Latent class analysis-derived classification improves the cancer-specific death stratification of lymphomas: A large retrospective cohort study.

Lymphomas have diverse etiologies, treatment approaches, and prognoses. Accurate survival estimation is challenging for lymphoma patients due to their heightened susceptibility to non-lymphoma-related mortality. To overcome this challenge, we propose a novel lymphoma classification system that utilizes latent class analysis (LCA) and incorporates demographic and clinicopathological factors as indicators. We conducted LCA using data from 221,812 primary lymphoma patients in the Surveillance, Epidemiology, and End Results (SEER) database and identified four distinct LCA-derived classes. The LCA-derived classification efficiently stratified patients, thereby adjusting the bias induced by competing risk events such as non-lymphoma-related death. This remains effective even in cases of limited availability of cause-of-death information, leading to an enhancement in the accuracy of lymphoma prognosis assessment. Additionally, we validated the LCA-derived classification model in an external cohort and observed its improved prognostic stratification of molecular subtypes. We further explored the molecular characteristics of the LCA subgroups and identified potential driver genes specific to each subgroup. In conclusion, our study introduces a novel LCA-based lymphoma classification system that provides improved prognostic prediction by accounting for competing risk events. The proposed classification system enhances the clinical relevance of molecular subtypes and offers insights into potential therapeutic targets.

Prognostic stratification in DLBCL patients with aberrant MYC gene.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease characterized by a subset of patients who exhibit treatment resistance and poor prognoses. Genomic assays have been widely employed to identify high-risk individuals characterized by rearrangements in the MYC, BCL2 and BCL6 genes. These patients typically undergo more aggressive therapeutic treatments; however, there remains a significant variation in their treatment outcomes. This study introduces an MYC signature score (MYCSS) derived from gene expression profiles, specifically designed to evaluate MYC overactivation in DLBCL patients. MYCSS was validated across several independent cohorts to assess its ability to stratify patients based on MYC-related genetic and molecular aberrations, enhancing the accuracy of prognostic evaluations compared to conventional MYC biomarkers. Our results indicate that MYCSS significantly refines prognostic accuracy beyond that of conventional MYC biomarkers focused on genetic aberrations. More importantly, we found that nearly 50% of patients identified as high risk by traditional MYC metrics actually share similar survival prospects with those having no MYC aberrations. These patients may benefit from standard GCB-based therapies rather than more aggressive treatments. MYCSS provides a robust signature that identifies high-risk patients, aiding in the precision treatment of DLBCL, and minimizing the potential for overtreatment.

Metabolic and immune-related gene signatures: Predictive stratification and prognostic implications in gastric cancer.

BACKGROUND: Gastric cancer, marked by its heterogeneous nature, showcases various molecular subtypes and clinical trajectories. This research delves into the significance of metabolic and immune-driven pathways in gastric cancer, constructing a prognostic signature derived from differentially expressed metabolic and immune-correlated genes (DE-MIGs). METHODS: Metabolic and immune-associated gene were sourced from the GeneCards database. Differential expression analysis on the TCGA-STAD dataset was executed using the limma package, unveiling 51 DE-MIGs that underwent functional enrichment scrutiny. The LASSO Cox regression methodology guided the creation of the prognostic signature, and individual patient risk scores were determined. Assessment tools like CIBERSORT, ESTIMATE and ssGSEA were deployed to study the immune microenvironment, while mutation profiles, genomic stability, resistance to chemotherapy and immunotherapy responsiveness were scrutinized across distinct signature categorizations. RESULTS: Among the identified DE-MIGs, 26 were significantly tied to the overall survival of gastric cancer patients. The developed prognostic signature proficiently differentiated patients into high-risk and low-risk cohorts, with the latter showing markedly better outcomes. The study underscored the centrality of the immune microenvironment in influencing gastric cancer outcomes. Key pathways such as TGF-Beta, TP53 and NRF2 dominated the high-risk group, whereas the LRTK-RAS and WNT pathways characterized the low-risk group. Interestingly, the low-risk segment also manifested a heightened tumor mutation burden and enhanced susceptibility to immunotherapy. CONCLUSIONS: Our findings introduce a pivotal prognostic signature, rooted in DE-MIGs, that effectively segregates gastric cancer patients into distinct risk-based segments. Insights into the influential role of the immune microenvironment in gastric cancer progression pave the way for more refined therapeutic interventions.

Investigating the prognostic role of lncRNAs associated with disulfidptosis-related genes in clear cell renal cell carcinoma.

