Identifying high-risk multiple myeloma patients: A novel approach using a clonal gene signature.

Multiple myeloma (MM) is a heterogeneous disease with a small subset of high-risk patients having poor prognoses. Identifying these patients is crucial for treatment management and strategic decisions. In this study, we developed a novel computational framework to define prognostic gene signatures by selecting genes with expression driven by clonal copy number alterations. We applied this framework to MM and developed a clonal gene signature (CGS) consisting of 22 genes and evaluated in five independent datasets. The CGS provided significant prognostic values after adjusting for well-established factors including cytogenetic abnormalities, International Staging System (ISS), and Revised ISS (R-ISS). Importantly, CGS demonstrated higher performance in identifying high-risk patients compared to the GEP70 and SKY92 signatures recommended for prognostic stratification of MM. CGS can further stratify patients into subgroups with significantly differential prognoses when applied to the high- and low-risk groups identified by GEP70 and SKY92. Additionally, CGS scores are significantly associated with patient response to dexamethasone, a commonly used treatment for MM. In summary, we proposed a computational framework that requires only gene expression data to identify CGSs for prognosis prediction. CGS provides a useful biomarker for improving prognostic stratification in MM, especially for identifying the highest-risk patients.

Characterization of driver mutations identifies gene signatures predictive of prognosis and treatment sensitivity in multiple myeloma.

In multiple myeloma (MM), while frequent mutations in driver genes are crucial for disease progression, they traditionally offer limited insights into patient prognosis. This study aims to enhance prognostic understanding in MM by analyzing pathway dysregulations in key cancer driver genes, thereby identifying actionable gene signatures. We conducted a detailed quantification of mutations and pathway dysregulations in 10 frequently mutated cancer driver genes in MM to characterize their comprehensive mutational impacts on the whole transcriptome. This was followed by a systematic survival analysis to identify significant gene signatures with enhanced prognostic value. Our systematic analysis highlighted 2 significant signatures, TP53 and LRP1B, which notably outperformed mere mutation status in prognostic predictions. These gene signatures remained prognostically valuable even when accounting for clinical factors, including cytogenetic abnormalities, the International Staging System (ISS), and its revised version (R-ISS). The LRP1B signature effectively distinguished high-risk patients within low/intermediate-risk categories and correlated with significant changes in the tumor immune microenvironment. Additionally, the LRP1B signature showed a strong association with proteasome inhibitor pathways, notably predicting patient responses to bortezomib and the progression from monoclonal gammopathy of unknown significance to MM. Through a rigorous analysis, this study underscores the potential of specific gene signatures in revolutionizing the prognostic landscape of MM, providing novel clinical insights that could influence future translational oncology research.

Development and validation of a new tool to estimate early mortality in patients with advanced cancer treated with immunotherapy.

BACKGROUND: Immune checkpoint inhibitors (ICIs) are standard treatments for advanced solid cancers. Resistance to ICIs, both primary and secondary, poses challenges, with early mortality (EM) within 30-90 days indicating a lack of benefit. Prognostic factors for EM, including the lung immune prognostic index (LIPI), remain underexplored. METHODS: We performed a retrospective, observational study including patients affected by advanced solid tumors, treated with ICI as single agent or combined with other agents. Logistic regression models identified factors associated with EM and 90-day progression risks. A nomogram for predicting 90-day mortality was built and validated within an external cohort. RESULTS: In total, 637 patients received ICIs (single agent or in combination with other drugs) for advanced solid tumors. Most patients were male (61.9%), with NSCLC as the prevalent tumor (61.8%). Within the cohort, 21.3% died within 90 days, 8.4% died within 30 days, and 34.5% experienced early progression. Factors independently associated with 90-day mortality included ECOG PS 2 and a high/intermediate LIPI score. For 30-day mortality, lung metastasis and a high/intermediate LIPI score were independent risk factors. Regarding early progression, high/intermediate LIPI score was independently associated. A predictive nomogram for 90-day mortality combining LIPI and ECOG PS achieved an AUC of 0.76 (95% CI 0.71-0.81). The discrimination ability of the nomogram was confirmed in the external validation cohort (n = 255) (AUC 0.72, 95% CI 0.64-0.80). CONCLUSION: LIPI and ECOG PS independently were able to estimate 90-day mortality, with LIPI also demonstrating prognostic validity for 30-day mortality and early progression.

