Signature Model Research Articles

Inflammation-related molecular signatures involved in the anticancer activities of brigatinib as well as the prognosis of EML4-ALK lung adenocarcinoma patient.

Although ALK tyrosine kinase inhibitors (ALK-TKIs) have shown remarkable benefits in EML4-ALK positive NSCLC patients compared to conventional chemotherapy, the optimal sequence of ALK-TKIs treatment remains unclear due to the emergence of primary and acquired resistance and the lack of potential prognostic biomarkers. In this study, we systematically explored the validity of sequential ALK inhibitors (alectinib, lorlatinib, crizotinib, ceritinib and brigatinib) for a heavy-treated patient with EML4-ALK fusion via developing an in vitro and in vivo drug testing system based on patient-derived models. Based on the patient-derived models and clinical responses of the patient, we found that crizotinib might inhibit proliferation of EML4-ALK positive tumors resistant to alectinib and lorlatinib. In addition, NSCLC patients harboring the G1269A mutation, which was identified in alectinib, lorlatinib and crizotinib-resistant NSCLC, showed responsiveness to brigatinib and ceritinib. Transcriptomic analysis revealed that brigatinib suppressed the activation of multiple inflammatory signaling pathways, potentially contributing to its anti-tumor activity. Moreover, we constructed a prognostic model based on the expression of IL6, CXCL1, and CXCL5, providing novel perspectives for predicting prognosis in EML4-ALK positive NSCLC patients. In summary, our results delineate clinical responses of sequential ALK-TKIs treatments and provide insights into the mechanisms underlying the superior effects of brigatinib in patients harboring ALKG1269A mutation and resistant towards alectinib, lorlatinib and crizotinib. The molecular signatures model based on the combination of IL6, CXCL1 and CXCL5 has the potential to predict prognosis of EML4-ALK positive NSCLC patients.

An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer.

Ovarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting the discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, are promising new markers. Previous studies only focused on exosome-related genes and employed the Lasso algorithm to construct prediction models, which are not robust. 420 OC patients from the TCGA datasets were divided into training and validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with exosome-related genes were selected using Pearson analysis. Univariate COX regression analysis was used to filter prognosis-related lncRNAs. The overlapping lncRNAs were identified as candidate lncRNAs for machine learning. Based on 10 machine learning algorithms and 117 algorithm combinations, the optimal predictor combinations were selected according to the C index. The exosome-related LncRNA Signature (ERLS) model was constructed using multivariate COX regression. Based on the median risk score of the training datasets, the patients were divided into high- and low-risk groups. Kaplan-Meier survival analysis, the time-dependent ROC, immune cell infiltration, immunotherapy response, and immune checkpoints were analyzed. 64 lncRNAs were subjected to a machine-learning process. Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model. Kaplan-Meier survival analysis showed that the high-risk group had a lower survival rate. The area under the curve (AUC) in predicting OS at 1, 3, and 5 years were 0.758, 0.816, and 0.827 in the entire TCGA cohort. xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration, which may contribute to the activation of cytolytic activity, inflammation promotion, and T-cell co-stimulation pathways. The low-risk group had higher expression levels of PDL1, CTLA4, and higher TMB. The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy. Patients with low expression of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly better survival prospects, whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes. Our study constructed an ERLS model that can predict prognostic risk and immunotherapy response, optimizing clinical management for OC patients.

A novel disulfidptosis-related gene signature predicts overall survival of glioblastoma patients.

Aim: The aim of this study was to investigate the prognostic relevance of disulfidptosis-related genes in glioblastoma using bioinformatic analysis in The Cancer Genome Atlas Program-Glioblastoma (TCGA-GBM) database and develop a gene signature model for predicting patient prognosis. Methods: We conducted a bioinformatic analysis using the TCGA-GBM database and employed weighted co-expression network analysis to identify disulfidptosis-related genes. Subsequently, we developed a predictive gene signature model based on these genes to stratify glioblastoma patients into high and low-risk groups. Results: Patients categorized into the high-risk group based on the disulfidptosis-related gene signature exhibited a significantly reduced survival rate in comparison to those in the low-risk group. Functional analysis also revealed notable differences in the immune status between the two risk groups. Conclusion: In conclusion, a new disulfidptosis-related gene signature can be utilised to predict prognosis in GBM.

Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer.

A combination of fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is the standard for adjuvant therapy of resected early-stage colon cancer (CC). Oxaliplatin leads to lasting and disabling neurotoxicity. Reserving the regimen for patients who benefit from oxaliplatin would maximize efficacy and minimize unnecessary adverse side effects. We trained a new machine learning model, referred to as the colon oxaliplatin signature (COLOXIS) model, for predicting response to oxaliplatin-containing regimens. We examined whether COLOXIS was predictive of oxaliplatin benefits in the CC adjuvant setting among 1,065 patients treated with 5-fluorouracil plus leucovorin (FULV; n = 421) or FULV + oxaliplatin (FOLFOX; n = 644) from NSABP C-07 and C-08 phase III trials. The COLOXIS model dichotomizes patients into COLOXIS+ (oxaliplatin responder) and COLOXIS- (nonresponder) groups. Eight-year recurrence-free survival was used to evaluate oxaliplatin benefits within each of the groups, and the predictive value of the COLOXIS model was assessed using the P value associated with the interaction term (int P) between the model prediction and the treatment effect. Among 1,065 patients, 526 were predicted as COLOXIS+ and 539 as COLOXIS-. The COLOXIS+ prediction was associated with prognosis for FULV-treated patients (hazard ratio [HR], 1.52 [95% CI, 1.07 to 2.15]; P = .017). The model was predictive of oxaliplatin benefits: COLOXIS+ patients benefited from oxaliplatin (HR, 0.65 [95% CI, 0.48 to 0.89]; P = .0065; int P = .03), but COLOXIS- patients did not (COLOXIS- HR, 1.08 [95% CI, 0.77 to 1.52]; P = .65). The COLOXIS model is predictive of oxaliplatin benefits in the CC adjuvant setting. The results provide evidence supporting a change in CC adjuvant therapy: reserve oxaliplatin only for COLOXIS+ patients, but further investigation is warranted.

Sphinx-in-the-Head: Group Signatures from Symmetric Primitives

Group signatures and their variants have been widely used in privacy-sensitive scenarios such as anonymous authentication and attestation. In this paper, we present a new post-quantum group signature scheme from symmetric primitives. Using only symmetric primitives makes the scheme less prone to unknown attacks than basing the design on newly proposed hard problems whose security is less well-understood. However, symmetric primitives do not have rich algebraic properties, and this makes it extremely challenging to design a group signature scheme on top of them. It is even more challenging if we want a group signature scheme suitable for real-world applications, one that can support large groups and require few trust assumptions. Our scheme is based on MPC-in-the-head non-interactive zero-knowledge proofs, and we specifically design a novel hash-based group credential scheme, which is rooted in the SPHINCS+ signature scheme but with various modifications to make it MPC (multi-party computation) friendly. The security of the scheme has been proved under the fully dynamic group signature model. We provide an implementation of the scheme and demonstrate the feasibility of handling a group size as large as 2 60 . This is the first group signature scheme from symmetric primitives that supports such a large group size and meets all the security requirements.

Disulfidptosis and ferroptosis related genes predict prognosis and personalize treatment for hepatocellular carcinoma.

