Immunophenotypic clustering in paediatric acute myeloid leukaemia.

Acute myeloid leukaemia (AML) is a highly heterogeneous disease, exhibiting diverse subtypes according to the characteristics of tumour cells. The immunophenotype is one of the aspects acquired routinely through flow cytometry in the diagnosis of AML. Here, we characterized the antigen expression in paediatric AML cases across both morphological and molecular genetic subgroups. We discovered a subgroup of patients with unfavourable prognosis that can be immunologically characterized, irrespective of morphological FAB results or genetic aberrations. Cox regression analysis unveiled key antigens influencing the prognosis of AML patients. In terms of underlying genotypes, we observed that the antigenic profiles and outcomes of one specific group, primarily composed of CBFA2T3::GLIS2 and FUS::ERG, were analogous to the reported RAM phenotype. Overall, our data highlight the significance of immunophenotype to tailor treatment for paediatric AML.

circRNAs as prognostic markers in pediatric acute myeloid leukemia.

Circular RNAs (circRNAs) arise from precursor mRNA processing through back-splicing and have been increasingly recognized for their functions in various cancers including acute myeloid leukemia (AML). However, the prognostic implications of circRNA in AML remain unclear. We conducted a comprehensive genome-wide analysis of circRNAs using RNA-seq data in pediatric AML. We revealed a group of circRNAs associated with inferior outcomes, exerting effects on cancer-related pathways. Several of these circRNAs were transcribed directly from genes with established functions in AML, such as circRUNX1, circWHSC1, and circFLT3. Further investigations indicated the increased number of circRNAs and linear RNAs splicing were significantly correlated with inferior clinical outcomes, highlighting the pivotal role of splicing dysregulation. Subsequent analysis identified a group of upregulated RNA binding proteins in AMLs associated with high number of circRNAs, with TROVE2 being a prominent candidate, suggesting their involvement in circRNA associated prognosis. Through the integration of drug sensitivity data, we pinpointed 25 drugs that could target high-risk AMLs characterized by aberrant circRNA transcription. These findings underscore prognostic significance of circRNAs in pediatric AML and offer an alternative perspective for treating high-risk cases in this malignancy.

Bone marrow stromal cells dictate lanosterol biosynthesis and ferroptosis of multiple myeloma.

Ferroptosis has been demonstrated a promising way to counteract chemoresistance of multiple myeloma (MM), however, roles and mechanism of bone marrow stromal cells (BMSCs) in regulating ferroptosis of MM cells remain elusive. Here, we uncovered that MM cells were more susceptible to ferroptotic induction under the interaction of BMSCs using in vitro and in vivo models. Mechanistically, BMSCs elevated the iron level in MM cells, thereby activating the steroid biosynthesis pathway, especially the production of lanosterol, a major source of reactive oxygen species (ROS) in MM cells. We discovered that direct coupling of CD40 ligand and CD40 receptor constituted the key signaling pathway governing lanosterol biosynthesis, and disruption of CD40/CD40L interaction using an anti-CD40 neutralizing antibody or conditional depletion of Cd40l in BMSCs successfully eliminated the iron level and lanosterol production of MM cells localized in the Vk*MYC Vk12653 or NSG mouse models. Our study deciphers the mechanism of BMSCs dictating ferroptosis of MM cells and highlights the therapeutic potential of non-apoptosis strategies for managing refractory or relapsed MM patients.

Human emissions of size-resolved fluorescent bioaerosols in control situations.

