Deciphering tertiary lymphoid structure heterogeneity reveals prognostic signature and therapeutic potentials for colorectal cancer: a multicenter retrospective cohort study.

BACKGROUND: Tertiary lymphoid structures (TLSs) exert a crucial role in the tumor microenvironment (TME), impacting tumor development, immune escape, and drug resistance. Nonetheless, the heterogeneity of TLSs in colorectal cancer (CRC) and their impact on prognosis and treatment response remain unclear. METHODS: We collected genome, transcriptome, clinicopathological information, and digital pathology images from multiple sources. An unsupervised clustering algorithm was implemented to determine diverse TLS patterns in CRC based on the expression levels of 39 TLS signature genes (TSGs). Comprehensive explorations of heterogeneity encompassing mutation landscape, TME, biological characteristics, response to immunotherapy, and drug resistance were conducted using multi-omics data. TLSscore was then developed to quantitatively assess TLS patterns of individuals for further clinical applicability. RESULTS: Three distinct TLS patterns were identified in CRC. Cluster 1 exhibited upregulation of proliferation-related pathways, high metabolic activity, and intermediate prognosis, while Cluster 2 displayed activation of stromal and carcinogenic pathways and a worse prognosis. Both Cluster 1 and Cluster 2 may potentially benefit from adjuvant chemotherapy. Cluster 3, characterized by the activation of immune regulation and activation pathways, demonstrated a favorable prognosis and enhanced responsiveness to immunotherapy. We subsequently employed a regularization algorithm to construct the TLSscore based on 9 core genes. Patients with lower TLSscore trended to prolonged prognosis and a more prominent presence of TLSs, which may benefit from immunotherapy. Conversely, those with higher TLSscore exhibited increased benefits from adjuvant chemotherapy. CONCLUSIONS: We identified distinct TLS patterns in CRC and characterized their heterogeneity through multi-omics analyses. The TLSscore held promise for guiding clinical decision-making and further advancing the field of personalized medicine in CRC.

Seeding Atomic Silver into Internal Lattice Sites of Transition Metal Oxide for Advanced Electrocatalysis.

Transition metal oxides (TMOs) are widely studied for loading of various catalysts due to their low cost and high structure flexibility. However, the prevailing close-packed nature of most TMOs crystals has restricted the available loading sites to surface only, while their internal bulk lattice remains unactuated due to the inaccessible narrow space that blocks out most key reactants and/or particulate catalysts. Herein, using tunnel-structured MnO2, this study demonstrates how TMO's internal lattice space can be activated as extra loading sites for atomic Ag in addition to the conventional surface-only loading, via which a dual-form Ag catalyst within MnO2 skeleton is established. In this design, not only faceted Ag nanoparticles are confined onto MnO2 surface by coherent lattice-sharing, Ag atomic strings are also seeded deep into the sub-nanoscale MnO2 tunnel lattice, enriching the catalytically active sites. Tested for electrochemical CO2 reduction reaction (eCO2RR), such dual-form catalyst exhibits a high Faradaic efficiency (94%), yield (67.3 mol g-1 h-1) and durability (≈48 h) for CO production, exceeding commercial Ag nanoparticles and most Ag-based electrocatalysts. Theoretical calculations further reveal the concurrent effect of such dual-form catalyst featuring facet-dependent eCO2RR for Ag nanoparticles and lattice-confined eCO2RR for Ag atomic strings, inspiring the future design of catalyst-substrate configuration.

Structure Embedded Nucleus Classification for Histopathology Images.

Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected by the local receptive field of convolutions, and pay less attention to the spatial distribution of nuclei or the irregular contour shape of a nucleus. In this paper, we first propose a novel polygon-structure feature learning mechanism that transforms a nucleus contour into a sequence of points sampled in order, and employ a recurrent neural network that aggregates the sequential change in distance between key points to obtain learnable shape features. Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations. To capture the correlations between the categories of nuclei and their surrounding tissue patterns, we further introduce edge features that are defined as the background textures between adjacent nuclei. Lastly, we integrate both polygon and graph structure learning mechanisms into a whole framework that can extract intra and inter-nucleus structural characteristics for nuclei classification. Experimental results show that the proposed framework achieves significant improvements compared to the previous methods. Code and data are made available via https://github.com/lhaof/SENC.