Understanding the interplay between disulfidptosis, ferroptosis, and hepatocellular carcinoma (HCC) could provide valuable insights into the pathogenesis of HCC and potentially identify novel therapeutic targets for the treatment of this deadly disease. This study aimed to identify a prognostic signature for HCC by examining the differential expression of genes related to disulfidptosis and ferroptosis (DRG-FRG), and to assess its clinical applicability. By integrating 23 disulfidptosis and 259 ferroptosis related genes with HCC messenger RNA (mRNA) expression data from The Cancer Genome Atlas (TCGA), differentially expressed DRG-FRG genes were identified. From these, 11 DRG-FRG genes were selected to construct a risk signature model using least absolute shrinkage and selection operator regression analyses. The prognostic performance of this model was evaluated by Kaplan-Meier survival analysis and time-dependent receiver operating characteristic (ROC) analysis. Subsequently, a nomogram was built by combining the signature with clinical variables. To further delve into the underlying mechanisms, we performed bioinformatics analysis using a variety of databases. A prognostic signature based on 11 DRG-FRG genes effectively categorized HCC patients into high- and low-risk groups, showing a significant survival difference. Even after considering clinical variables, this signature remained an independent prognostic factor. Furthermore, the signature played a role in various critical biological processes and pathways that drive HCC progression. Potential therapeutic benefits could be derived from small molecule drugs targeting NQO1 and SLC7A11. Interestingly, the high-risk group exhibited resistance to several chemotherapeutic drugs, yet showed sensitivity to others when contrasted with the low-risk group. Lastly, the DRG-FRG genes signature had a strong correlation with the tumor immune microenvironment, marked by an elevated expression of immune checkpoint molecules in the high-risk group. The signature based on 11 DRG-FRG genes stands out as a promising prognostic biomarker for HCC. Beyond its predictive value, it sheds light on the intricate crosstalk between DRG-FRG genes and HCC. Importantly, these findings could pave the way for enhanced prognostic prediction, informed treatment decisions, and the advancement of immunotherapy for HCC patients.

Exploration of a hypoxia-immune-related microenvironment gene signature and prediction model for hepatitis C-induced early-stage fibrosis

BackgroundLiver fibrosis contributes to significant morbidity and mortality in Western nations, primarily attributed to chronic hepatitis C virus (HCV) infection. Hypoxia and immune status have been reported to be significantly correlated with the progression of liver fibrosis. The current research aimed to investigate the gene signature related to the hypoxia-immune-related microenvironment and identify potential targets for liver fibrosis.MethodSequencing data obtained from GEO were employed to assess the hypoxia and immune status of the discovery set utilizing UMAP and ESTIMATE methods. The prognostic genes were screened utilizing the LASSO model. The infiltration level of 22 types of immune cells was quantified utilizing CIBERSORT, and a prognosis-predictive model was established based on the selected genes. The model was also verified using qRT-PCR with surgical resection samples and liver failure samples RNA-sequencing data.ResultsElevated hypoxia and immune status were linked to an unfavorable prognosis in HCV-induced early-stage liver fibrosis. Increased plasma and resting NK cell infiltration were identified as a risk factor for liver fibrosis progression. Additionally, CYP1A2, CBS, GSTZ1, FOXA1, WDR72 and UHMK1 were determined as hypoxia-immune-related protective genes. The combined model effectively predicted patient prognosis. Furthermore, the preliminary validation of clinical samples supported most of the conclusions drawn from this study.ConclusionThe prognosis-predictive model developed using six hypoxia-immune-related genes effectively predicts the prognosis and progression of liver fibrosis. The current study opens new avenues for the future prediction and treatment of liver fibrosis.

Osteosarcoma neutrophil extracellular trap network-associated gene recurrence and metastasis model

Osteosarcoma (OS) is the most common malignancy in children and adolescents and has a high probability of recurrence and metastasis. A growing number of studies have shown that neutrophil extracellular traps (NETs) are strongly associated with cancer metastasis, but in osteosarcoma, genes associated with NETs that promote osteosarcoma recurrence and metastasis remain to be explored. We systematically investigated the gene expression patterns of NETs in OS samples from the GEO database. NETs molecular typing was evaluated based on NETs expression profiles, and the association between NETs molecular subtypes and immune microenvironment and metastatic features were explored. Ultimately, we constructed a signature model and column line graph associated with metastasis prediction and screened possible potential drugs for metastatic osteosarcoma. We established two different molecular subtypes of NETs, which showed significant differences in metastatic status, metastasis time, tumor immune microenvironment, and biological effects. We also constructed a NETs-related gene metastasis signature(NRGMS) to assess the expression pattern of NETs in patients to predict metastatic recurrence in osteosarcoma patients. We screened for TOMM40 and FH associated with metastatic recurrence in osteosarcoma patients. Overall, this study constructs a predictive model for osteosarcoma metastasis of NETs-related genes, which is expected to provide new insights into the metastasis of osteosarcoma.