Quantification of Diffusion Magnetic Resonance Imaging for Prognostic Prediction of Neonatal Hypoxic-Ischemic Encephalopathy.

Neonatal hypoxic-ischemic encephalopathy (HIE) is the leading cause of acquired neonatal brain injury with the risk of developing serious neurological sequelae and death. An accurate and robust prediction of short- and long-term outcomes may provide clinicians and families with fundamental evidence for their decision-making, the design of treatment strategies, and the discussion of developmental intervention plans after discharge. Diffusion tensor imaging (DTI) is one of the most powerful neuroimaging tools with which to predict the prognosis of neonatal HIE by providing microscopic features that cannot be assessed by conventional magnetic resonance imaging (MRI). DTI provides various scalar measures that represent the properties of the tissue, such as fractional anisotropy (FA) and mean diffusivity (MD). Since the characteristics of the diffusion of water molecules represented by these measures are affected by the microscopic cellular and extracellular environment, such as the orientation of structural components and cell density, they are often used to study the normal developmental trajectory of the brain and as indicators of various tissue damage, including HIE-related pathologies, such as cytotoxic edema, vascular edema, inflammation, cell death, and Wallerian degeneration. Previous studies have demonstrated widespread alteration in DTI measurements in severe cases of HIE and more localized changes in neonates with mild-to-moderate HIE. In an attempt to establish cutoff values to predict the occurrence of neurological sequelae, MD and FA measurements in the corpus callosum, thalamus, basal ganglia, corticospinal tract, and frontal white matter have proven to have an excellent ability to predict severe neurological outcomes. In addition, a recent study has suggested that a data-driven, unbiased approach using machine learning techniques on features obtained from whole-brain image quantification may accurately predict the prognosis of HIE, including for mild-to-moderate cases. Further efforts are needed to overcome current challenges, such as MRI infrastructure, diffusion modeling methods, and data harmonization for clinical application. In addition, external validation of predictive models is essential for clinical application of DTI to prognostication.

An inflammatory gene-related prognostic risk score model for prognosis and immune infiltration in glioblastoma.

This study aimed to screen for key genes related to the prognosis of patients with glioblastoma (GBM). First, bioinformatics analysis was performed based on databases such as TCGA and MSigDB. Inflammatory-related genes were obtained from the MSigDB database. The TCGA-tumor samples were divided into cluster A and B groups based on consensus clustering. Multivariate Cox regression was applied to construct the risk score model of inflammatory-related genes based on the TCGA database. Second, to understand the effects of model characteristic genes on GBM cells, U-87 MG cells were used for knockdown experiments, which are important means for studying gene function. PLAUR is an unfavorable prognostic biomarker for patients with glioma. Therefore, the model characteristic gene PLAUR was selected for knockdown experiments. The prognosis of cluster A was significantly better than that of cluster B. The verification results also demonstrate that the risk score could predict overall survival. Although the immune cells in cluster B and high-risk groups increased, no matching survival advantage was observed. It may be that stromal activation inhibits the antitumor effect of immune cells. PLAUR knockdown inhibits tumor cell proliferation, migration, and invasion, and promoted tumor cell apoptosis. In conclusion, a prognostic prediction model for GBM composed of inflammatory-related genes was successfully constructed. Increased immune cell expression may be linked to a poor prognosis for GBM, as stromal activation decreased the antitumor activity of immune cells in cluster B and high-risk groups. PLAUR may play an important role in tumor cell proliferation, migration, invasion, and apoptosis.

Comprehensive Risk Assessment in Patients With Pulmonary Arterial Hypertension Referred for Lung Transplantation.