INTRODUCTION: Renal cell carcinoma (RCC) is a grave malignancy that poses a significant global health burden with over 400,000 new cases annually. Disulfidptosis, a newly discovered programmed cell death process, is linked to the actin cytoskeleton, which plays a vital role in maintaining cell shape and survival. The role of disulfidptosis is poorly depicted in the clear cell histologic variant of RCC (ccRCC). METHODS: Three sets of ccRCC cohorts, ICGC_RECA-EU (n = 91), GSE76207 (n = 32) and TCGA-KIRC (n = 607), were included in our study, the batch effect of which was removed using the "combat" function. Correlation was calculated using the "rcorr" function of the "Hmisc" package for Pearson analysis, which was visualized using the "pheatmap" package. Principal component analysis was performed by the "vegan" package, visualized using the "scatterplot3d" package. Long non-coding RNAs (lncRNAs) associated with disulfidptosis were screened out using least absolute shrinkage and selection operator (LASSO) and COX analysis. Tumor mutation, immune landscaping and immunotherapy prediction were performed for further characterization of two risk groups. RESULTS: A total of 1822 disulfidptosis-related lncRNAs was selected, among which 308 lncRNAs were found to be significantly associated with the clinical outcome of ccRCC patients. We retained 11 disulfidptosis-related lncRNAs, namely, AP000439.3, RP11-417E7.1, RP11-119D9.1, LINC01510, SNHG3, AC156455.1, RP11-291B21.2, EMX2OS, AC093850.2, HAGLR and RP11-389C8.2, through LASSO and COX analysis for prognosis model construction, which displayed satisfactory accuracy (area under the curve, AUC, values all above 0.6 in multiple cohorts) in stratification of ccRCC prognosis. A nomogram model was constructed by integrating clinical factors with risk score, which further enhanced the prediction efficacy (AUC values all above 0.7 in multiple cohorts). We found that patients of male gender, higher clinical stages and advanced pathological T stage were inclined to have higher risk score values. Dactinomycin_1911, Vinblastine_1004, Daporinad_1248 and Vinorelbine_2048 were identified as promising candidate drugs for treating ccRCC patients of higher risk score value. Moreover, patients of higher risk value were prone to be resistant to immunotherapy. CONCLUSION: We developed a prognosis predicting model based on 11 selected disulfidptosis-related lncRNAs, the efficacy of which was verified in different cohorts. Furthermore, we delineated an intricate portrait of tumor mutation, immune topography and pharmacosensitivity evaluations within disparate risk stratifications.

Prognostic Factors for Recovery from Left Recurrent Laryngeal Nerve Palsy After Minimally Invasive McKeown Esophagectomy: A Retrospective Study.

BACKGROUND: Recurrent laryngeal nerve (RLN) palsy is a serious complication of esophagectomy that affects the patient's phonation and the ability to prevent life-threatening aspiration events. The aim of this single-center, retrospective study was to investigate the clinical course of left RLN palsy and to identify the main prognostic factors for recovery. METHODS: The study cohort consisted of 85 patients who had developed left RLN palsy after minimally invasive McKeown esophagectomy. Vocal cord function was assessed in all participants through laryngoscopic examinations, both in the immediate postoperative period and during follow-up. Permanent palsy was defined as no evidence of recovery after 6 months. Univariate and multivariable logistic regression analyses were applied to evaluate the associations between different variables and the outcome of palsy. RESULTS: Twenty-two (25.8%) patients successfully recovered from left RLN palsy. On multivariable logistic regression analysis, active smoking (odds ratio [OR] 0.335, p = 0.038) and the use of thoracoscopic surgery (vs. robotic surgery; OR 0.264, p = 0.028) were identified as independent unfavorable predictors for recovery from palsy. The estimated rates of recovery derived from a logistic regression model for patients harboring two, one, or no risk factors were 13.16%, 31.15-34.75%, and 61.39%, respectively. CONCLUSION: Only one-quarter of patients who had developed left RLN palsy after minimally invasive McKeown esophagectomy were able to fully recover. Smoking habits and the surgical approach were identified as key determinants of recovery. Patients harboring adverse prognostic factors are potential candidates for early intervention strategies.

Development of a prognostic model for predicting long-term visual acuity after cataract surgery in children with bilateral congenital cataracts: a single centre retrospective, observational study.