Preoperative CT-based deep learning radiomics model to predict lymph node metastasis and patient prognosis in bladder cancer: a two-center study

ObjectiveTo establish a model for predicting lymph node metastasis in bladder cancer (BCa) patients.MethodsWe retroactively enrolled 239 patients who underwent three-phase CT and resection for BCa in two centers (training set, n = 185; external test set, n = 54). We reviewed the clinical characteristics and CT features to identify significant predictors to construct a clinical model. We extracted the hand-crafted radiomics features and deep learning features of the lesions. We used the Minimum Redundancy Maximum Relevance algorithm and the least absolute shrinkage and selection operator logistic regression algorithm to screen features. We used nine classifiers to establish the radiomics machine learning signatures. To compensate for the uneven distribution of the data, we used the synthetic minority over-sampling technique to retrain each machine-learning classifier. We constructed the combined model using the top-performing radiomics signature and clinical model, and finally presented as a nomogram. We evaluated the combined model’s performance using the area under the receiver operating characteristic, accuracy, calibration curves, and decision curve analysis. We used the Kaplan–Meier survival curve to analyze the prognosis of BCa patients.ResultsThe combined model incorporating radiomics signature and clinical model achieved an area under the receiver operating characteristic of 0.834 (95% CI: 0.659–1.000) for the external test set. The calibration curves and decision curve analysis demonstrated exceptional calibration and promising clinical use. The combined model showed good risk stratification performance for progression-free survival.ConclusionThe proposed CT-based combined model is effective and reliable for predicting lymph node status of BCa patients preoperatively.Critical relevance statementBladder cancer is a type of urogenital cancer that has a high morbidity and mortality rate. Lymph node metastasis is an independent risk factor for death in bladder cancer patients. This study aimed to investigate the performance of a deep learning radiomics model for preoperatively predicting lymph node metastasis in bladder cancer patients.Key points• Conventional imaging is not sufficiently accurate to determine lymph node status.• Deep learning radiomics model accurately predicted bladder cancer lymph node metastasis.• The proposed method showed satisfactory patient risk stratification for progression-free survival.Graphical

Targeting LTA4H facilitates the reshaping of the immune microenvironment mediated by CCL5 and sensitizes ovarian cancer to Cisplatin.

Ovarian cancer is the most lethal and aggressive gynecological cancer with a high recurrence rate and is often diagnosed late. In ovarian cancer, multiple metabolic enzymes of lipid metabolism are abnormally expressed, resulting in metabolism disorder. As a characteristic pathway in polyunsaturated fatty acid (PUFA) metabolism, arachidonic acid (AA) metabolism is disturbed in ovarian cancer. Therefore, we established a 10-gene signature model to evaluate the prognostic risk of PUFA-related genes. This 10-gene signature has strong robustness and can play a stable predictive role in datasets of various platforms (TCGA, ICGC, and GSE17260). The high association between the risk subgroups and clinical characteristics indicated a good performance of the model. Our data further indicated that the high expression of LTA4H was positively correlated with poor prognosis in ovarian cancer. Deficiency of LTA4H enhanced sensitivity to Cisplatin and modified the characteristics of immune cell infiltration in ovarian cancer. Additionally, our results indicate that CCL5 was involved in the aberrant metabolism of the AA/LTA4H axis, which contributes to the reduction of tumor-infiltrating CD8+ T cells and immune escape in ovarian cancer. These findings provide new insights into the prognosis and potential target of LTA4H/CCL5 in treating ovarian cancer.

