MRI and hepatectomy, were consecutively enrolled (training/validation cohort=193/80). Preoperative clinicoradiological variables were collected and analyzed. The multivariable logistic regression was performed to determine the independent predictors of MVI and create a risk score system. The C-index, calibration curve and decision curve were used to evaluate the performance of the risk score. A risk score-based prognostic stratification system was performed in mHCC patients. The risk score system was further verified in the validation cohort. AFP > 400 ng/mL, presence of satellite nodule, mosaic architecture and increased total tumor diameter were independent predictors of MVI while fat in mass was an independent protective factor of MVI. The risk score yielded satisfactory C-index values (training/validation cohort: 0.777/0.758) and fitted well in calibration curves. Decision curve analysis further confirmed its clinical utility. Based on the risk score, mHCC patients were stratified into high-/low-MVI-risk subgroups with significantly different recurrence-free survival (both P < 0.001). The presented risk score incorporating clinicoradiological parameters could stratify mHCC patients into high-risk and low-risk subgroups and predict prognosis in patients with resectable mHCC.

Cuproptosis-Related Gene Signature Contributes to Prognostic Prediction and Immunosuppression in Multiple Myeloma.

Cuproptosis is a type of programmed cell death triggered by accumulation of intracellular copper which was considered closely related to tumor progression. The study of cuproptosis in multiple myeloma (MM) is however limited. To determine the prognostic significance of cuproptosis-related gene signature in MM, we interrogated gene expression and overall survival with other available clinical variables from public datasets. Four cuproptosis-related genes were included to establish a prognostic survival model by least absolute shrinkage and selection operator (LASSO) Cox regression analysis, which showed a good performance on prognosis prediction in both training and validation cohorts. Patients with higher cuproptosis-related risk score (CRRS) exhibited worse prognosis compared with lower risk score. Survival prediction capacity and clinical benefit were elevated after integrating CRRS to existing prognostic stratification system (International Staging System, ISS or Revised International Staging System, RISS) both on 3-year and 5-year survival. Based on CRRS groups, functional enrichment analysis and immune infiltration in bone marrow microenvironment revealed correlation between CRRS and immunosuppression. In conclusion, our study found that cuproptosis-related gene signature is an independent poor prognostic factor and functions negatively on immune microenvironment, which provides another perspective on prognosis assessment and immunotherapy strategy in MM.

An MRI-Based Prognostic Stratification System for Medical Decision-Making of Multinodular Hepatocellular Carcinoma Patients Beyond the Milan Criteria.

The suitability of hepatectomy among patients with multinodular hepatocellular carcinoma (MHCC) beyond the Milan criteria remains controversial. There is a need for a reliable risk stratification tool among these patients for the selection of ideal candidates of curative resection. To determine the clinicoradiological prognostic factors for patients with MHCC beyond the Milan criteria to further develop a stratification system. Retrospective. 176 patients with pathologically confirmed MHCC beyond the Milan criteria. The 1.5 T scanner, including T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. Conventional MRI features and preoperative laboratory data including aspartate aminotransferase (AST) and α-fetoprotein (AFP) were collected and analyzed. Two nomograms incorporating clinicoradiological variables were independently constructed to predict recurrence-free survival (RFS) and overall survival (OS) with Cox regression analyses and verified with 5-fold cross validation. Based on the nomograms, two prognostic stratification systems for RFS and OS were further developed. The Cohen's kappa/intraclass correlation coefficient, C-index, calibration curve, Kaplan-Meier curve, log-rank test. A P value <0.05 was considered statistically significant. AST > 40 U/L, increased tumor burden score, radiological liver cirrhosis and nonsmooth tumor margin were independent predictors for poor RFS, while AST > 40 U/L, AFP > 400 ng/mL and radiological liver cirrhosis were independent predictors for poor OS. The two nomograms demonstrated good discrimination performance with C-index of 0.653 (95% confidence interval [CI], 0.602-0.794) and 0.685 (95% CI, 0.623-0.747) for RFS and OS, respectively. The 5-fold cross validation further validated the discrimination capability of the nomograms. Based on the nomogram models, MHCC patients beyond the Milan criteria were stratified into low-/medium-/high-risk groups with significantly different RFS and OS. The proposed MRI-based prognostic stratification system facilitates the refinement and further subclassification of patients with MHCC beyond the Milan criteria. 4. 2.