Human bioaerosols contribute significantly to indoor air quality. This study used a Wideband Integrated Bioaerosol Sensor (WIBS-4A) instrument for real-time measurement of particle size distribution and count to differentiate fluorescent bioaerosols from non-fluorescent aerosols. Through an experiment involving 12 subjects (six men and six women) wearing standard cotton clothing in a 2 m × 2 m × 2 m environmental chamber, we established a quantitative method to obtain the bioaerosol emission rate of a single subject, aiming to explore the effects of masks and sex on bioaerosol emissions from different individuals. The mean emission rates of fluorescent bioaerosols in the particle size ranges of 0.5-2.5 µm and 2.5-10 µm were 3.192±2.11×104 counts/(person·h) and 13.98±9.34×104 counts/(person·h), respectively. A comparison between those wearing and not wearing masks revealed no significant differences in the emissions of fluorescent bioaerosols. This suggests respiratory sources may not significantly impact the emissions of fluorescent bioaerosols from individuals under seated breathing conditions. Significant disparities in the fluorescent bioaerosol emission rates of different biological sexes were observed through independent sample analysis. Males exhibited 41 % and 15 % higher emission rates than females for particle size ranges of 0.5-2.5 µm and 2.5-10 µm, respectively, possibly because of different metabolic rates. A significant correlation between metabolic rates and fluorescent bioaerosols (sig = 0.044 < 0.05) was observed in all the subjects. These findings underscore the individual variations that affect bioaerosol emission rates. The data provided by this study will facilitate further analysis of the on-site measured data and source analysis.

High-dose methotrexate pharmacokinetics and its impact on prognosis of paediatric acute lymphoblastic leukaemia patients: A population pharmacokinetic study.

This study delivers a comprehensive evaluation of the efficacy and pharmacokinetics of high-dose methotrexate (HDMTX) in a large cohort of Chinese paediatric acute lymphoblastic leukaemia patients. A total of 533 patients were included in the prognostic analysis. An association was observed between lower steady-state MTX concentrations (<56 µmol/L) and poorer outcomes in intermediate-/high-risk (IR/HR) patients. Subgroup analysis further revealed that this relationship between concentrations and prognosis was even more pronounced in patients with MLL rearrangements. In contrast, such an association did not emerge within the low-risk patient group. Additionally, utilizing population pharmacokinetic modelling (6051 concentrations from 815 patients), we identified the significant impact of physiological maturation, estimated glomerular filtration rate, sex and concurrent dasatinib administration on MTX pharmacokinetics. Simulation-based recommendations include a reduced dosage regimen for those with renal insufficiency and a specific 200 mg/kg dosage for infants under 1 year. The findings underscore the critical role of HDMTX in treating IR/HR populations and call for a reassessment of its application in lower-risk groups. An individualized pharmacokinetic dosage regimen could achieve the most optimal results, ensuring the largest proportion of steady-state concentrations within the optimal range.

Genome-Wide Bioinformatics Analysis of SWEET Gene Family and Expression Verification of Candidate PaSWEET Genes in Potentilla anserina.

Sugars act as the main energy sources in many fruit and vegetable crops. The biosynthesis and transportation of sugars are crucial and especially contribute to growth and development. SWEET is an important gene family that plays a vital role in plants' growth, development, and adaptation to various types of stresses (biotic and abiotic). Although SWEET genes have been identified in numerous plant species, there is no information on SWEETs in Potentilla anserina. In the present study, we performed a comprehensive genome-wide bioinformatics analysis and identified a total of 23 candidate PaSWEETs genes in the Potentilla anserina genome, which were randomly distributed on ten different chromosomes. The phylogenetic analysis, chromosomal location, gene structure, specific cis-elements, protein interaction network, and physiological characteristics of these genes were systematically examined. The identified results of the phylogenetic relationship with Arabidopsis thaliana revealed that these PaSWEET genes were divided into four clades (I, II, III, and IV). Moreover, tissue-specific gene expression through quantitative real-time polymerase chain reaction (qRT-PCR) validation exposed that the identified PaSWEETs were differentially expressed in various tissues (roots, stems, leaves, and flowers). Mainly, the relative fold gene expression in swollen and unswollen tubers effectively revealed that PaSWEETs (7, 9, and 12) were highly expressed (300-, 120-, and 100-fold) in swollen tubers. To further elucidate the function of PaSWEETs (7, 9, and 12), their subcellular location was confirmed by inserting them into tobacco leaves, and it was noted that these genes were present on the cell membrane. On the basis of the overall results, it is suggested that PaSWEETs (7, 9, and 12) are the candidate genes involved in swollen tuber formation in P. anserina. In crux, we speculated that our study provides a valuable theoretical base for further in-depth function analysis of the PaSWEET gene family and their role in tuber development and further enhancing the molecular breeding of Potentilla anserina.

Identifying Hub Genes for Glaucoma Based on Bulk RNA Sequencing Data and Multi-machine Learning Models.