Selective Organ-Targeting Hafnium Oxide Nanoparticles with Multienzyme-Mimetic Activities Attenuate Radiation-Induced Tissue Damage.

Radioprotective agents hold clinical promises to counteract off-target adverse effects of radiation and benefit radiotherapeutic outcomes, yet the inability to control drug transport in human organs poses a leading limitation. Based upon a validated rank-based multigene signature model, radiosensitivity indices are evaluated of diverse normal organs as a genomic predictor of radiation susceptibility. Selective ORgan-Targeting (SORT) hafnium oxide nanoparticles (HfO2 NPs) are rationally designed via modulated synthesis by α-lactalbumin, homing to top vulnerable organs. HfO2 NPs like Hensify are commonly radioenhancers, but SORT HfO2 NPs exhibit surprising radioprotective effects dictated by unfolded ligands and Hf(0)/Hf(IV) redox couples. Still, the X-ray attenuation patterns allow radiological confirmation in target organs by dual-beam spectral computed tomography. SORT HfO2 NPs present potent antioxidant activities, catalytically scavenge reactive oxygen species, and mimic multienzyme catalytic activities. Consequently, SORT NPs rescue radiation-induced DNA damage in mouse and rabbit models and provide survival benefits upon lethal exposures. In addition to inhibiting radiation-induced mitochondrial apoptosis, SORT NPs impede DNA damage and inflammation by attenuating activated FoxO, Hippo, TNF, and MAPK interactive cascades. A universal methodology is proposed to reverse radioenhancers into radioprotectors. SORT radioprotective agents with image guidance are envisioned as compelling in personalized shielding from radiation deposition.

Immunogenic cell death-related classification reveals prognosis and effectiveness of immunotherapy in breast cancer

Lack of specific biomarkers and effective drug targets constrains therapeutic research in breast cancer (BC). In this regard, therapeutic modulation of damage-associated molecular patterns (DAMPs)-induced immunogenic cell death (ICD) may help improve the effect of immunotherapy in individuals with BC. The aim of this investigation was to develop biomarkers for ICD and to construct ICD-related risk estimation models to predict prognosis and immunotherapy outcomes of BC. RNA-seq transcriptome information and medical data from individuals with BC (n = 943) were obtained from TCGA. Expression data from a separate BC cohort (GEO: GSE20685) were used for validation. We identified subtypes of high and low ICD gene expression by consensus clustering and assessed the connection between ICD subtypes and tumor microenvironment (TME). In addition, different algorithms were used to construct ICD-based prognostic models of BC. BC samples were categorized into subtypes of high and low ICD expression depending on the expression of genes correlated with ICD. The subtype of ICD high-expression subtypes are correlated with poor prognosis in breast cancer, while ICD low-expression subtypes may predict better clinical outcomes. We also created and verified a predictive signature model depending on four ICD-related genes (ATG5, CD8A, CD8B, and HSP90AA1), which correlates with TME status and predicts clinical outcomes of BC patients. We highlight the connection of ICD subtypes with the dynamic evolution of TME in BC and present a novel ICD-based prognostic model of BC. In clinical practice, distinction of ICD subtype and assessment of ICD-related biomarkers should help guide treatment planning and improve the effectiveness of tumor immunotherapy.

Prognostic Significance of Glycolysis-Related Genes in Lung Squamous Cell Carcinoma.

Lung squamous cell carcinoma (LUSC) is one of the most common malignancies. There is growing evidence that glycolysis-related genes play a critical role in tumor development, maintenance, and therapeutic response by altering tumor metabolism and thereby influencing the tumor immune microenvironment. However, the overall impact of glycolysis-related genes on the prognostic significance, tumor microenvironment characteristics, and treatment outcome of patients with LUSC has not been fully elucidated. We used The Cancer Genome Atlas (TCGA) dataset to screen glycolysis-related genes with prognostic effects in LUSC and constructed signature and nomogram models using Lasso and Cox regression, respectively. In addition, we analyzed the immune infiltration and tumor mutation load of the genes in the models. We finally obtained a total of glycolysis-associated DEGs. The signature model and nomogram model had good prognostic power for LUSC. Gene expression in the models was highly correlated with multiple immune cells in LUSC. Through this analysis, we have identified and validated for the first time that glycolysis-related genes are highly associated with the development of LUSC. In addition, we constructed the signature model and nomogram model for clinical decision-making.