BACKGROUND: Whether comprehensive risk assessment predicts post-referral outcome in patients with pulmonary arterial hypertension (PAH) referred for lung transplantation (LT) in Japan is unknown. METHODS AND RESULTS: We retrospectively analyzed 52 PAH patients referred for LT. Risk status at referral was assessed using 3- and 4-strata models from the 2022 European Society of Cardiology and European Respiratory Society guidelines. The 3-strata model intermediate-risk group was further divided into 2 groups based on the median proportion of low-risk variables (modified risk assessment [MRA]). The primary outcome was post-referral mortality. During follow-up, 9 patients died and 13 patients underwent LT. There was no survival difference among 3-strata model groups. The 4-strata model classified 33, 16, and 3 patients as low intermediate, high intermediate, and high risk, respectively. The 4-strata model identified high-risk patients with a 1-year survival rate of 33%, but did not discriminate survival between the intermediate-risk groups. The MRA classified 15, 28, 8, and 1 patients as low, low intermediate, high intermediate, and high risk, respectively. High intermediate- or high-risk patients had worse survival (P<0.001), with 1- and 3-year survival rates of 64% and 34%, respectively. MRA high intermediate- or high-risk classification was associated with mortality (hazard ratio 12.780; 95% confidence interval 2.583-63.221; P=0.002). CONCLUSIONS: Patients classified as high intermediate or high risk by the MRA after treatment should be referred for LT.

Using heart rate variability to develop a predictive model for post-operative cardiovascular complications following major abdominal surgery: A pilot study.

BACKGROUND: Heart Rate Variability (HRV) is a dynamic reflection of heart rhythm regulation by various physiological inputs. HRV deviations have been found to correlate with clinical outcomes in patients under physiological stresses. Perioperative cardiovascular complications occur in up to 5% of adult patients undergoing abdominal surgery and are associated with significantly increased mortality. This pilot study aimed to develop a predictive model for post-operative cardiovascular complications using HRV parameters for early risk stratification and aid post-operative clinical decision-making. METHODS: Adult patients admitted to High Dependency Units after elective major abdominal surgery were recruited. The primary composite outcome was defined as cardiovascular complications within 7 days post-operatively. ECG monitoring for HRV parameters was conducted at three time points (pre-operative, immediately post-operative, and post-operative day 1) and analyzed based on outcome group and time interactions. Candidate HRV predictors were included in a multivariable logistic regression analysis incorporating a stepwise selection algorithm. RESULTS: 89 patients were included in the analysis, with 8 experiencing cardiovascular complications. Three HRV parameters, when measured immediately post-operatively and composited with patient age, provided the basis for a predictive model with AUC of 0.980 (95% CI: 0.953, 1.00). The negative predictive value was 1.00 at a statistically optimal predicted probability cut-off point of 0.16. CONCLUSION: Our model holds potential for accelerating clinical decision-making and aiding in patient triaging post-operatively, using easily acquired HRV parameters. Risk stratification with our model may enable safe early step-down care in patients assessed to have a low risk profile of post-operative cardiovascular complications.

CDK6 as a Biomarker for Immunotherapy, Drug Sensitivity, and Prognosis in Bladder Cancer: Bioinformatics and Immunohistochemical Analysis.

Background: CDK6 is linked to tumor progression and metastasis, although its molecular mechanism and prognostic value are unclear in bladder cancer. Materials and methods: In our study, raw data were obtained from public databases and Single-center retrospective case series. We conducted a bioinformatics analysis and immunohistochemistry to explore the biological landscape of CDK6 in tumors, with a particular focus on bladder cancer. We examined its expression characteristics and prognostic value and performed functional annotation analysis using gene function enrichment. We also assessed the association between bladder cancer molecular subtypes and mutation spectra and analyzed the landscape of the tumor immune microenvironment to predict treatment response sensitivity. Results: Our study found that CDK6 was a potential prognostic marker for bladder cancer. We discovered that bladder cancer patients with high CDK6 expression do not respond well to immunotherapy and have a poor prognosis. CDK6 regulates tumor immune status, metabolism, and cell cycle-related signaling pathways, thereby influencing tumor biological behavior. Furthermore, CDK6 mediated the suppression of the immune microenvironment to weaken anti-tumor immune responses. Finally, a comprehensive characterization of CDK6 was applied in the prognostic prediction of bladder cancer, suggesting that targeting CDK6 represents a potential therapeutic option. Conclusions: These results indicated that CDK6 is an independent biomarker for predicting prognosis and immunotherapy efficacy of bladder cancer. A deeper understanding of its specific molecular mechanisms may provide new treatment strategies.

Investigation of statistical methods used in prognostic prediction models for obstetric care: A 10 year-span cross-sectional study.