Validation of the novel International Association for the Study of Lung Cancer grading system and prognostic value of filigree micropapillary and discohesive growth pattern in invasive pulmonary adenocarcinoma

IntroductionThe Pathology Committee of the International Association for the Study of Lung Cancer (IASLC) proposed a new histological grading system based on the combination of predominant and high-grade patterns in 2020. Materials and MethodsPathological sections from 631 patients with stage I-III invasive lung adenocarcinoma were reviewed. We then determined the histological grade according to the new grading system and confirmed the pathological features that included the filigree micropapillary and discohesive growth pattern. Applying of the novel IASLC grading system in prognosis stratification was verified and the clinical significance of the pathological characteristics was explored. ResultsCox multivariable analysis revealed that in the stage I-III invasive lung adenocarcinoma, the IASLC grading system was significantly associated with disease-free survival (DFS) [hazard ratio (HR) = 1.419; 95 % confidence interval (CI): 1.040–1.937; P = 0.027] and overall survival (OS) (HR = 1.899; 95 % CI: 1.168–3.087; P = 0.010). In patients with IASLC Grades 1 and 2, the simultaneous presence of filigree micropapillary and discohesive growth pattern was significantly correlated with DFS (HR = 1.899; 95 % CI:1.168–3.087; P = 0.010). However, the filigree micropapillary and discohesive growth pattern did not affect the OS (HR = 2.786; P = 0.317). The competitive risk model revealed that in the stage I cohort, the simultaneous presence of filigree micropapillary and discohesive growth pattern was a risk factor for recurrence and metastasis [sub- distribution HR (SHR) = 1.987; 95 %CI: 1.122–3.518; P = 0.019]. ConclusionOur study verified that the new prognostic stratification system was an effective stratification tool. Filigree micropapillary and discohesive growth pattern may also be risk factors for DFS, postoperative recurrence and metastasis.

ScRNA-seq and bulk RNA-seq reveal the characteristics of ferroptosis and establish a risk signature in cholangiocarcinoma

Ferroptosis is a recently discovered mode of cell death that inhibits tumor growth. Single-cell RNA sequencing (scRNA-seq) is a powerful tool for analyzing tumor heterogeneity and the immune microenvironment at the single-cell level. We used CIBERSORT to identify cellular immune scores and found that monocytes had significantly infiltrated and were correlated with prognosis in cholangiocarcinoma. scRNA-seq data were extracted from the Gene Expression Omnibus database, and the FindCluster() package was used for cell cluster analysis, which obtained 21 cell clusters, and there was increased TNFSF13B-TFRC intercellular communication between monocytes and cholangiocytes. A weighted correlation network analysis was performed with the WGCNA package to obtain monocyte-related gene modules. Univariate and multivariate Cox analyses were then performed to further establish the signature, and the reliability of the signature was assessed by receiver operating characteristic curve and decision curve analysis. A nomogram signature based on the Kaplan-Meier survival analysis was established. We found that the communication between monocytes and malignant cells in cholangiocarcinoma may be a regulatory factor of ferroptosis in cancer cells. The prognostic stratification system of the three-gene signature related to monocytes and ferroptosis can accurately assess the prognostic risk for cholangiocarcinoma.

Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features.

BackgroundThis study aimed to construct a prognostic stratification system for gastric cancer (GC) using tumour invasion-related genes to more accurately predict the clinical prognosis of GC.MethodologyTumour invasion-related genes were downloaded from CancerSEA, and their expression data in the TCGA-STAD dataset were used to cluster samples via non-negative matrix factorisation (NMF). Differentially expressed genes (DEGs) between subtypes were identified using the limma package. KEGG pathway and GO functional enrichment analyses were conducted using the WebGestaltR package (v0.4.2). The immune scores of molecular subtypes were evaluated using the R package ESTIMATE, MCPcounter and the ssGSEA function of the GSVA package. Univariate, multivariate and lasso regression analyses of DEGs were performed using the coxph function of the survival package and the glmnet package to construct a RiskScore model. The robustness of the model was validated using internal and external datasets, and a nomogram was constructed based on the model.ResultsBased on 97 tumour invasion-related genes, 353 GC samples from TCGA were categorised into two subtypes, thereby indicating the presence of inter-subtype differences in prognosis. A total of 569 DEGs were identified between the two subtypes; of which, four genes were selected to construct the risk model. This four-gene signature was robust and exhibited stable predictive performance in different platform datasets (GSE26942 and GSE66229), indicating that the established model performed better than other existing models.ConclusionA prognostic stratification system based on a four-gene signature was developed with a desirable area under the curve in the training and independent validation sets. Therefore, the use of this system as a molecular diagnostic test is recommended to assess the prognostic risk of patients with GC.