Radiomics nomogram for preoperative differentiation of early-stage serous borderline ovarian tumors and serous malignant ovarian tumors.

This study aimed to construct a radiomics nomogram and validate its performance in the preoperative differentiation between early-stage (I and II) serous borderline ovarian tumors (SBOTs) and serous malignant ovarian tumors (SMOTs). Data were collected from 80 patients with early-stage SBOTs and 102 with early-stage SMOTs (training set: n = 127; validation set: n = 55). Univariate and multivariate analyses were performed to identify the independent clinicoradiological factors. A radiomics signature model was constructed using radiomics features extracted from multidetector computed tomography images of the venous phase, in which the least absolute shrinkage and selection operator regression was employed to lessen the dimensionality of the data and choose the radiomics features. A nomogram model was established by combining independent clinicoradiological factors with the radiomics signature. The performance of nomogram calibration, discrimination, and clinical usefulness was evaluated using training and validation sets. In terms of clinicoradiological characteristics, age (p = 0.001), the diameter of the solid component (p = 0.009), and human epididymis protein 4 level (p < 0.001) were identified as the independent risk factors of SMOT, for which the area under the curves (AUCs) were calculated to be 0.850 and 0.836 in the training and validation sets, respectively. Nine features were finally selected to construct the radiomics signature model, which exhibited AUCs of 0.879 and 0.826 for the training and validation sets, respectively. The nomogram model demonstrated considerable calibration and discrimination with AUCs of 0.940 and 0.909 for the training and validation sets, respectively. The nomogram model displayed more prominent clinical usefulness than the clinicoradiological and radiomics signature models according to the decision curve analysis. The nomogram model can be employed as an individualized preoperative non-invasive tool for differentiating early-stage SBOTs from SMOTs.

Identification of the Mechanism of Action of the Index Components of Banxia Xiexin Decoction for Gastric Cancer through Network Pharmacology, Bioinformatics, and Molecular Docking Analysis

Banxia Xiexin decoction (BXD) is a traditional prescription widely used to treat gastrointestinal conditions, including gastric cancer. Through network pharmacology, bioinformatics, and molecular docking analysis, this study aimed to investigate the potential mechanism of the antigastric cancer effect of BXD and pave the way for future research. The network pharmacology analysis used BXD index components to improve reliability and validity. Prognosis-related genes identified through Lasso and Cox regression analysis were considered potential BXD core targets for gastric cancer. Functional enrichment analysis was conducted to uncover the potential mechanism of action of BXD in gastric cancer. In addition, molecular docking of the index components of BXD and the core targets was used to validate the results. The present study obtained six index components of BXD and 155 corresponding antigastric cancer targets. ANXA5, CYP19A1, FGF1, and F2 in the prognostic signature model were identified as core targets of the index components of BXD. Protein-protein interaction networks and functional enrichment analysis indicated that proteoglycans in cancer, PI3K-Akt, and other pathways were involved. According to molecular docking results, six index components showed good-to-strong binding affinities to the core targets. The results indicated that the index components of BXD act on multiple pathways and targets of gastric cancer. Our study paved the way for further investigation of the antigastric cancer activity and mechanisms of BXD.