AIMS: The aims of this study were to determine hub genes in glaucoma through multiple machine learning algorithms. BACKGROUND: Glaucoma has afflicted many patients for many years, with excessive pressure in the eye continuously damaging the nervous system and leading to severe blindness. An effective molecular diagnostic method is currently lacking. OBJECTIVE: The present study attempted to reveal the molecular mechanism and gene regulatory network of hub genes in glaucoma, followed by an attempt to reveal the drug-gene-disease network regulated by hub genes. METHODS: A microarray sequencing dataset (GSE9944) was obtained through the Gene Expression Omnibus database. The differentially expressed genes in Glaucoma were identified. Based on these genes, we constructed three machine learning models for feature training, Random Forest model (RF), Least absolute shrinkage and selection operator regression model (LASSO), and Support Vector Machines model (SVM). Meanwhile, Weighted Gene Co-Expression Network Analysis (WGCNA) was performed for GSE9944 expression profiles to identify Glaucoma-related genes. The overlapping genes in the four groups were considered as hub genes of Glaucoma. Based on these genes, we also constructed a molecular diagnostic model of Glaucoma. In this study, we also performed molecular docking analysis to explore the gene-drug network targeting hub genes. In addition, we evaluated the immune cell infiltration landscape in Glaucoma samples and normal samples by applying CIBERSORT method. RESULTS: 8 hub genes were determined: ATP6V0D1, PLEC, SLC25A1, HRSP12, PKN1, RHOD, TMEM158 and GSN. The diagnostic model showed excellent diagnostic performance (area under the curve=1). GSN might positively regulate T cell CD4 naïve as well as negatively regulate T cell regulation (Tregs). In addition, we constructed gene-drug networks in an attempt to explore novel therapeutic agents for Glaucoma. CONCLUSION: Our results systematically determined 8 hub genes and established a molecular diagnostic model that allowed the diagnosis of Glaucoma. Our study provided a basis for future systematic studies of Glaucoma pathogenesis.

Who are optimal candidates for primary tumor resection in patients with metastatic gastric adenocarcinoma? A population-based study.

BACKGROUND: The research aimed to construct a novel predictive nomogram to identify specific metastatic gastric adenocarcinoma (mGAC) populations who could benefit from primary tumor resection (PTR). METHOD: Patients with mGAC were included in the SEER database and divided into PTR and non-PTR groups. The Kaplan-Meier analysis, propensity score matching (PSM), least absolute shrink and selection operator (LASSO) regression, multivariable logistic regression, and multivariate Cox regression methods were then used. Finally, the prediction nomograms were built and tested. RESULTS: 3185 patients with mGAC were enrolled. Among the patients, 679 cases underwent PTR while the other 2506 patients didn't receive PTR. After PSM, the patients in the PTR group presented longer median overall survival (15.0 vs. 7.0 months, p < 0.001). Among the PTR group, 307 (72.9%) patients obtained longer overall survival than seven months (beneficial group). Then the LASSO logistic regression was performed, and gender, grade, T stage, N stage, pathology, and chemotherapy were included to construct the nomogram. In both the training and validation cohorts, the nomogram exhibited good discrimination (AUC: 0.761 and 0.753, respectively). Furthermore, the other nomogram was constructed to predict 3-, 6-, and 12-month cancer-specific survival based on the variables from the multivariate Cox analysis. The 3-, 6-, and 12-month AUC values were 0.794, 0.739, and 0.698 in the training cohort, and 0.805, 0.759, and 0.695 in the validation cohorts. The calibration curves demonstrated relatively good consistency between the predicted and observed probabilities of survival in two nomograms. The models' clinical utility was revealed through decision curve analysis. CONCLUSION: The benefit nomogram could guide surgeons in decision-making and selecting optimal candidates for PTR among mGAC patients. And the prognostic nomogram presented great prediction ability for these patients.

Unraveling the Role of Metal Vacancy Sites and Doped Nitrogen in Enhancing Pseudocapacitance Performance of Defective MXene.