Developing a novel DNA methylation risk score for survival and identification of prognostic gene mutations in endometrial cancer: a study based on TCGA data.

Few studies have focused on DNA methylation in endometrial cancer. The aim of our study is identify its role in endometrial cancer prognosis. A publicly available dataset was retrieved from The Cancer Genome Atlas. For validation of expression alteration due to methylation, RNA sequencing data were obtained from other independent cohorts. MethSurv was used to search for candidate CpG probes, which were then filtered by least absolute shrinkage and selection operator Cox regression and multivariate Cox regression analyses to identify final set of CpG probes for overall survival. A methylation-based risk model was developed and receiver operating characteristic analysis with area under curve was used for evaluation. Patients were divided into high- and low-risk groups using an optimal cut-off point. Comprehensive bioinformatic analyses were conducted to identify hub genes, key transcription factors, and enriched cancer-related pathways. Kaplan-Meier curve was used for survival analysis. A 5-CpG signature score was established. Its predictive value for 5-year overall survival was high, with area under curve of 0.828, 0.835 and 0.816 for the training, testing and entire cohorts. cg27487839 and cg12885678 had strong correlation with their gene expression, XKR6 and PTPRN2, and lower PTPRN2 expression was associated with poorer survival in both The Cancer Genome Atlas and the validation datasets. Low-risk group was associated with significantly better survival. Low-risk group harboured more mutations in hub genes and key transcription factors, and mutations in SP1 and MECP2 represented favourable outcome. We developed a methylation-based prognostic stratification system for endometrial cancer. Low-risk group was associated with better survival and harboured more mutations in the key regulatory genes.

Risk Factors Associated with Poor Outcome in Patients with Infective Endocarditis: An Italian Single-Center Experience.

Background: Nowadays, infective endocarditis (IE) is still burdened by a high mortality. In the absence of an adequate prognostic stratification system, it is important to assess new predictors of poor outcomes. The aim of our study is to evaluate which factors were associated with higher mortality in IE patients. Methods: A retrospective cohort study enrolled patients with an IE diagnosis at the Infectious Diseases Clinic of the University ‘G. D’Annunzio’, Chieti, Italy from January 2013 to December 2019. For each patient, demographic, anamnestic and clinical information, embolic phenomena, laboratory and microbiologic data, treatment, and outcomes were collected and analyzed. A correlation analysis was performed. Results: Sixty-eight patients with EI were studied; among them, the mortality was 17.6%, 20.6%, and 23.5%, intra-hospital, at 1 month from discharge and at 6 months from discharge, respectively. Mortality was significantly correlated with age, estimated glomerular filtration rate, and procalcitonin values when considering either basal values (r = 0.266, p = 0.029), or values at 48–72 h from the start of an antibiotic therapy (r = 0.222; p < 0.05), cerebral embolization for 6-month mortality (r = 0.284; p = 0.019), and inadequate antibiotic therapy (r = 0.232, p < 0.05). Conclusions: Procalcitonin values, at EI diagnosis and at 48–72 h after starting antibiotics, are prognostic factors useful for stratifying patient risk, and for setting up a personalized treatment. Of note, cerebral embolization and an inappropriate empirical treatment were associated with a higher mortality in the short- and long-term.