Multiparametric MRI-based radiomics combined with pathomics features for prediction of the efficacy of neoadjuvant chemotherapy in breast cancer

PurposeThe aim of this study is to investigate a new method that combines radiological and pathological breast cancer information to predict discrepancies in pathological responses for individualized treatment planning. We used baseline multiparametric magnetic resonance imaging and hematoxylin and eosin-stained biopsy slides to extract quantitative feature information and predict the pathological response to neoadjuvant chemotherapy in breast cancer patients. MethodsWe retrospectively collected data from breast cancer patients who received neoadjuvant chemotherapy in our hospital from August 2016 to January 2018; multiparametric magnetic resonance imaging (contrast-enhanced T1-weighted imaging and diffusion-weighted imaging) and whole slide image of hematoxylin and eosin-stained biopsy sections were collected. Quantitative imaging features were extracted from the multiparametric magnetic resonance imaging and the whole slide image were used to construct a radiopathomics signature model powered by machine learning methods. Models based on multiparametric magnetic resonance imaging or whole slide image alone were also constructed for comparison and referred to as the radiomics signature and pathomics signature models, respectively. Four modeling methods were used to establish prediction models. Model performances were evaluated using receiver operating characteristic curve analysis and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. ResultsThe radiopathomics signature model had favourable performance for the prediction of pathological complete response in the training set (the best value: area under the curve 0.83, accuracy 0.84, and sensitivity 0.87), and in the test set (the best value: area under the curve 0.91, accuracy 0.90, and sensitivity 0.88). In the test set, the radiopathomics signature model also significantly outperformed the radiomics signature (the best value: area under the curve 0.83, accuracy 0.64, and sensitivity 0.62), pathomics signature (the best value: area under the curve 0.60, accuracy 0.74, and sensitivity 0.62) (p > 0.05). Decision curve analysis and calibration curves confirmed the excellent performance of these prediction models in discrimination, calibration, and clinical usefulness. ConclusionsThe results of this study suggest that radiopathomics, the combination of both radiological information regarding the whole tumor and pathological information at the cellular level, could potentially predict discrepancies in pathological response and provide evidence for rational treatment plans.

Leveraging diverse cell-death patterns to predict the clinical outcome of immune checkpoint therapy in lung adenocarcinoma: Based on muti-omics analysis and vitro assay.

Advanced LUAD shows limited response to treatment including immune therapy. With the development of sequencing omics, it is urgent to combine high-throughput multi-omics data to identify new immune checkpoint therapeutic response markers. Using GSE72094 (n = 386) and GSE31210 (n = 226) gene expression profile data in the GEO database, we identified genes associated with lung adenocarcinoma (LUAD) death using tools such as "edgeR" and "maftools" and visualized the characteristics of these genes using the "circlize" R package. We constructed a prognostic model based on death-related genes and optimized the model using LASSO-Cox regression methods. By calculating the cell death index (CDI) of each individual, we divided LUAD patients into high and low CDI groups and examined the relationship between CDI and overall survival time by principal component analysis (PCA) and Kaplan-Meier analysis. We also used the "ConsensusClusterPlus" tool for unsupervised clustering of LUAD subtypes based on model genes. In addition, we collected data on the expression of immunomodulatory genes and model genes for each cohort and performed tumor microenvironment analyses. We also used the TIDE algorithm to predict immunotherapy responses in the CDI cohort. Finally, we studied the effect of PRKCD on the proliferation and migration of LUAD cells through cell culture experiments. The study utilized the TCGA-LUAD cohort (n = 493) and identified 2,901 genes that are differentially expressed in patients with LUAD. Through KEGG and GO enrichment analysis, these genes were found to be involved in a wide range of biological pathways. The study also used univariate Cox regression models and LASSO regression analyses to identify 17 candidate genes that were best associated with mortality prognostic risk scores. By comparing the overall survival (OS) outcomes of patients with different CDI values, it was found that increased CDI levels were significantly associated with lower OS rates. In addition, the study used unsupervised cluster analysis to divide 115 LUAD patients into two distinct clusters with significant differences in OS timing. Finally, a prognostic indicator called CDI was established and its feasibility as an independent prognostic indicator was evaluated by Cox proportional risk regression analysis. The immunotherapy efficacy was more sensitive in the group with high expression of programmed cell death models. Relationship between programmed cell death (PCD) signature models and drug reactivity. After evaluating the median inhibitory concentration (IC50) of various drugs in LUAD samples, statistically significant differences in IC50 values were found in cohorts with high and low CDI status. Specifically, Gefitinib and Lapatinib had higher IC50 values in the high-CDI cohort, while Olaparib, Oxaliplatin, SB216763, and Axitinib had lower values. These results suggest that individuals with high CDI levels are sensitive to tyrosine kinase inhibitors and may be resistant to conventional chemotherapy. Therefore, this study constructed a gene model that can evaluate patient immunotherapy by using programmed cell death-related genes based on muti-omics. The CDI index composed of these programmed cell death-related genes reveals the heterogeneity of lung adenocarcinoma tumors and serves as a prognostic indicator for patients.