BACKGROUND: To evaluate factors influencing best corrected visual acuity (BCVA) in paediatric patients with bilateral congenital cataracts (CC) after cataract extraction and intraocular lens (IOL) implantation, as well as develop a robust model for predicting long-term visual acuity. METHODS: This retrospective study followed 194 paediatric patients with bilateral CC from January 2008 to December 2021. The endpoint event was defined as a final BCVA < 0.22 Log MAR at the last follow-up, which indicated good outcome. The probability of reaching this endpoint event was modelled using Cox proportional hazards regression analysis and internally validated through 200 iteration of 5-fold cross-validation. RESULTS: A prognostic model for long-term visual acuity in bilateral CC after surgical treatment was established as follows: ln h(t) = -0.009 × "age at cataract extraction" - 0.015 × "age at IOL implantation" - 2.934 × "without nystagmus at last follow - up" + ln h0(0), in which h0(t) represents the baseline risk equation that can be any non-negative equation for time (t); h(t) represents the probability of the endpoint event occurring at time (t) without any endpoint event occurring before it. The model was visualized using a nomogram and contour plot to facilitate clinical practice. The model demonstrated reasonably accurate discrimination with an area under the receiver operating characteristic curve of 0.712 (95% confidence interval [CI]: 0.589-0.835) and a C-index of 0.797 (95% CI: 0.683-0.911). According to the model, children with bilateral CC had a higher likelihood of achieving a good outcome (BCVA < 0.22 Log MAR) if they underwent cataract extraction before the age of six months (hazard ratio [HR] 1.80, 95% CI: 0.92-3.70), received IOL implantation before the age of thirty-one months (HR 3.70, 95% CI: 1.77-7.80), and presented without nystagmus during their last follow-up visit (HR 11.20, 95% CI: 3.96-31.80). CONCLUSIONS: This long-term visual acuity prognostic model demonstrates adequate performance for individualized prediction and assists in clinical decision-making. The risk stratification index guides optimal timing for surgery.

Evaluation of consequential risk in ED patients with non-cardiac syncope.

BACKGROUND: Following standard syncope care, after exclusion of cardiac syncope, further workup is generally only recommended in cases of severe syncope due to consequential risk such that syncope is associated with injury or negative impacts on quality of life. This study is aimed to identify incidence and risk factors of severe syncope due to consequential risk, in a cohort of ED patients with non-cardiac syncope. METHODS: In a sample of 356 cases, we we conducted a case-control study comparing personal data, drug regimen, comorbidities, characteristics of syncope and previous episodes in patients with vs. without a severe syncope. RESULTS: Patients with severe syncope (120, 31.7% of total) resulted more frequently treated with a polypharmacy and CNS agents and affected by comorbidities entailing risk of falling; they more frequently had syncope occurred in a risky context, unwitnessed (55.8%), not preceded by prodromes (56.6%) and with clinical characteristics different from reflex syncope (82.3%); in these patients, previous episodes more frequently were clustered in the last years and complicated by major injuries. Absence of witnesses and prodromes and ED diagnosis different from reflex syncope resulted to be independently associated with severe syncope due to consequential risk. CONCLUSIONS: Syncope has a negative impact on a patient's life, through injuries or other personal consequences, in roughly one third of cases; to identity these patients, needing further investigation, emergency physicians should focus on episodes not preceded by prodromes, unwitnessed and with characteristic other than reflex syncope. Nonetheless, specific tools are needed to evaluate the impact of syncope on quality of life, to avoid clogging the path after ED discharge.

Novel disulfidptosis-derived gene blueprint stratifying patients with breast cancer.

INTRODUCTION: Breast cancer remains the predominant cancer among females, accounting for about 24.2% of all cancer cases. Alarmingly, it is the primary cause of cancer-related mortality in women under 45. METHODS: This research analyzed RNA sequencing data from 1082 TCGA-BRCA and 107 GSE58812 breast cancer patients. Single-cell RNA data from five patients in the GSE118389 data set were also studied. Using Random forest and COX regression, we developed a prognostic model. Pathway analysis employed GSVA and GO, while immune profiles were assessed via ssGSEA and MCPcounter. Mutation patterns utilized maftools, and drug sensitivity scores were derived from the GDSC database with oncoPredict. RESULTS: Analysis of the GSE118389 data set identified three distinct cell types: immune, epithelial, and stromal. P53 and VEGF were notably enriched. Five key genes (TMEM251, ADAMTSL2, CDC123, PSMD1, TLE1) were pinpointed for their prognostic significance. We introduced a disulfidptosis-associated score as a novel risk factor for breast cancer prognosis. Survival outcomes varied significantly between training and validation sets. Comprehensive immune profiling revealed no difference in activated CD8-positive T cells between risk groups, but a positive correlation of NK cells, neutrophils, cytotoxic lymphocytes, and monocytic cells with the riskscore was noted. Importantly, a negative association between the drug Nelarabine and riskscore was identified. CONCLUSION: This research underscores the significance of a disulfidptosis-associated gene signature in breast cancer prognosis.