INTRODUCTION: Obstetric care is a highly active area in the development and application of prognostic prediction models. The development and validation of these models often require the utilization of advanced statistical techniques. However, failure to adhere to rigorous methodological standards could greatly undermine the reliability and trustworthiness of the resultant models. Consequently, the aim of our study was to examine the current statistical practices employed in obstetric care and offer recommendations to enhance the utilization of statistical methods in the development of prognostic prediction models. MATERIAL AND METHODS: We conducted a cross-sectional survey using a sample of studies developing or validating prognostic prediction models for obstetric care published in a 10-year span (2011-2020). A structured questionnaire was developed to investigate the statistical issues in five domains, including model derivation (predictor selection and algorithm development), model validation (internal and external), model performance, model presentation, and risk threshold setting. On the ground of survey results and existing guidelines, a list of recommendations for statistical methods in prognostic models was developed. RESULTS: A total of 112 eligible studies were included, with 107 reporting model development and five exclusively reporting external validation. During model development, 58.9% of the studies did not include any form of validation. Of these, 46.4% used stepwise regression in a crude manner for predictor selection, while two-thirds made decisions on retaining or dropping candidate predictors solely based on p-values. Additionally, 26.2% transformed continuous predictors into categorical variables, and 80.4% did not consider nonlinear relationships between predictors and outcomes. Surprisingly, 94.4% of the studies did not examine the correlation between predictors. Moreover, 47.1% of the studies did not compare population characteristics between the development and external validation datasets, and only one-fifth evaluated both discrimination and calibration. Furthermore, 53.6% of the studies did not clearly present the model, and less than half established a risk threshold to define risk categories. In light of these findings, 10 recommendations were formulated to promote the appropriate use of statistical methods. CONCLUSIONS: The use of statistical methods is not yet optimal. Ten recommendations were offered to assist the statistical methods of prognostic prediction models in obstetric care.

The early prediction of gestational diabetes mellitus by machine learning models.

BACKGROUND: We aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with sociodemographic and obstetrics features in the pre-conceptional period. METHODS: We collected the data of pregnant women who were admitted to the obstetric clinic in the first trimester. The maternal age, body mass index, gravida, parity, previous birth weight, smoking status, the first-visit venous plasma glucose level, the family history of diabetes mellitus, and the results of an oral glucose tolerance test of the patients were evaluated. The women were categorized into groups based on having and not having a GDM diagnosis and also as being nulliparous or primiparous. 7 common ML algorithms were employed to construct the predictive model. RESULTS: 97 mothers were included in the study. 19 and 26 nulliparous were with and without GDM, respectively. 29 and 23 primiparous were with and without GDM, respectively. It was found that the greatest feature importance variables were the venous plasma glucose level, maternal BMI, and the family history of diabetes mellitus. The eXtreme Gradient Boosting (XGB) Classifier had the best predictive value for the two models with the accuracy of 66.7% and 72.7%, respectively. DISCUSSION: The XGB classifier model constructed with maternal sociodemographic findings and the obstetric history could be used as an early prediction model for GDM especially in low-income countries.

Prognostic communication in amyotrophic lateral sclerosis: findings from a Nationwide Italian survey.

BACKGROUND: Amyotrophic Lateral Sclerosis (ALS) is a fatal motor neuron disease with a highly variable prognosis. Among the proposed prognostic models, the European Network for the cure of ALS (ENCALS) survival model has demonstrated good predictive performance. However, few studies have examined prognostic communication and the diffusion of prognostic algorithms in ALS care. OBJECTIVE: To investigate neurologists' attitudes toward prognostic communication and their knowledge and utilization of the ENCALS survival model in clinical practice. METHODS: A web-based survey was administered between May 2021 and March 2022 to the 40 Italian ALS Centers members of the Motor Neuron Disease Study Group of the Italian Society of Neurology. RESULTS: Twenty-two out of 40 (55.0%) Italian ALS Centers responded to the survey, totaling 37 responses. The model was known by 27 (73.0%) respondents. However, it was predominantly utilized for research (81.1%) rather than for clinical prognostic communication (7.4%). Major obstacles to prognostic communication included the unpredictability of disease course, fear of a negative impact on patients or caregivers, dysfunctional reaction to diagnosis, and cognitive impairment. Nonetheless, the model was viewed as potentially useful for improving clinical management, increasing disease awareness, and facilitating care planning, especially end-of-life planning. CONCLUSIONS: Despite the widespread recognition and positive perceptions of the ENCALS survival model among Italian neurologists with expertise in ALS, its implementation in clinical practice remains limited. Addressing this disparity may require systematic investigations and targeted training to integrate tailored prognostic communication into ALS care protocols, aligning with the growing availability of prognostic tools for ALS.