Forward Modeling of Gamma Reaction History Signatures From Anticipated Deuterium-Tritium Filled MagLIF Implosions on Sandia’s Z-Machine

Forward Modeling of Gamma Reaction History Signatures From Anticipated Deuterium-Tritium Filled MagLIF Implosions on Sandia’s Z-Machine

Algorithm of the electronic sign-code signature on the basis of the composition of existing complexities

The article developed a new algorithm for electronic digital signature in the composition of existing difficulties: discrete logarithm in a finite field, addition of points with rational coordinates of the elliptic curve. Based on a combination of the complexities of a discrete logarithm on a finite field with a large number characteristic, decomposition of a sufficiently large odd number into prime factors, and addition of points of an elliptic curve, an electronic digital signature algorithm is developed for generating. The generally recognized model of electronic sign-code signature covers three processes: generation of digital signature keys; ESS formation; confirmation of authenticity of electronic sign-code signature. Firmness offered AESS is based on several complexities: calculations of the discrete logarithming in a finite field, decisions of the task of expansion of enough big odd number on simple factors, executions of operation of addition of points of the elliptic curve set in a finite field. It considerably raises crypto algorithm strength.

Immunogenic profiling of metastatic uveal melanoma discerns a potential signature related to prognosis

BackgroundUveal melanoma (UM) is an aggressive intraocular malignant tumor. The present study aimed to identify the key genes associated with UM metastasis and established a gene signature to analyze the relationship between the signature and prognosis and immune cell infiltration. Later, a predictive model combined with clinical variables was developed and validated.MethodsTwo UM gene expression profile chip datasets were downloaded from TCGA and GEO databases. Immune-related genes (IRGs) were obtained from IMPORT database. First, these mRNAs were intersected with IRGs, and weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression of genes primarily associated with metastasis of UM. Univariate Cox regression analysis screened the genes related to prognosis. LASSO-Cox established a risk score to distinguish high-risk group and low-risk group. Then the GSEA enrichment pathway and immune cell infiltration of the two groups were compared. And combined with clinical variables, a predictive model was constructed. The time-dependent receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve were used to verify the stability and accuracy of the final predictive model, and a nomogram was then drawn.ResultsThe MEblack, MEpurple, and MEblue modules were significantly associated with the metastasis of UM patients (P value < 0.001, = 0.001, = 0.022, respectively). Four genes (UBXN2B, OTUD3, KAT8, LAMTOR2) were obtained by Pearson correlation analysis, weighted gene correlation network analysis (WGCNA), univariate Cox, and LASSO-Cox. And a novel prognostic risk score was established. Immune-related prognostic signature can well classify UM patients into high-risk and low-risk groups. Kaplan–Meier curve showed that the OS of high-risk patients was worse than that of low-risk patients. In addition, the risk score played an important role in evaluating the signaling pathway and immune cell infiltration of UM patients in high-risk and low-risk groups. Both the training set and validation set of the model showed good predictive accuracy in the degree of differentiation and calibration (e.g., 1-year overall survival: AUC = 0.930 (0.857–1.003)). Finally, a nomogram was established to serve in clinical practice.SignificanceUM key gene signature and prognosis predictive model might provide insights for further investigation of the pathogenesis and development of UM at the molecular level, and provide theoretical basis for determining new prognostic markers of UM and immunotherapy.