Nitrogen-doped titanium carbides (MXene) films exhibit extraordinary volumetric capacitance when high-concentration sulfuric acid electrolyte is utilized owing to the enhancement of pseudocapacitance. However, the energy storage mechanism of nitrogen-doped MXene is unclear due to the complex electrode structure and electrolyte ions' behavior. Here, based on pristine MXene (Ti3C2O2), three different MXene structures are constructed by introducing metal vacancy sites and doped nitrogen atoms, namely, defective MXene (Ti2.9C2O2), nitrogen-doped MXene (Ti3C2O1.9N0.1), and nitrogen-doped MXene with metal vacancy sites (Ti2.9C2O1.9N0.1). Then, the density functional theory (DFT)-based calculations coupled with the effective screening medium reference interaction site method (ESM-RISM) are applied to reveal the electrochemical behavior at the electrode/electrolyte interfacial area. Through analyzing the electronic structure, electrical double-layer capacitance (EDLC), and equilibrium potential of the pseudocapacitance reaction, the specific effect of structural changes on their performance can be clarified: metal vacancy sites can reduce the potential difference of gap layer (Outer Helmholtz plane) at charged state and increase the electronic capacity of Ti, which can be used to explain the high pseudocapacitance, low charge transfer resistance and high-rate capacity properties of nitrogen-doped MXene observed in experiments.

Comparative analysis of machine learning-based ultrasound radiomics in predicting malignancy of partially cystic thyroid nodules.

OBJECTIVE: To investigate the application of machine learning (ML) model-based thyroid ultrasound radiomics in the evaluation of malignancy in partially cystic thyroid nodules (PCTNs). METHODS: One hundred and ninety-two patients with 197 nodules PCTNs from January 2020 to December 2020 were retrospectively analyzed. Radiomics features were extracted based on hand-crafted features from the ultrasound images, and machine learning methods were used to build a classification model by radiomics features. The least absolute shrinkage and selection operator regression was applied to select the features of nonzero coefficients from radiomics features. The prediction performance of the established model was mainly evaluated by the area under the curve (AUC) and accuracy, sensitivity, and specificity. RESULTS: Nineteen radiomics features were extracted from the original images for each nodule. Eight ML classifiers were able to differentiate malignancy in PCTNs. The AUC, accuracy, sensitivity, and specificity of k-Nearest Neighbor (KNN) model were 0.909, 82.95%, 83.33%, and 89.90%, respectively, on the test cohort. The comparative result showed statistically equivalent performance for thyroid nodule diagnosis based on image fusion and single image. In addition, the ML-Based ultrasound radiomics system showed a better AUC as compared with ACR TI-RADS model and the ultrasound features model. CONCLUSION: The novel ultrasonic-based ML model has an important clinical value for predicting malignancy in PCTNs. It can provide clinicians with a preoperative non-invasive primary screening method for PCTN diagnosis to avoid unnecessary medical investment and improve treatment outcomes.

Chromatin accessibility landscape of relapsed pediatric B-lineage acute lymphoblastic leukemia.

For around half of the pediatric B-lineage acute lymphoblastic leukemia (B-ALL) patients, the molecular mechanism of relapse remains unclear. To fill this gap in knowledge, here we characterize the chromatin accessibility landscape in pediatric relapsed B-ALL. We observe rewired accessible chromatin regions (ACRs) associated with transcription dysregulation in leukemia cells as compared with normal B-cell progenitors. We show that over a quarter of the ACRs in B-ALL are in quiescent regions with high heterogeneity among B-ALLs. We identify subtype-specific and allele-imbalanced chromatin accessibility by integrating multi-omics data. By characterizing the differential ACRs between diagnosis and relapse in B-ALL, we identify alterations in chromatin accessibility during drug treatment. Further analysis of ACRs associated with relapse free survival leads to the identification of a subgroup of B-ALL which show early relapse. These data provide an advanced and integrative portrait of the importance of chromatin accessibility alterations in tumorigenesis and drug responses.

Effect of Nanoscale in Situ Interface Welding on the Macroscale Thermal Conductivity of Insulating Epoxy Composites: A Multiscale Simulation Investigation.