Establishment and validation of a novel invasion-related gene signature for predicting the prognosis of ovarian cancer

BackgroundOvarian cancer (OC) is an invasive gynaecologic cancer with a high cancer-related death rate. The purpose of this study was to establish an invasion-related multigene signature to predict the prognostic risk of OC.MethodsWe extracted 97 invasion-related genes from The Cancer Genome Atlas (TCGA) database. Then, the ConsensusClusterPlus and limma packages were used to calculate differentially expressed genes (DEGs). To calculate the immune scores of the molecular subtypes, we used ESTIMATE to evaluate the stromal score, immune score and ESTIMATE score. MCP-counter and the GSVA package ssgsea were used to evaluate the types of infiltrating immune cells. Survival and nomogram analyses were performed to explore the prognostic value of the signature. Finally, qPCR, immunohistochemistry staining and functional assays were used to evaluate the expression and biological abilities of the signature genes in OC.ResultsBased on the consistent clustering of invasion-related genes, cases in the OC datasets were divided into two subtypes. A significant difference was observed in prognosis between the two subtypes. Most genes were highly expressed in the C1 group. Based on the C1 group genes, we constructed an invasion-related 6-gene prognostic risk model. Furthermore, to verify the signature, we used the TCGA-test and GSE32062 and GSE17260 chip datasets for testing and finally obtained a good risk prediction effect in those datasets. Moreover, the results of the qPCR and immunohistochemistry staining assays revealed that KIF26B, VSIG4 and COL6A6 were upregulated and that FOXJ1, MXRA5 and CXCL9 were downregulated in OC tissues. The functional study showed that the expression of KIF26B, VSIG4, COL6A6, FOXJ1, MXRA5 and CXCL9 can regulate the migration and invasion abilities of OC cells.ConclusionWe developed a 6-gene prognostic stratification system (FOXJ1, MXRA5, KIF26B, VSIG4, CXCL9 and COL6A6) that is independent of clinical features. These results suggest that the signature could potentially be used to evaluate the prognostic risk of OC patients.

Based on Molecular Subtypes, Immune Characteristics and Genomic Variation to Constructing and Verifying Multi-Gene Prognostic Characteristics of Colorectal Cancer.

Background: Colon cancer (COAD) has been identified as being among the most prevalent tumors globally and ranked the third major contributor to cancer-related mortality. COAD is a molecularly heterogeneous disease. There are great differences in clinical manifestations and prognosis among different molecular subtypes. Methods:379 TCGA-COAD samples were divided into four subtypes: primary proliferative, with collective, crypt-like, and EMT invasion. The differences among the four subtypes were analyzed from the multidimensional perspectives of immunity, genomic variation, and prognosis. The limma package was utilized to identify differentially expressed genes (DEGs) amongst different molecular subtypes. Phenotype-related coexpressed gene modules were identified using WGCNA. The polygenic prognosis model was created utilizing the lasso Cox analysis and verified by time-dependent subject operating characteristics (ROC). Results: There are some differences in prognosis, TMB and common gene variation, immune score, and immunotherapy/chemotherapy between proliferative and three invasive molecular subtypes. 846 differential genes (DEGs) were obtained by limma packet analysis. Differential gene analysis was utilized to screen the DEGs among distinct subtypes, which were significantly enriched in the pathways related to tumorigenesis and development. Co-expression network analysis found 46 co-expressed genes correlated with proliferative and three invasive phenotypes. Based on differentially co-expressed genes, we developed a prognostic risk model of 8-genes signature, which exhibited strong stability regardless of external and internal validation. RT-PCR experiments proved the expression of eight genes in tumor and normal samples. Conclusion: We have developed an eight-gene signature prognostic stratification system. Furthermore, we proposed that this classifier can serve as a molecular diagnostic tool to assess the prognosis of colon cancer patients.

Papillary Thyroid Cancer Prognosis: An Evolving Field.

Simple SummaryOver the last couple of decades, the prognostic stratification systems of differentiated thyroid cancer (DTC) patients have been revised several times in an attempt to achieve a tailored clinical management reflecting the single patients’ needs. Such revisions are likely to continue in the near future, since the prognostic value of a number of promising clinicopathological features and new molecular biomarkers are being evaluated. Here, we will review the current staging systems of thyroid cancer patients and discuss the most relevant clinicopathological parameters and new molecular markers that are potentially capable of refining the prognosis.Over the last few years, a great advance has been made in the comprehension of the molecular pathogenesis underlying thyroid cancer progression, particularly for the papillary thyroid cancer (PTC), which represents the most common thyroid malignancy. Putative cancer driver mutations have been identified in more than 98% of PTC, and a new PTC classification into molecular subtypes has been proposed in order to resolve clinical uncertainties still present in the clinical management of patients. Additionally, the prognostic stratification systems have been profoundly modified over the last decade, with a view to refine patients’ staging and being able to choose a clinical approach tailored on single patient’s needs. Here, we will briefly discuss the recent changes in the clinical management of thyroid nodules, and review the current staging systems of thyroid cancer patients by analyzing promising clinicopathological features (i.e., gender, thyroid auto-immunity, multifocality, PTC histological variants, and vascular invasion) as well as new molecular markers (i.e., BRAF/TERT promoter mutations, miRNAs, and components of the plasminogen activating system) potentially capable of ameliorating the prognosis of PTC patients.