Prognostic potential of lipid profiling in cancer patients: a systematic review of mass spectrometry-based studies.

Cancer prognosis remains a critical clinical challenge. Lipidomic analysis via mass spectrometry (MS) offers the potential for objective prognostic prediction, leveraging the distinct lipid profiles of cancer patient-derived specimens. This review aims to systematically summarize the application of MS-based lipidomic analysis in prognostic prediction for cancer patients. Our systematic review summarized 38 studies from the past decade that attempted prognostic prediction of cancer patients through lipidomics. Commonly analyzed cancers included colorectal, prostate, and breast cancers. Liquid (serum and urine) and tissue samples were equally used, with liquid chromatography-tandem MS being the most common analytical platform. The most frequently evaluated prognostic outcomes were overall survival, stage, and recurrence. Thirty-eight lipid markers (including phosphatidylcholine, ceramide, triglyceride, lysophosphatidylcholine, sphingomyelin, phosphatidylethanolamine, diacylglycerol, phosphatidic acid, phosphatidylserine, lysophosphatidylethanolamine, lysophosphatidic acid, dihydroceramide, prostaglandin, sphingosine-1-phosphate, phosphatidylinosito, fatty acid, glucosylceramide and lactosylceramide) were identified as prognostic factors, demonstrating potential for clinical application. In conclusion, the potential for developing lipidomics in cancer prognostic prediction was demonstrated. However, the field is still nascent, necessitating future studies for validating and establishing lipid markers as reliable prognostic tools in clinical practice.

Prospective external validation of radiomics-based predictive model of distant metastasis after dynamic tumor tracking stereotactic body radiation therapy in patients with non-small-cell lung cancer: A multi-institutional analysis.

BACKGROUND AND PURPOSE: This study aims to externally validate a predictive model for distant metastasis (DM) with computed tomography (CT)-based radiomics features in prospectively enrolled non-small-cell lung cancer patients undergoing dynamic tumor-tracking stereotactic body radiation therapy (DTT-SBRT). MATERIALS AND METHODS: The study collected retrospective data from 567 patients across 11 institutions as the training dataset and prospectively enrolled 42 patients from four institutions as the external test dataset. Four clinical features were collected, and 944 CT-based radiomic features were extracted from gross tumor volumes. After standardization and feature selection, DM predictive models were developed using fine and gray regression (FG) and random survival forest (RSF), incorporating clinical and radiomic features, and their combinations within the training dataset. Then, the model was applied to the test dataset, dividing patients into high- and low-risk groups based on medians of risk scores. Model performance was assessed using the concordance index (C-index), and the statistical significance between groups was evaluated using Gray's test. RESULTS: In the training dataset, 122 of 567 patients (21.5%) developed DM, compared to 9 of 42 patients (21.4%) in the test dataset. In the test dataset, the C-indices of the clinical, radiomics, and hybrid models with FG were 0.559, 0.544, and 0.560, respectively, whereas those with RSF were 0.576, 0.604, and 0.627, respectively. The hybrid model with RSF, which exhibited the best predictive performance of all models, identified 7 of 23 patients (30.4%) as high risk and 2 of 19 patients (10.5%) as low risk for DM incidence in the test dataset (p = 0.116). CONCLUSION: Although predictive models for DM lack significance when applied to prospectively enrolled cases undergoing DTT-lung SBRT, the model with RSF exhibits a consistent capacity to effectively classify patients at a high risk of developing DM.

Deep radiomics-based prognostic prediction of oral cancer using optical coherence tomography.