Shallow whole-genome sequencing of bone marrow aspirates in myelodysplastic neoplasms: A retrospective comparison with cytogenetics.

Copy number alterations (CNA) are powerful prognostic markers in myelodysplastic neoplasms (MDS) and are routinely analyzed by conventional cytogenetic analysis (CCA) on bone marrow (BM). Although CCA is still the gold standard, it requires extensive hands-on time and highly trained staff for the analysis, making it a laborious technique. To reduce turn-around-time per case, shallow whole genome sequencing (sWGS) technologies offer new perspectives for the diagnostic work-up of this disorder. We compared sWGS with CCA for the detection of CNAs in 33 retrospective BM samples of patients with MDS. Using sWGS, CNAs were detected in all cases and additionally allowed the analysis of three cases for which CCA failed. The prognostic stratification (IPSS-R score) of 27 out of 30 patients was the same with both techniques. In the remaining cases, discrepancies were caused by the presence of balanced translocations escaping sWGS detection in two cases, a subclonal aberration reported with CCA that could not be confirmed by FISH or sWGS, and the presence of an isodicentric chromosome idic(17)(p11) missed by CCA. Since sWGS can almost entirely be automated, our findings indicate that sWGS is valuable in a routine setting validating it as a cost-efficient tool.

Predictors and prognostic stratification for lupus low disease activity state: results from a prospective clinical trial.

OBJECTIVE: To identify predictors for lupus low disease activity state (LLDAS), early-achieved LLDAS and long-term disease activity, and to refine a prognostic stratification tool for use in active SLE patients. METHOD: A total of 245 active SLE patients were enrolled, followed up quarterly from 2014 to 2016. LLDAS-50 was defined as the maintenance of LLDAS for ≥50% of the observed time. LLDAS at 3 months after cohort entry (LLDAS-3mo) was considered an early-achieved LLDAS. Multivariate analysis was performed to identify predictors for LLDAS, early-achieved LLDAS and long-term disease activity. Based on the factors associated with LLDAS, a prognostic stratification tool for LLDAS was established. RESULTS: The 2-year probability of achieving LLDAS was 62.9% (154/245). Multivariate analysis-determined renal involvement, haematological involvement and hypocomplementaemia were negative predictors for achieving LLDAS and LLDAS-50. In multivariate logistic analysis, antiphospholipid antibodies positivity, hypocomplementaemia, renal involvement and haematological involvement were identified as negative predictors for achieving LLDAS-3mo. LLDAS-3mo (P < 0.0001; risk ratio: 47.694; 95% CI: 13.776, 165.127) was a strong predictor for LLDAS-50. The probability of achieving LLDAS, LLDAS-50 and LLDAS-3mo were 88.9% (32/36), 69.4% (25/36) and 41.7% (15/36) in the low-risk group, 65% (65/100), 51.0% (51/100) and 32.0% (32/100) in intermediate-risk group, and 52.8% (57/108), 27.8% (30/108) and 13.0% (14/108) in high-risk group respectively. Significant differences (P < 0.0001) were observed in the LLDAS Kaplan-Meier estimates for the three risk groups based on the identified risk factors. CONCLUSION: Renal involvement, haematological involvement and hypocomplementaemia were negative predictors of LLDAS achievement and maintenance. LLDAS-3mo was a positive predictor for the long-term sustainment of LLDAS.

Global longitudinal strain by CMR improves prognostic stratification in acute myocarditis presenting with normal LVEF.