Insulating thermally conductive polymer composites are in great demand in integrated-circuit packages, for efficient heat dissipation and to alleviative short-circuit risk. Herein, the continuous oriented hexagonal boron nitride (h-BN) frameworks (o-BN@SiC) were prepared via self-assembly and in situ chemical vapor infiltration (CVI) interface welding. The insulating o-BN@SiC/epoxy (o-BN@SiC/EP) composites exhibited enhanced thermal conductivity benefited from the CVI-SiC-welded BN-BN interface. Further, multiscale simulation, combining first-principles calculation, Monte Carlo simulation, and finite-element simulation, was performed to quantitatively reveal the effect of the welded BN-BN interface on the heat transfer of o-BN@SiC/EP composites. Phonon transmission in solders and phonon-phonon coupling of filler-solder interfaces enhanced the interfacial heat transfer between adjacent h-BN microplatelets, and the interfacial thermal resistance of the dominant BN-BN interface was decreased to only 3.83 nK·m2/W from 400 nK·m2/W, plunging by over 99%. This highly weakened interfacial thermal resistance greatly improved the heat transfer along thermal pathways and resulted in a 26% thermal conductivity enhancement of o-BN@SiC/EP composites, compared with physically contacted oriented h-BN/EP composites, at 15 vol % h-BN. This systematic multiscale simulation broke through the barrier of revealing the heat transfer mechanism of polymer composites from the nanoscale to the macroscale, which provided rational cognition about the effect of the interfacial thermal resistance between fillers on the thermal conductivity of polymer composites.

CXCL10 Recruitment of γδ T Cells into the Hypoxic Bone Marrow Environment Leads to IL17 Expression and Multiple Myeloma Progression.

In multiple myeloma (MM), bone marrow stromal cells (BMSC) shape a unique niche within the bone marrow, promoting T-cell dysfunction and driving MM progression; however, the precise underlying mechanisms remain elusive. Here, we show that BMSC-mediated reprogramming of MM cells led to heightened production of CXCL10. CXCL10 orchestrated the recruitment of γδ T cells into the bone marrow, and this was observed in both the Vk*MYC and 5TGM1 mouse models of MM, as well as in patients experiencing refractory or relapsed MM. Furthermore, the dysfunctional γδ T cells in the MM bone marrow niche exhibited increased PD-1 expression and IL17 production. In the Vk*MYC mouse model, MM-associated bone lesions and mortality were markedly alleviated in Tcrd-/- mice, and MM disease progression could be rescued in these mice upon transplantation of γδ T cells expanded from wild-type mice, but not from Il17-/- mice. Mechanistically, the hypoxic microenvironment prevailing in the MM bone marrow niche stimulated the expression of steroid receptor coactivator 3 (SRC-3) in γδ T cells, which in turn interacted with the transcriptional factor RORγt, promoting Il17 transcription. Pharmacologic inhibition of SRC-3 utilizing SI-2 effectively suppressed Il17A expression in γδ T cells, leading to alleviation of MM progression in the murine models and enhancing the anti-multiple myeloma efficacy of bortezomib. Our results illuminated the bone marrow microenvironment's involvement in provoking γδ T-cell dysfunction throughout MM progression and suggest SRC-3 inhibition as a promising strategy to enhance the effectiveness of immunotherapies targeting γδ T cells.

Direction-of-Arrival Estimation Method Based on Neural Network with Temporal Structure for Underwater Acoustic Vector Sensor Array.

Acoustic vector sensor (AVS) is a kind of sensor widely used in underwater detection. Traditional methods use the covariance matrix of the received signal to estimate the direction-of-arrival (DOA), which not only loses the timing structure of the signal but also has the problem of weak anti-noise ability. Therefore, this paper proposes two DOA estimation methods for underwater AVS arrays, one based on a long short-term memory network and attention mechanism (LSTM-ATT), and the other based on Transformer. These two methods can capture the contextual information of sequence signals and extract features with important semantic information. The simulation results show that the two proposed methods perform much better than the multiple signal classification (MUSIC) method, especially in the case of low signal-to-noise ratio (SNR), the DOA estimation accuracy has been greatly improved. The accuracy of the DOA estimation method based on Transformer is comparable to that of the DOA estimation method based on LSTM-ATT, but the computational efficiency is obviously better than that of the DOA estimation method based on LSTM-ATT. Therefore, the DOA estimation method based on Transformer proposed in this paper can provide a reference for fast and effective DOA estimation under low SNR.