Bubble Defect Generation Mechanism in Slot Die Coating of High-Viscosity Fluids.

Slot die coating is a widely used technique for the production of high accuracy films. One of the main challenges in slot die coating processes is determining the optimal range of operating parameters to prevent defects, such as bubbles, in high-viscosity films. In this study, both simulation and experimental methods were used to investigate the changes in the upstream meniscus under various coating parameters. Two types of air entrainment processes were considered, dominated by fluid inertia and viscous force. The formation of anticlockwise and clockwise vortex flows near the bottom of the upstream meniscus was found to alter the apparent dynamic contact angle of the upstream meniscus, leading to fluctuations in the apparent dynamic contact angle within a range of 70°-75° to 135°-140°. These fluctuations eventually resulted in air entrainment. This research is important for improving the quality and efficiency of slot die coating processes.

Surface Adsorption and Proton Chemistry of Ultra-Stabilized Aqueous Zinc-Manganese Dioxide Batteries.

Aqueous rechargeable Zinc (Zn) batteries incorporating MnO2 cathodes possess favorable sustainability properties and are being considered for low-cost, high-safety energy storage. However, unstable electrode structures and unclear charge storage mechanisms limit their development. Here, advanced transmission electron microscopy, electrochemical analysis, and theoretical calculations are utilized to study the working mechanisms of a Zn/MnO2 battery with a Co2+ -stabilized, tunnel-structured α-MnO2 cathode (Cox MnO2 ). It is shown that Co2+ can be pre-intercalated into α-MnO2 and occupy the (2 × 2) tunnel structure, which improves the structural stability of MnO2 , facilitates the proton diffusion and Zn2+ adsorption on the MnO2 surface upon battery cycling. It is further revealed that for the MnO2 cathode, the charge storage reaction proceeds mainly by proton intercalation with the formation of α-Hy Cox MnO2 , and that the anode design (with or without Zn metal) affects the surface adsorption of by-product Zn4 SO4 (OH)6 ·nH2 O on MnO2 surface. This work advances the fundamental understanding of rechargeable Zn batteries and also sheds light on efficient electrode modifications toward performance enhancement.

Senescence-based colorectal cancer subtyping reveals distinct molecular characteristics and therapeutic strategies.

Cellular senescence has been listed as a hallmark of cancer, but its role in colorectal cancer (CRC) remains unclear. We comprehensively evaluated the transcriptome, genome, digital pathology, and clinical data from multiple datasets of CRC patients and proposed a novel senescence subtype for CRC. Multi-omics data was used to analyze the biological features, tumor microenvironment, and mutation landscape of senescence subtypes, as well as drug sensitivity and immunotherapy response. The senescence score was constructed to better quantify senescence in each patient for clinical use. Unsupervised learning revealed three transcriptome-based senescence subtypes. Cluster 1, characterized by low senescence and activated proliferative pathways, was sensitive to chemotherapeutic drugs. Cluster 2, characterized by intermediate senescence and high immune infiltration, exhibited significant immunotherapeutic advantages. Cluster 3, characterized by high senescence, high immune, and stroma infiltration, had a worse prognosis and maybe benefit from targeted therapy. We further constructed a senescence scoring system based on seven senescent genes through machine learning. Lower senescence scores were highly predictive of longer disease-free survival, and patients with low senescence scores may benefit from immunotherapy. We proposed the senescence subtypes of CRC and our findings provide potential treatment interventions for each CRC senescence subtype to promote precision treatment.

Differential expression and significance of 5-hydroxymethylcytosine modification in hepatitis B virus carriers and patients with liver cirrhosis and liver cancer.