BACKGROUND: Prognostic stratification of acute myocarditis (AM) presenting with normal left ventricular ejection fraction (LVEF) relies mostly on late gadolinium enhancement (LGE) characterization. Left ventricular peak global longitudinal strain (LV-GLS) measured by feature tracking analysis might improve prognostication of AM presenting with normal LVEF. METHODS: Data of patients undergoing cardiac magnetic resonance (CMR) for clinically suspected AM in seven European Centres (2013-2020) were retrospectively analysed. Patients with AM confirmed by CMR and LVEF ≥50% were included. LGE was visually characterized: localized versus. non-localized, subepicardial versus midwall. LV-GLS was measured by dedicated software. The primary outcome was the first occurrence of an adverse cardiovascular event (ACE) including cardiac death, life-threatening arrhythmias, development of heart failure or of LVEF <50%. RESULTS: Of 389 screened patients, 256 (66%) fulfilled inclusion criteria: median age 36 years, 71% males, median LVEF 60%, median LV-GLS -17.3%. CMR was performed at 4 days from hospitalization. At 27 months, 24 (9%) patients experienced ≥1 ACE (71% developed LVEF <50%). Compared to the others, they had lower median LV-GLS values (-13.9% vs. -17.5%, p = .001). At Kaplan-Meier analysis, impaired LV-GLS (both considered as > -20% or quartiles), non-localized and midwall LGE were associated with ACEs. Patients with LV-GLS ≤-20% did not experience ACEs. LV-GLS remained associated with ACEs after adjustment for non-localized and midwall LGE. CONCLUSION: In AM presenting with LVEF ≥50%, LV-GLS provides independent prognostic value over LGE characterization, improving risk stratification and representing a rationale for further studies of therapy in this cohort.

Multiparametric MRI-Based Radiomic Signature for Preoperative Evaluation of Overall Survival in Intrahepatic Cholangiocarcinoma After Partial Hepatectomy.

BACKGROUND: The clinical outcomes of patients with intrahepatic cholangiocarcinoma (ICC) after partial hepatectomy remain suboptimal. Identifying patients with poor outcomes before surgery is urgently required. PURPOSE: To develop a multiparametric magnetic resonance imaging (MRI)-based radiomic signature to evaluate overall survival (OS) preoperatively and to investigate its incremental value for disease stratification. STUDY TYPE: Retrospective. SUBJECTS: One hundred and sixty-three patients with pathologically defined ICC, divided into training (N = 115) and validation sets (N = 48). SEQUENCE: Three-dimensional T1-weighted gradient-echo sequence with and without contrast agent, T2-weighted fast spin-echo sequence, and diffusion-weighted imaging with single-shot echo-planar sequence at 1.5 T or 3.0 T. ASSESSMENT: OS was defined as the time from the date of surgery to death or last contact. The radiomic signature was built based on the least absolute shrinkage and selection operator regression model. A clinicopathologic-radiographic (CPR) model and a combined model integrating radiomic signature with CPR factors were developed with multivariable Cox regression models. STATISTICAL TESTS: Harrell's concordance index (C-index) was used to compare the discrimination of different models. Net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to quantify the improvement of prognostic accuracy after adding radiomic signature. RESULTS: The high-risk patients of death defined by the radiomic signature showed significantly lower OS compared with low-risk patients in validation set (3-year OS 17.1% vs. 56.4%, P < 0.001). Integrating radiomic signature into tumor, node, and metastasis (TNM) staging system significantly improved the prognostic accuracy compared with TNM stage alone (validation set C-index 0.745 vs. 0.649, P = 0.039, NRI improvement 39.9%-43.8%, IDI improvement 16.1%-19.4%). The radiomic signature showed no significant difference of C-index with postoperative CPR model (validation set, 0.698 vs. 0.674, P = 0.752). Incorporating the radiomic signature into CPR model significantly improved prognostic accuracy (NRI improvement 32.5%-34.3%, IDI improvement 8.1%-12.9%). DATA CONCLUSION: Multiparametric MRI-based radiomic signature is a potential biomarker for preoperative prognostic evaluation of ICC patients. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 4.

Potential competing risk of death in older high-risk endometrial carcinoma patients: Results from a multicentric retrospective cohort.