Integration of scRNA-Seq and Bulk RNA-Seq to Analyse the Heterogeneity of Ovarian Cancer Immune Cells and Establish a Molecular Risk Model.

BackgroundConsiderable evidence suggests that the heterogeneity of ovarian cancer (OC) is a major cause of treatment failure. Single-cell RNA sequencing (scRNA-seq) is a powerful tool to analyse the heterogeneity of the tumour at the single-cell level, leading to a better understanding of cell function at the genetic and cellular levels.MethodsOC scRNA-seq data were extracted from the Gene Expression Omnibus (GEO) database and the FindCluster () package used for cell cluster analysis. The GSVA package was used for single-sample gene set enrichment analysis (ssGSEA) analysis to obtain a Hallmark gene set score and bulk RNA-seq data were used to analyse the key genes of OC-associated immune cell subsets. CIBERSORT was used to identify immune scores of cells and the “WGCNA” package for the weighted correlation network analysis (WGCNA). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) analyses of subtype groups were performed by GSEA. Then, univariate Cox and lasso regression were performed to further establish a signature. Finally, qPCR and immunohistochemistry staining were used to evaluate the expression of signature genes in OC.ResultsTwo scRNA-seq (GSE154600 and GES158937) datasets were integrated to obtain 20 cell clusters. T cells or NK cells (cluster 5, 6, 7, 11), B cells (cluster 16, 19, 20) and myeloid cells (cluster 4, 9, 10) were clustered according to immune cell markers. The ssGSEA revealed that M1- and M2-like myeloid cell-related genes were significantly upregulated in P3 and P4 patients in the GSE154600 data. Immune cell analysis in TCGA-OC showed that a high abundance of M1-like tumour-associated macrophages (TAMS) predicts better survival. WGCNA, univariate Cox and lasso Cox regression established a two-gene signature (RiskScore=-0.059*CXCL13-0.034*IL26). Next, the TCGA-test and TCGA-OC were used to test the risk prediction ability of the signature, showing a good effect in the datasets. Moreover, the qPCR and immunohistochemistry staining revealed that the expression of CXCL13 and IL26 was reduced in OC tissues.ConclusionA two-gene signature prognostic stratification system (CXCL13 and IL26) was developed based on the heterogeneity of OC immune cells to accurately evaluate the prognostic risk.

Development of a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer

Uterine corpus endometrial carcinoma locally infiltrates numerous immune cells and other tumor immune microenvironment components. These cells are involved in malignant tumor growth and proliferation and the process of resistance toward immunotherapies. Here, we aimed to develop a tumor immune microenvironment-related prognostic signature for high-risk grade III endometrial carcinoma based on The Cancer Genome Atlas. The signature was systematically correlated with immune infiltration characteristics of the tumor microenvironment. The seven-gene Riskscore signature was robust and performed well in training, testing, and Gene Expression Omnibus-independent cohorts. A nomogram comprising the gene signature accurately predicted patient prognosis, with our model performing better than other endometrial cancer-related signatures. Analysis of the IMvigor210 immunotherapy cohort revealed that subgroups with a low Riskscore had a better prognosis than subgroups with a high Riskscore. Subgroups with a low Riskscore exhibited immune cell infiltration and inflammatory profiles, whereas subgroups with a high Riskscore experienced progressive disease. The receiver operating characteristic curve indicated that risk score, neoantigen, and tumor mutation burden models together accurately predicted treatment response. Taken together, we developed a tumor microenvironment-based seven-gene prognostic stratification system to predict the prognosis of patients with high-risk endometrial cancer and guide more effective immunotherapy strategies.