BACKGROUND: The relationship between hepatitis B surface antigen (HBsAg)-positive carrier status and liver cancer has been extensively studied. However, the epigenetic changes that occur during progression from HBsAg-positive carrier status or cirrhosis to liver cancer are unknown. The epigenetic modification of DNA hydroxymethylation is critical in tumor development. Further, 5-hydroxymethylcytosine (5hmC) is an important base for DNA demethylation and epigenetic regulation. It is also involved in the assembly of chromosomes and the regulation of gene expression. However, the mechanism of action of 5hmC in HBsAg-positive carriers or patients with cirrhosis who develop liver cancer has not been fully elucidated. AIM: To investigate the possible epigenetic mechanism of HBsAg-positive carriers and hepatocellular carcinoma (HCC) progression from cirrhosis. METHODS: Forty HBsAg-positive carriers, forty patients with liver cirrhosis, and forty patients with liver cancer admitted to the First People's Hospital of Yongkang between March 2020 and November 2021 were selected as participants. Free DNA was extracted using a cf-DNA kit. cfDNA was extracted by 5hmC DNA sequencing for principal component analysis, the expression profiles of the three groups of samples were detected, and the differentially expressed genes (DEGs) modified by hydroxymethylation were screened. Bioinformatic analysis was used to enrich DEGs, such as in biological pathways. RESULTS: A total of 16455 hydroxymethylated genes were identified. Sequencing results showed that 32 genes had significant 5hmC modification differences between HBsAg carriers and liver cancer patients, of which 30 were upregulated and 2 downregulated in patients with HCC compared with HBsAg-positive carriers. Significant 5hmC modification differences between liver cirrhosis and liver cancer patients were identified in 20 genes, of which 17 were upregulated and 3 were downregulated in patients with HCC compared with those with cirrhosis. These genes may have potential loci that are undiscovered or unelucidated, which contribute to the development and progression of liver cancer. Analysis of gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes showed that the major signaling pathways involved in the differential genes were biliary secretion and insulin secretion. The analysis of protein interactions showed that the important genes in the protein-protein interaction network were phosphoenolpyruvate carboxykinase and solute carrier family 2. CONCLUSION: The occurrence and development of liver cancer involves multiple genes and pathways, which may be potential targets for preventing hepatitis B carriers from developing liver cancer.

Effects of bubbles on particle dynamic behavior and concentration distribution in High-viscosity liquid under negative pressure.

The vacuum method is a widely adopted technique for eliminating bubbles from polymers containing particles. To investigate the influence of bubbles on the behavior of particles and the concentration distribution in high-viscosity liquids under negative pressure, experimental and numerical methods have been employed. The experimental findings demonstrated a positive correlation between the diameter and rising velocity of bubbles and the negative pressure. As the negative pressure increased from - 10 kPa to - 50 kPa, the position of the region where the particles were concentrated in the vertical direction was elevated. Furthermore, when the negative pressure exceeded - 50 kPa, the particle distribution became sparse and layered locally. The Lattice Boltzmann method (LBM) integrated with the discrete phase model (DPM) was utilized to investigate the phenomenon, and the outcomes revealed that rising bubbles have an inhibitory effect on particle sedimentation, and the extent of inhibition was determined by the negative pressure. In addition, vortexes generated by differences in the rising velocity between bubbles resulted in a particle distribution that was sparse and layered locally. This research provides a reference for achieving desired particle distributions using a vacuum defoaming approach and should be further studied to extend its applicability to suspensions containing particles with different viscosities.

Dihydrotanshinone I inhibits hepatocellular carcinoma cells proliferation through DNA damage and EGFR pathway.

Background: The incidence and mortality of hepatocellular carcinoma (HCC) are globally on the rise. Dihydrotanshinone I, a natural product isolated from Salvia miltiorrhiza Bunge, has attracted extensive attention in recent years for its anti-tumour proliferation efficiency. Methods: Cell proliferations in hepatoma cells (Huh-7 and HepG2) were evaluated by MTT and colony formation assays. Immunofluorescence (IF) of 53BP1 and flow cytometry analysis were performed to detect DNA damage and cell apoptosis. Furthermore, network pharmacological analysis was applied to explore the potential therapeutic targets and pathway of dihydrotanshinone I. Results: The results showed that dihydrotanshinone I effectively inhibited the proliferation of Huh-7 and HepG2 cells. Moreover, dihydrotanshinone I dose-dependently induced DNA-damage and apoptosis in vitro. Network pharmacological analysis and molecular simulation results indicated that EGFR might be a potential therapeutic target of dihydrotanshinone I in HCC. Collectively, our findings suggested that dihydrotanshinone I is a novel candidate therapeutic agent for HCC treatment.

An immune, stroma, and epithelial-mesenchymal transition-related signature for predicting recurrence and chemotherapy benefit in stage II-III colorectal cancer.