INTRODUCTION: Adjuvant therapeutic decisions in older endometrial carcinoma (EC) patients are challenged by a balance between more frequent aggressive EC and comorbidities. We assessed whether EC and comorbidities are competing or cumulative risks in older EC patients. METHODS: All consecutive patients treated for FIGO stage I-IV EC in two University Hospitals in Paris between 2010 and 2017 were retrospectively included. Patients were categorized as: <70 years (y), >70y without comorbidity (fit), and > 70y with a Charlson comorbidity index>3 (comorbid). Association between high-risk EC (2021-ESGO-ETRO-ESP) or comorbidity, and disease-specific-survival (DSS), was evaluated using Cox model (estimation of cause-specific hazard ratio (CSHR), and Fine-Gray model (subdistribution HR) to account for competing events (death unrelated with EC). RESULTS: Overall, 253 patients were included (median age = 67y, IQR[59-77], median follow-up = 61.5 months, [44.4-76.8]). Among them, 109 (43%) were categorized at high-risk (proportion independent of age), including 67 (26%) who had TP53-mutated tumors. Comorbidity and high-risk group were both associated with all-cause mortality (HR = 4.09, 95%CI[2.29; 7.32] and HR = 3.21, 95%CI [1.69; 6.09], respectively). By multivariate analysis, patients with high-risk EC exhibited poorer DSS, regardless of age/comorbidity (Adjusted-CSHR = 6.62, 95%CI[2.53;17.3]; adjusted-SHR = 6.62 95%CI[2.50;17.5]). Patients>70y-comorbid with high-risk EC had 5-years cumulative incidences of EC-related and EC-unrelated death of 29% and 19%, respectively. In patients <70y, 5-years cumulative incidence of EC-related and EC-unrelated death were 25% and < 1% (one event), respectively. CONCLUSION: High-risk EC patients are exposed to poorer DSS regardless of age/comorbidities, comorbidities and cancer being two cumulative rather than competing risks. Our results suggest that age/comorbidity alone should not lead to underestimate EC-specific survival.

Development and validation of a RNAseq signature for prognostic stratification in endometrial cancer.

BACKGROUND: Despite recent advances in endometrial carcinoma (EC) molecular characterization, its prognostication remains challenging. We aimed to assess whether RNAseq could stratify EC patient prognosis beyond current classification systems. METHODS: A prognostic signature was identified using a LASSO-penalized Cox model trained on TCGA (N = 543 patients). A clinically applicable polyA-RNAseq-based work-flow was developed for validation of the signature in a cohort of stage I-IV patients treated in two Hospitals [2010-2017]. Model performances were evaluated using time-dependent ROC curves (prediction of disease-specific-survival (DSS)). The additional value of the RNAseq signature was evaluated by multivariable Cox model, adjusted on high-risk prognostic group (2021 ESGO-ESTRO-ESP guidelines: non-endometrioid histology or stage III-IVA orTP53-mutated molecular subgroup). RESULTS: Among 209 patients included in the external validation cohort, 61 (30%), 10 (5%), 52 (25%), and 82 (40%), had mismatch repair-deficient, POLE-mutated, TP53-mutated tumors, and tumors with no specific molecular profile, respectively. The 38-genes signature accurately predicted DSS (AUC = 0.80). Most disease-related deaths occurred in high-risk patients (5-years DSS = 78% (95% CI = [68%-89%]) versus 99% [97%-100%] in patients without high-risk). A composite classifier accounting for the TP53-mutated subgroup and the RNAseq signature identified three classes independently associated with DSS: RNAseq-good prognosis (reference, 5-years DSS = 99%), non-TP53 tumors but with RNAseq-poor prognosis (adjusted-hazard ratio (aHR) = 5.75, 95% CI[1.14-29.0]), and TP53-mutated subgroup (aHR = 5.64 [1.12-28.3]). The model accounting for the high-risk group and the composite classifier predicted DSS with AUC = 0.84, versus AUC = 0.76 without (p = 0.01). CONCLUSION: RNA-seq profiling can provide an additional prognostic information to established classification systems, and warrants validation for potential RNAseq-based therapeutic strategies in EC.

Implication of microRNAs in Carcinogenesis with Emphasis on Hematological Malignancies and Clinical Translation.

MicroRNAs (miRNAs) are evolutionarily conserved small non-coding RNAs, that are involved in the multistep process of carcinogenesis, contributing to all established hallmarks of cancer. In this review, implications of miRNAs in hematological malignancies and their clinical utilization fields are discussed. As components of the complex regulatory network of gene expression, influenced by the tissue microenvironment and epigenetic modifiers, miRNAs are "micromanagers" of all physiological processes including the regulation of hematopoiesis and metabolic pathways. Dysregulated miRNA expression levels contribute to both the initiation and progression of acute leukemias, the metabolic reprogramming of malignantly transformed hematopoietic precursors, and to the development of chemoresistance. Since they are highly stable and can be easily quantified in body fluids and tissue specimens, miRNAs are promising biomarkers for the early detection of hematological malignancies. Besides novel opportunities for differential diagnosis, miRNAs can contribute to advanced chemoresistance prediction and prognostic stratification of acute leukemias. Synthetic oligonucleotides and delivery vehicles aim the therapeutic modulation of miRNA expression levels. However, major challenges such as efficient delivery to specific locations, differences of miRNA expression patterns between pediatric and adult hematological malignancies, and potential side effects of miRNA-based therapies should be considered.