A novel nomogram and risk stratification system predicting the cancer-specific survival of patients with gastric neuroendocrine carcinoma: a study based on SEER database and external validation.

BACKGROUND: Gastric neuroendocrine carcinoma (GNEC) is a rare histology of gastric cancer. The retrospective study was designed to construct and validate a nomogram for predicting the cancer-specific survival (CSS) of postoperative GNEC patients. METHODS: Data for 28 patients from the Hangzhou TCM Hospital were identified as the external validation cohort. A total of 1493 patients were included in the SEER database and randomly assigned to the training group (1045 patients) and internal validation group (448 patients). The nomogram was constructed using the findings of univariate and multivariate Cox regression studies. The model was evaluated by consistency index (C-index), calibration plots, and clinical net benefit. Finally, the effect between the nomogram and AJCC staging system was compared by net reclassification index (NRI) and integrated discrimination improvement (IDI). RESULTS: Age, gender, grade, T stage, N stage, metastasis, primary site, tumor size, RNE, and chemotherapy were incorporated in the nomogram. The C-indexes were 0.792 and 0.782 in the training and internal verification sets. The 1-, 3-, and 5-year CSS predicted by the nomogram and actual measurements had good agreement in calibration plots. The 1-, 3-, and 5-year NRI were 0.21, 0.29, and 0.37, respectively. The 1-, 3-, and 5-year IDI values were 0.10, 0.12, and 0.13 (P < 0.001), respectively. In 1-, 3-, and 5-year CSS prediction using DCA curves, the nomogram outperformed the AJCC staging system. The nomogram performed well in both the internal and external validation cohorts. CONCLUSION: We developed and validated a nomogram to predict 1-, 3-, and 5-year CSS for GNEC patients after surgical resection. This well-performing model could help doctors enhance the treatment plan.

Optimization of Subthreshold Swing and Hysteresis in Hf0.5Zr0.5O2-Based MoS2 Negative Capacitance Field-Effect Transistors by Modulating Capacitance Matching.

Negative capacitance field effect transistors made of Hf0.5Zr0.5O2 (HZO) are one of the most promising candidates for low-power-density devices because of the extremely steep subthreshold swing and high open-state currents resulting from the addition of ferroelectric materials in the gate dielectric layer. In this paper, HZO thin films were prepared by magnetron sputtering combined with rapid thermal annealing. Their ferroelectric properties were adjusted by changing the annealing temperature and the thickness of HZO. Two-dimensional MoS2 back-gate negative capacitance field-effect transistors (NCFETs) based on HZO were prepared as well. Different annealing temperatures, thicknesses of HZO thin films, and Al2O3 thicknesses were studied to achieve optimal capacitance matching, aiming to reduce both the subthreshold swing of the transistor and the hysteresis of the NCFET. The NCFET exhibits a minimum subthreshold swing as low as 27.9 mV/decade, negligible hysteresis (∼20 mV), and the ION/IOFF of up to 1.58 × 107. Moreover, a negative drain-induced barrier lowering effect and a negative differential resistance effect have been observed. This steep-slope transistor is compatible with standard CMOS manufacturing processes and attractive for 2D logic and sensor applications as well as future energy-efficient nanoelectronic devices with scaled power supplies.

De-escalating chemotherapy for stage I-II gastric neuroendocrine carcinoma? A real-world competing risk analysis.