BACKGROUND: Debates exist on the treatment decision of the stage II/III colorectal cancer (CRC) due to the insufficiency of the current TNM stage-based risk stratification system. Epithelial-mesenchymal transition (EMT) and tumor microenvironment (TME) have both been linked to CRC progression in recent studies. We propose to improve the prognosis prediction of CRC by integrating TME and EMT. METHODS: In total, 2382 CRC patients from seven datasets and one in-house cohort were collected, and 1640 stage II/III CRC patients with complete survival information and gene expression profiles were retained and divided into a training cohort and three independent validation cohorts. Integrated analysis of 398 immune, stroma, and epithelial-mesenchymal transition (ISE)-related genes identified an ISE signature independently associated with the recurrence of CRC. The underlying biological mechanism of the ISE signature and its influence on adjuvant chemotherapy was further explored. RESULTS: We constructed a 26-gene signature which was significantly associated with poor outcome in Training cohort (p < 0.001, HR [95%CI] = 4.42 [3.25-6.01]) and three independent validation cohorts (Validation cohort-1: p < 0.01, HR [95%CI] = 1.70 [1.15-2.51]; Validation cohort-2: p < 0.001, HR [95% CI] = 2.30 [1.67-3.16]; Validation cohort-3: p < 0.01, HR [95% CI] = 2.42 [1.25-4.70]). After adjusting for known clinicopathological factors, multivariate cox analysis confirmed the ISE signature's independent prognostic value. Subgroup analysis found that stage III patients with low ISE score might benefit from adjuvant chemotherapy (p < 0.001, HR [95%CI] = 0.15 [0.04-0.55]). Hypergeometric test and enrichment analysis revealed that low-risk group was enriched in thr immune pathway while high-risk group was associated with the EMT pathway and CMS4 subtype. CONCLUSION: We proposed an ISE signature for robustly predicting the recurrence of stage II/III CRC and help treatment decision by identifying patients who will not benefit from current standard treatment.

CT-based radiogenomic analysis dissects intratumor heterogeneity and predicts prognosis of colorectal cancer: a multi-institutional retrospective study.

BACKGROUND: This study aimed to develop a radiogenomic prognostic prediction model for colorectal cancer (CRC) by investigating the biological and clinical relevance of intratumoural heterogeneity. METHODS: This retrospective multi-cohort study was conducted in three steps. First, we identified genomic subclones using unsupervised deconvolution analysis. Second, we established radiogenomic signatures to link radiomic features with prognostic subclone compositions in an independent radiogenomic dataset containing matched imaging and gene expression data. Finally, the prognostic value of the identified radiogenomic signatures was validated using two testing datasets containing imaging and survival information collected from separate medical centres. RESULTS: This multi-institutional retrospective study included 1601 patients (714 females and 887 males; mean age, 65 years ± 14 [standard deviation]) with CRC from 5 datasets. Molecular heterogeneity was identified using unsupervised deconvolution analysis of gene expression data. The relative prevalence of the two subclones associated with cell cycle and extracellular matrix pathways identified patients with significantly different survival outcomes. A radiogenomic signature-based predictive model significantly stratified patients into high- and low-risk groups with disparate disease-free survival (HR = 1.74, P = 0.003). Radiogenomic signatures were revealed as an independent predictive factor for CRC by multivariable analysis (HR = 1.59, 95% CI:1.03-2.45, P = 0.034). Functional analysis demonstrated that the 11 radiogenomic signatures were predominantly associated with extracellular matrix and immune-related pathways. CONCLUSIONS: The identified radiogenomic signatures might be a surrogate for genomic signatures and could complement the current prognostic strategies.

Atomic Mechanisms of Cation Adsorption/Exchange in Octahedral Molecular Sieves.

Octahedral molecular sieves (OMSs) based on MnO2 have been widely studied in the fields of deionization, geochemistry, and energy storage due to their microporous tunnel framework capable of adsorbing and exchanging various ions, particularly cations. The understanding of cation adsorption/exchange within OMS tunnels demands atomic-scale exploration, which has been scarcely reported. Here, we disclose how various cations (K+/Ag+/Na+) interplay within the OMS tunnel space on an atomic scale. Not only are the lattice sites for each adsorbed cation species pinpointed but the scenario of dual-cation adsorption within single tunnels is also demonstrated, together with the discovery of characteristic concentration-dependent cation ordering. Moreover, compared with the theoretical parent tunnel phase, the heterogeneous tunnels, though sparsely distributed, exhibit a distinct yet orderly cationic accommodation, highlighting the non-negligible role of tunnel heterogeneity in regulating OMS physiochemistry. Our findings clarify the long-existing ambiguities in nano- and atomic-scale science of the ion adsorption process in OMS materials and are expected to inspire their structural/compositional engineering toward functionality enhancement in various fields.