Genomic landscape of CD34+ hematopoietic cells in myelodysplastic syndrome and gene mutation profiles as prognostic markers.

Myelodysplastic syndrome (MDS) includes a group of diseases characterized by dysplasia of bone marrow myeloid lineages with ineffective hematopoiesis and frequent evolution to acute myeloid leukemia (AML). Whole-genome sequencing was performed in CD34(+) hematopoietic stem/progenitor cells (HSPCs) from eight cases of refractory anemia with excess blasts (RAEB), the high-risk subtype of MDS. The nucleotide substitution patterns were found similar to those reported in AML, and mutations of 96 protein-coding genes were identified. Clonal architecture analysis revealed the presence of subclones in six of eight cases, whereas mutation detection of CD34(+) versus CD34(-) cells revealed heterogeneity of HSPC expansion status. With 39 marker genes belonging to eight functional categories, mutations were analyzed in 196 MDS cases including mostly RAEB (n = 89) and refractory cytopenia with multilineage dysplasia (RCMD) (n = 95). At least one gene mutation was detected in 91.0% of RAEB, contrary to that in RCMD (55.8%), suggesting a higher mutational burden in the former group. Gene abnormality patterns differed between MDS and AML, with mutations of activated signaling molecules and NPM1 being rare, whereas those of spliceosome more common, in MDS. Finally, gene mutation profiles also bore prognostic value in terms of overall survival and progression free survival.

Prognostic impact of histological subtyping in triple-negative breast cancer.

The impact of special histological types (ST) in triple-negative breast cancer (TNBC) and its association with overall outcome has gained increasing relevance as survival has been linked to specific histological TNBC subtypes. We evaluated the clinicopathological and survival data of 598 patients with 613 TNBCs, including 464 TNBCs of no special type (NST) and 149 TNBCs ST (low-grade, n = 12, 8.1%; high-grade, n = 112, 75.2%; apocrine and androgen receptor-positive [APO AR], n = 25, 16.8%). Patients with low-grade TNBC ST and TNBC ST APO AR were significantly older (P < 0.001) and had a lower Ki67 index (P < 0.001) than those with TNBC NST. Patients with high-grade TNBC ST were significantly older (P = 0.006) and had poorer pathological responses to neoadjuvant chemotherapy (NAC) (P < 0.001) than those with TNBC NST. Significant survival differences were observed between low-grade TNBC ST, TNBC ST APO AR, high-grade TNBC ST, and TNBC NST in the entire study group (DFS, P = 0.002; DDFS, P = 0.001) and in the non-NAC subgroup (OS, P = 0.034; DFS, P = 0.001; DDFS, P < 0.001). Patients with low-grade TNBC ST had the best survival outcomes. Patients with high-grade TNBC ST showed significantly worse outcomes than those with TNBC NST (entire study group: OS, P = 0.049; DFS, P < 0.001; DDFS, P = 0.001; non-NAC subgroup: OS, P = 0.014; DFS, P < 0.001; DDFS, P < 0.001). We conclude that prognostic stratification of TNBC ST is ultimately important for optimizing the therapeutic management of patients with these rare tumor entities.

A single-cell characterised signature integrating heterogeneity and microenvironment of lung adenocarcinoma for prognostic stratification.