BACKGROUND: This study aims to evaluate the integration of optical coherence tomography (OCT) and peripheral blood immune indicators for predicting oral cancer prognosis by artificial intelligence. METHODS: In this study, we examined patients undergoing radical oral cancer resection and explored inherent relationships among clinical data, OCT images, and peripheral immune indicators for oral cancer prognosis. We firstly built a peripheral blood immune indicator-guided deep learning feature representation method for OCT images, and further integrated a multi-view prognostic radiomics model incorporating feature selection and logistic modeling. Thus, we can assess the prognostic impact of each indicator on oral cancer by quantifying OCT features. RESULTS: We collected 289 oral mucosal samples from 68 patients, yielding 1,445 OCT images. Using our deep radiomics-based prognosis model, it achieved excellent discrimination for oral cancer prognosis with the area under the receiver operating characteristic curve (AUC) of 0.886, identifying systemic immune-inflammation index (SII) as the most informative feature for prognosis prediction. Additionally, the deep learning model also performed excellent results with 85.26% accuracy and 0.86 AUC in classifying the SII risk. CONCLUSIONS: Our study effectively merged OCT imaging with peripheral blood immune indicators to create a deep learning-based model for inflammatory risk prediction in oral cancer. Additionally, we constructed a comprehensive multi-view radiomics model that utilizes deep learning features for accurate prognosis prediction. The study highlighted the significance of the SII as a crucial indicator for evaluating patient outcomes, corroborating our clinical statistical analyses. This integration underscores the potential of combining imaging and blood indicators in clinical decision-making. TRIAL REGISTRATION: The clinical trial associated with this study was prospectively registered in the Chinese Clinical Trial Registry with the trial registration number (TRN) ChiCTR2200064861. The registration was completed on 2021.

[Establishment and verification of a prognostic nomogram for survival of tongue squamous cell carcinoma patients who underwent cervical dissection].

OBJECTIVE: To evaluate the prognostic significance of inflammatory biomarkers, prognostic nutritional index and clinicopathological characteristics in tongue squamous cell carcinoma (TSCC) patients who underwent cervical dissection. METHODS: The retrospective cohort study consisted of 297 patients undergoing tumor resection for TSCC between January 2017 and July 2018. The study population was divided into the training set and validation set by 7 :3 randomly. The peripheral blood indices of interest were preoperative neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), systemic inflammation score (SIS) and prognostic nutritional index (PNI). Kaplan-Meier survival analysis and multivariable Cox regression analysis were used to evaluate independent prognostic factors for overall survival (OS) and disease-specific survival (DSS). The nomogram's accuracy was internally validated using concordance index, receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration plot and decision curve analysis. RESULTS: According to the univariate Cox regression analysis, clinical TNM stage, clinical T category, clinical N category, differentiation grade, depth of invasion (DOI), tumor size and pre-treatment PNI were the prognostic factors of TSCC. Multivariate Cox regression analysis revealed that pre-treatment PNI, clinical N category, DOI and tumor size were independent prognostic factors for OS or DSS (P < 0.05). Positive neck nodal status (N≥1), PNI≤50.65 and DOI > 2.4 cm were associated with the poorer 5-year OS, while a positive neck nodal status (N≥1), PNI≤50.65 and tumor size > 3.4 cm were associated with poorer 5-year DSS. The concordance index of the nomograms based on independent prognostic factors was 0.708 (95%CI, 0.625-0.791) for OS and 0.717 (95%CI, 0.600-0.834) for DSS. The C-indexes for external validation of OS and DSS were 0.659 (95%CI, 0.550-0.767) and 0.780 (95%CI, 0.669-0.890), respectively. The 1-, 3- and 5-year time-dependent ROC analyses (AUC = 0.66, 0.71 and 0.72, and AUC = 0.68, 0.77 and 0.79, respectively) of the nomogram for the OS and DSS pronounced robust discriminative ability of the model. The calibration curves showed good agreement between the predicted and actual observations of OS and DSS, while the decision curve confirmed its pronounced application value. CONCLUSION: Pre-treatment PNI, clinical N category, DOI and tumor size can potentially be used to predict OS and DSS of patients with TSCC. The prognostic nomogram based on these variables exhibited good accurary in predicting OS and DSS in patients with TSCC who underwent cervical dissection. They are effective tools for predicting survival and helps to choose appropriate treatment strategies to improve the prognosis.

Cuproptosis-related lncRNA signature for prognostic prediction in patients with acute myeloid leukemia.