Derivation and validation of a lipid-covered prognostic model for mature T-cell lymphomas

BackgroundMature T-cell lymphomas (MTCLs), a group of diseases with high aggressiveness and vulnerable prognosis, lack for the accurate prognostic stratification systems at present. Novel prognostic markers and models are urgently demanded. Aberrant lipid metabolism is closely related to the tumor progression but its prognostic significance in MTCLs remains unexplored. This study aims to investigate the relationship between dysregulated lipid metabolism and survival prognosis of MTCLs and establish a novel and well-performed prognostic scoring system for MTCL patients.MethodsA total of 173 treatment-naive patients were enrolled in this study. Univariate and multivariate Cox regression analysis were performed to assess the prognostic significance of serum lipid profiles and screen out independent prognostic factors, which constituted a novel prognostic model for MTCLs. The performance of the novel model was assessed in the training and validation cohort, respectively, by examining its calibration, discrimination and clinical utility.ResultsAmong the 173 included patients, 115 patients (01/2006–12/2016) constituted the training cohort and 58 patients (01/2017–06/2020) formed the validation cohort. Univariate analysis revealed declined total cholesterol (TC, P = 0.000), high-density lipoprotein cholesterol (HDL-C, P = 0.000) and increased triglycerides (TG, P = 0.000) correlated to inferior survival outcomes. Multivariate analysis revealed extranodal involved sites ≥ 2 (hazard ratio [HR]: 2.439; P = 0.036), β2-MG ≥ 3 mg/L (HR: 4.165; P = 0.003) and TC < 3.58 mmol/L (HR: 3.338; P = 0.000) were independent predictors. Subsequently, a novel prognostic model, EnBC score, was constructed with these three factors. Harrell’s C-index of the model in the training and validation cohort was 0.840 (95% CI 0.810–0.870) and 0.882 (95% CI 0.822–0.942), respectively, with well-fitted calibration curves. The model divided patients into four risk groups with distinct OS [median OS: not available (NA) vs. NA vs. 14.0 vs. 4.0 months, P < 0.0001] and PFS (median PFS: 84.0 vs. 19.0 vs. 8.0 vs. 1.5 months, P < 0.0001). Time-dependent receiver operating characteristic curve and decision curve analysis further revealed that EnBC score provided higher diagnostic capacity and clinical benefit, compared with International Prognostic Index (IPI).ConclusionFirstly, abnormal serum lipid metabolism was demonstrated significantly related to the survival of MTCL patients. Furthermore, a lipid-covered prognostic scoring system was established and performed well in stratifying patients with MTCLs.

Systematic profiling of invasion-related gene signature predicts prognostic features of lung adenocarcinoma.

Due to the high heterogeneity of lung adenocarcinoma (LUAD), molecular subtype based on gene expression profiles is of great significance for diagnosis and prognosis prediction in patients with LUAD. Invasion‐related genes were obtained from the CancerSEA database, and LUAD expression profiles were downloaded from The Cancer Genome Atlas. The ConsensusClusterPlus was used to obtain molecular subtypes based on invasion‐related genes. The limma software package was used to identify differentially expressed genes (DEGs). A multi‐gene risk model was constructed by Lasso‐Cox analysis. A nomogram was also constructed based on risk scores and meaningful clinical features. 3 subtypes (C1, C2 and C3) based on the expression of 97 invasion‐related genes were obtained. C3 had the worst prognosis. A total of 669 DEGs were identified among the subtypes. Pathway enrichment analysis results showed that the DEGs were mainly enriched in the cell cycle, DNA replication, the p53 signalling pathway and other tumour‐related pathways. A 5‐gene signature (KRT6A, MELTF, IRX5, MS4A1 and CRTAC1) was identified by using Lasso‐Cox analysis. The training, validation and external independent cohorts proved that the model was robust and had better prediction ability than other lung cancer models. The gene expression results showed that the expression levels of MS4A1 and KRT6A in tumour tissues were higher than in normal tissues, while CRTAC1 expression in tumour tissues was lower than in normal tissues. The 5‐gene signature prognostic stratification system based on invasion‐related genes could be used to assess prognostic risk in patients with LUAD.