BACKGROUND: The high heterogeneity of tumour and the complexity of tumour microenvironment (TME) greatly impacted the tumour development and the prognosis of cancer in the era of immunotherapy. In this study, we aimed to portray the single cell-characterised landscape of lung adenocarcinoma (LUAD), and develop an integrated signature incorporating both tumour heterogeneity and TME for prognosis stratification. METHODS: Single-cell tagged reverse transcription sequencing (STRT-seq) was performed on tumour tissues and matched normal tissues from 14 patients with LUAD for immune landscape depiction and candidate key genes selection for signature construction. Kaplan-Meier survival analyses and in-vitro cell experiments were conducted to confirm the gene functions. The transcriptomic profile of 1949 patients from 11 independent cohorts including nine public datasets and two in-house cohorts were obtained for validation. FINDINGS: We selected 11 key genes closely related to cell-to-cell interaction, tumour development, T cell phenotype transformation, and Ma/Mo cell distribution, including HLA-DPB1, FAM83A, ITGB4, OAS1, FHL2, S100P, FSCN1, SFTPD, SPP1, DBH-AS1, CST3, and established an integrated 11-gene signature, stratifying patients to High-Score or Low-Score group for better or worse prognosis. Moreover, the prognostically-predictive potency of the signature was validated by 11 independent cohorts, and the immunotherapeutic predictive potency was also validated by our in-house cohort treated by immunotherapy. Additionally, the in-vitro cell experiments and drug sensitivity prediction further confirmed the gene function and generalizability of this signature across the entire RNA profile spectrum. INTERPRETATION: This single cell-characterised 11-gene signature might offer insights for prognosis stratification and potential guidance for treatment selection. FUNDING: Support for the study was provided by National key research and development project (2022YFC2505004, 2022YFC2505000 to Z.W. and J.W.), Beijing Natural Science Foundation (7242114 to J.X.), National Natural Science Foundation of China of China (82102886 to J.X., 81871889 and 82072586 to Z.W.), Beijing Nova Program (20220484119 to J.X.), NSFC general program (82272796 to J.W.), NSFC special program (82241229 to J.W.), CAMS Innovation Fund for Medical Sciences (2021-1-I2M-012, 2022-I2M-1-009 to Z.W. and J.W.), Beijing Natural Science Foundation (7212084 to Z.W.), CAMS Key lab of translational research on lung cancer (2018PT31035 to J.W.), Aiyou Foundation (KY201701 to J.W.). Medical Oncology Key Foundation of Cancer Hospital Chinese Academy of Medical Sciences (CICAMS-MOCP2022003 to J.X.).

Bloodstream infection: Derivation and validation of a reliable and multidimensional prognostic score based on a machine learning model (BLISCO).

BACKGROUND: A bloodstream infection (BSI) prognostic score applicable at the time of blood culture collection is missing. METHODS: In total, 4,327 patients with BSIs were included, divided into a derivation (80%) and a validation dataset (20%). Forty-two variables among host-related, demographic, epidemiological, clinical, and laboratory extracted from the electronic health records were analyzed. Logistic regression was chosen for predictive scoring. RESULTS: The 14-day mortality model included age, body temperature, blood urea nitrogen, respiratory insufficiency, platelet count, high-sensitive C-reactive protein, and consciousness status: a score of ≥ 6 was correlated to a 14-day mortality rate of 15% with a sensitivity of 0.742, a specificity of 0.727, and an area under the curve of 0.783. The 30-day mortality model further included cardiovascular diseases: a score of ≥ 6 predicting 30-day mortality rate of 15% with a sensitivity of 0.691, a specificity of 0.699, and an area under the curve of 0.697. CONCLUSIONS: A quick mortality score could represent a valid support for prognosis assessment and resources prioritizing for patients with BSIs not admitted in the intensive care unit.

A cross-species transcriptomic analysis reveals a novel 2-dimensional classification system explaining the invasiveness heterogeneity of pancreatic neuroendocrine tumor.

Pancreatic neuroendocrine tumors (PanNETs), the second most common type of primary pancreatic tumors, display notable heterogeneity in invasiveness. Current knowledge regarding genomic alterations, including DAXX/ATRX, MEN1 mutations, and copy number variations (CNVs), provides some insights into tumor invasiveness. However, the underlying reasons for the significant variation in invasiveness between insulinoma and other types of PanNETs remain unclear. To construct a comprehensive model for the stratification of prognosis, we employed analysis of both the well-established Rip1-Tag2 (RT2) mouse model of PanNETs and human PanNETs with various functional types. Firstly, by applying single-cell and bulk RNA sequencing in PanNETs from different ages and strains of RT2 mice and human PanNETs, we introduced a 2-dimensional (2D) classification system. Based on the 2-D classification system, human PanNETs were mainly classified as benign insulinomas or non-insulinomas subclusters. Non-insulinomas subtypes mainly included gastrinomas, glucagonomas, VIPomas, and NF-PanNETs, which all exhibited potential invasiveness. In addition, we discovered an enrichment of specific CNV patterns and mutations in corresponding human PanNET subclusters. Then we denoted somatic DAXX/ATRX as the 'second hit' and confounding factors for invasiveness. Finally, by combining the 2D system, DAXX/ATRX mutation status, and tumor diameter, a group of indolent PanNETs with minimal recurrence risk was identified. In conclusion, our current work constructed a comprehensive model to elucidate the heterogeneity of invasiveness in PanNETs and improve prognostic stratification.