BACKGROUND: Long non-coding RNAs (lncRNAs) have been reported to have a crucial impact on the pathogenesis of acute myeloid leukemia (AML). Cuproptosis, a copper-triggered modality of mitochondrial cell death, might serve as a promising therapeutic target for cancer treatment and clinical outcome prediction. Nevertheless, the role of cuproptosis-related lncRNAs in AML is not fully understood. METHODS: The RNA sequencing data and demographic characteristics of AML patients were downloaded from The Cancer Genome Atlas database. Pearson correlation analysis, the least absolute shrinkage and selection operator algorithm, and univariable and multivariable Cox regression analyses were applied to identify the cuproptosis-related lncRNA signature and determine its feasibility for AML prognosis prediction. The performance of the proposed signature was evaluated via Kaplan-Meier survival analysis, receiver operating characteristic curves, and principal component analysis. Functional analysis was implemented to uncover the potential prognostic mechanisms. Additionally, quantitative real-time PCR (qRT-PCR) was employed to validate the expression of the prognostic lncRNAs in AML samples. RESULTS: A signature consisting of seven cuproptosis-related lncRNAs (namely NFE4, LINC00989, LINC02062, AC006460.2, AL353796.1, PSMB8-AS1, and AC000120.1) was proposed. Multivariable cox regression analysis revealed that the proposed signature was an independent prognostic factor for AML. Notably, the nomogram based on this signature showed excellent accuracy in predicting the 1-, 3-, and 5-year survival (area under curve = 0.846, 0.801, and 0.895, respectively). Functional analysis results suggested the existence of a significant association between the prognostic signature and immune-related pathways. The expression pattern of the lncRNAs was validated in AML samples. CONCLUSION: Collectively, we constructed a prediction model based on seven cuproptosis-related lncRNAs for AML prognosis. The obtained risk score may reveal the immunotherapy response in patients with this disease.

Characterization of myeloid signature genes for predicting prognosis and immune landscape in Ewing sarcoma.

Myeloid cells as a highly heterogeneous subpopulation of the tumor microenvironment (TME) are intimately associated with tumor development. Ewing sarcoma (EWS) is characterized by abundant myeloid cell infiltration in the TME. However, the correlation between myeloid signature genes (MSGs) and the prognosis of EWS patients was unclear. In this research, we synthetically characterized the expression of MSGs in a training cohort and classified EWS patients into two subtypes. Immune cell infiltration analysis revealed that MSGs subtypes correlated closely with different immune statuses. Furthermore, a three-gene prognostic model (CTSD, SIRPA, and FN1) was constructed by univariate, LASSO, and multivariate Cox analysis, and it showed excellent prognostic accuracy in EWS patients. We also developed a nomogram for better predicting the long-term survival of EWS. Functional enrichment analysis showed immune-related pathways were distinctly different in the high- and low-risk groups. Further analysis revealed that patients in the high-risk group were tightly associated with an immunosuppressive microenvironment. Finally, we validated the expression of these candidate genes by Western blot (WB), qPCR, and immunohistochemistry (IHC) analysis. To sum up, our study identified that the MSGs model was strongly linked to prognostic prediction and immune infiltration in EWS patients, providing novel insights into the clinical treatment and management of EWS patients.

Characterization of starvation response-related genes for predicting prognosis in breast cancer.

Breast cancer (BRCA) cells typically exist in nutrient-deficient microenvironments and quickly adapt to states with fluctuating nutrient levels. The tumor microenvironment of starvation is intensely related to metabolism and the malignant progression of BRCA. However, the potential molecular mechanism has not been thoroughly scrutinized. As a result, this study aimed to dissect the prognostic implications of mRNAs involved in the starvation response and construct a signature for forecasting the outcomes of BRCA. In this research, we investigated how starvation could affect BRCA cells' propensities for invasion and migration. The effects of autophagy and glucose metabolism mediated by starved stimulation were examined through transwell assays, western blot, and the detection of glucose concentration. A starvation response-related gene (SRRG) signature was ultimately generated by integrated analysis. The risk score was recognized as an independent risk indicator. The nomogram and calibration curves revealed that the model had excellent prediction accuracy. Functional enrichment analysis indicated this signature was significantly enriched in metabolic-related pathways and energy stress-related biological processes. Furthermore, phosphorylated protein expression of the model core gene EIF2AK3 increased after the stimulus of starvation, and EIF2AK3 may play an essential role in the progression of BRCA in the starved microenvironment. To sum up, we constructed and validated a novel SRRG signature that could accurately predict outcomes and may be developed as a therapeutic target for the precise treatment of BRCA.