BACKGROUND: The role of adjuvant chemotherapy in gastric neuroendocrine neoplasms (GNEC) has not been well clarified yet. The study was designed to investigate the potential effect of adjuvant chemotherapy in stage I-II GNEC patients and construct a predictive nomogram. METHOD: Stage I-II GNEC patients were included in the Surveillance, Epidemiology, and End Results (SEER) database and divided into chemotherapy and no-chemotherapy groups. We used Kaplan-Meier survival analyses, propensity score matching (PSM), and competing risk analyses. The predictive nomogram was then built and validated. RESULTS: Four hundred four patients with stage I-II GNEC were enrolled from the SEER database while 28 patients from Hangzhou TCM Hospital were identified as the external validation cohort. After PSM, similar 5-year cancer-specific survival was observed in two groups. The outcomes of competing risk analysis indicated a similar 5-year cumulative incidence of cancer-specific death (CSD) between the two cohorts (35.4% vs. 31.4%, p = 0.731). And there was no significant relation between chemotherapy and CSD in the multivariate competing risks regression analysis (HR, 0.79; 95% CI, 0.48-1.31; p = 0.36). Furthermore, based on the variables from the multivariate analysis, a competing event nomogram was created to assess the 1-, 3-, and 5-year risks of CSD. The 1-, 3-, and 5-year area under the receiver operating characteristic curve (AUC) values were 0.770, 0.759, and 0.671 in the training cohort, 0.809, 0.782, and 0.735 in the internal validation cohort, 0.786, 0.856, and 0.770 in the external validation cohort. Furthermore, calibration curves revealed that the expected and actual probabilities of CSD were relatively consistent. CONCLUSION: Stage I-II GNEC patients could not benefit from adjuvant chemotherapy after surgery. De-escalation of chemotherapy should be considered for stage I-II GNEC patients. The proposed nomogram exhibited excellent prediction ability.

A Novel Nomogram for Identifying Candidates for Adjuvant Chemotherapy in Patients With Stage IB Non-small Cell Lung Cancer.

BACKGROUND: This study aimed to develop a novel predictive nomogram to identify specific stage IB non-small cell lung cancer (NSCLC) populations who could benefit from adjuvant chemotherapy (ACT). METHOD: Stage IB NSCLC patients were included in the Surveillance, Epidemiology, and End Results (SEER) database and divided into the ACT and non-ACT groups. Then the methods of Kaplan-Meier analysis, propensity score matching (PSM), Least absolute shrink and selection operator (LASSO) regression, and multivariate logistic regression analyses were implemented. Finally, the predictive nomogram was constructed and validated. RESULTS: 9055 stage IB NSCLC patients were enrolled from the SEER database while 47 patients from Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University were identified as the external validation cohort. Of these patients, 1334 cases underwent ACT while the other 7721 patients didn't receive ACT. After PSM, the patients in the ACT group presented longer median overall survival (100 vs 82 months, P < .001). Among the ACT group, 482 (49.6%) patients achieving more prolonged overall survival than 82 months were regarded as the beneficiary population. Then the LASSO regression and multivariate logistic regression analyses were implemented. Finally, 8 predictors were selected for model construction, including age, gender, marital status, laterality, pathology, tumor size, regional nodes examined, and tumor size. The predictive nomogram demonstrated good discrimination in the training cohort (AUC = .781), internal validation cohort (AUC = .772), and external validation cohort (AUC = .851). And calibration curves indicated ideal consistency between the predicted and observed probabilities. Decision curve analysis presented a clinically useful model. CONCLUSION: The practical nomogram could guide treatment decision-making and select optimal ACT candidates among stage IB NSCLC patients.

Identification of cuproptosis-related lncRNAs to predict prognosis and immune infiltration characteristics in alimentary tract malignancies.

BACKGROUND: Alimentary tract malignancies (ATM) caused nearly one-third of all tumor-related death. Cuproptosis is a newly identified cell death pattern. The role of cuproptosis-associated lncRNAs in ATM is unknown. METHOD: Data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to identify prognostic lncRNAs by Cox regression and LASSO. Then a predictive nomogram was constructed based on seven prognostic lncRNAs. In addition, the prognostic potential of the seven-lncRNA signature was verified via survival analysis, the receiver operating characteristic (ROC) curve, calibration curve, and clinicopathologic characteristics correlation analysis. Furthermore, we explored the associations between the signature risk score and immune landscape, and somatic gene mutation. RESULTS: We identified 1211 cuproptosis-related lncRNAs and seven survival-related lncRNAs. Patients were categorized into high-risk and low-risk groups with significantly different prognoses. ROC and calibration curve confirmed the good prediction capability of the risk model and nomogram. Somatic mutations between the two groups were compared. We also found that patients in the two groups responded differently to immune checkpoint inhibitors and immunotherapy. CONCLUSION: The proposed novel seven lncRNAs nomogram could predict prognosis and guide treatment of ATM. Further research was required to validate the nomogram.