Molecular Signature Predictive of Long-Term Liver Fibrosis Progression to Inform Antifibrotic Drug Development.

BACKGROUND & AIMS: There is a major unmet need to assess the prognostic impact of antifibrotics in clinical trials because of the slow rate of liver fibrosis progression. We aimed to develop a surrogate biomarker to predict future fibrosis progression. METHODS: A fibrosis progression signature (FPS) was defined to predict fibrosis progression within 5 years in patients with hepatitis C virus and nonalcoholic fatty liver disease (NAFLD) with no to minimal fibrosis at baseline (n = 421) and was validated in an independent NAFLD cohort (n = 78). The FPS was used to assess response to 13 candidate antifibrotics in organotypic ex vivo cultures of clinical fibrotic liver tissues (n = 78) and cenicriviroc in patients with nonalcoholic steatohepatitis enrolled in a clinical trial (n = 19, NCT02217475). A serum protein-based surrogate FPS was developed and tested in a cohort of compensated cirrhosis patients (n = 122). RESULTS: A 20-gene FPS was defined and validated in an independent NAFLD cohort (adjusted odds ratio, 10.93; area under the receiver operating characteristic curve, 0.86). Among computationally inferred fibrosis-driving FPS genes, BCL2 was confirmed as a potential pharmacologic target using clinical liver tissues. Systematic ex vivo evaluation of 13 candidate antifibrotics identified rational combination therapies based on epigallocatechin gallate, which were validated for enhanced antifibrotic effect in ex vivo culture of clinical liver tissues. In patients with nonalcoholic steatohepatitis treated with cenicriviroc, FPS modulation was associated with 1-year fibrosis improvement accompanied by suppression of the E2F pathway. Induction of the PPARα pathway was absent in patients without fibrosis improvement, suggesting a benefit of combining PPARα agonism to improve the antifibrotic efficacy of cenicriviroc. A 7-protein serum protein-based surrogate FPS was associated with the development of decompensation in cirrhosis patients. CONCLUSION: The FPS predicts long-term fibrosis progression in an etiology-agnostic manner, which can inform antifibrotic drug development.

Use of platelet inhibitors for digital ulcers related to systemic sclerosis: EUSTAR study on derivation and validation of the DU-VASC model.

OBJECTIVE: To develop and validate the prognostic prediction model DU-VASC to assist the clinicians in decision-making regarding the use of platelet inhibitors (PIs) for the management of digital ulcers in patients with systemic sclerosis. Secondly, to assess the incremental value of PIs as predictor. METHODS: We analysed patient data from the European Scleroderma Trials and Research group registry (one time point assessed). Three sets of derivation/validation cohorts were obtained from the original cohort. Using logistic regression, we developed a model for prediction of digital ulcers (DUs). C-Statistics and calibration plots were calculated to evaluate the prediction performance. Variable importance plots and the decrease in C-statistics were used to address the importance of the predictors. RESULTS: Of 3710 patients in the original cohort, 487 had DUs and 90 were exposed to PIs. For the DU-VASC model, which includes 27 predictors, we observed good calibration and discrimination in all cohorts (C-statistic = 81.1% [95% CI: 78.9%, 83.4%] for the derivation and 82.3% [95% CI: 779.3%, 85.3%] for the independent temporal validation cohort). Exposure to PIs was associated with absence of DUs and was the most important therapeutic predictor. Further important factors associated with absence of DUs were lower modified Rodnan skin score, anti-Scl-70 negativity and normal CRP. Conversely, the exposure to phosphodiesterase-5 inhibitor, prostacyclin analogues or endothelin receptor antagonists seemed to be associated with the occurrence of DUs. Nonetheless, previous DUs remains the most impactful predictor of DUs. CONCLUSION: The DU-VASC model, with good calibration and discrimination ability, revealed that PI treatment was the most important therapy-related predictor associated with reduced DU occurrence.