Predicting prognosis and immunotherapy response among colorectal cancer patients based on a tumor immune microenvironment-related lncRNA signature.

Long non-coding RNAs (lncRNAs) remodel the tumor immune microenvironment (TIME) by regulating the functions of tumor-infiltrating immune cells. It remains uncertain the way that TIME-related lncRNAs (TRLs) influence the prognosis and immunotherapy response of colorectal cancer (CRC). Aiming at providing survival and immunotherapy response predictions, a CRC TIME-related lncRNA signature (TRLs signature) was developed and the related potential regulatory mechanisms were explored with a comprehensive analysis on gene expression profiles from 97 immune cell lines, 61 CRC cell lines and 1807 CRC patients. Stratifying CRC patients with the TRLs signature, prolonged survival was observed in the low-risk group, while the patients in the high-risk group had significantly higher pro-tumor immune cells infiltration and higher immunotherapy response rate. Through the complex TRLs-mRNA regulation network, immunoregulation pathways and immunotherapy response pathways were found to be differently activated between the groups. In conclusion, the CRC TRLs signature is capable of making prognosis and immunotherapy response predictions, which may find application in stratifying patients for immunotherapy in the bedside.

A Transcription Factor Signature Can Identify the CMS4 Subtype and Stratify the Prognostic Risk of Colorectal Cancer.

Background: Colorectal cancer (CRC) is a heterogeneous disease, and current classification systems are insufficient for stratifying patients with different risks. This study aims to develop a generalized, individualized prognostic consensus molecular subtype (CMS)-transcription factors (TFs)-based signature that can predict the prognosis of CRC. Methods: We obtained differentially expressed TF signature and target genes between the CMS4 and other CMS subtypes of CRC from The Cancer Genome Atlas (TCGA) database. A multi-dimensional network inference integrative analysis was conducted to identify the master genes and establish a CMS4-TFs-based signature. For validation, an in-house clinical cohort (n = 351) and another independent public CRC cohort (n = 565) were applied. Gene set enrichment analysis (GSEA) and prediction of immune cell infiltration were performed to interpret the biological significance of the model. Results: A CMS4-TFs-based signature termed TF-9 that includes nine TF master genes was developed. Patients in the TF-9 high-risk group have significantly worse survival, regardless of clinical characteristics. The TF-9 achieved the highest mean C-index (0.65) compared to all other signatures reported (0.51 to 0.57). Immune infiltration revealed that the microenvironment in the high-risk group was highly immune suppressed, as evidenced by the overexpression of TIM3, CD39, and CD40, suggesting that high-risk patients may not directly benefit from the immune checkpoint inhibitors. Conclusions: The TF-9 signature allows a more precise categorization of patients with relevant clinical and biological implications, which may be a valuable tool for improving the tailoring of therapeutic interventions in CRC patients.

Serum CHI3L1 as a diagnostic marker and risk factor for liver fibrosis in HBeAg-negative chronic hepatitis B.

Chronic hepatitis B (CHB) as the major inducement of hepatocellular carcinoma and cirrhosis, imposes a heavy health burden upon patients. This research aims to investigate the diagnostic value of serum chitinase 3-like 1 (CHI3L1) in hepatitis B e antigen (HBeAg)-negative CHB liver fibrosis (LF) and to analyze the risk factors. We selected 78 patients with HBeAg-negative CHB admitted to our hospital between October 2018 and October 2019, and grouped them (F0,1 group, n=38; F2-4 group, n=40) based on their stages evaluated by the METAVIR scoring system. Cubital venous blood was collected from patients in both groups to quantify the content of CHI3L1 after serum extraction. The correlation of CHI3L1 in CHB with LF diagnostic markers fibrosis 4 (FIB-4) and γ-glutamyltranspeptidase (GGT) to platelet (PLT) ratio (GPR) as well as LF staging was analyzed. The diagnostic value of serum CHI3L1 in HBeAg-negative CHB fibrosis staging was analyzed by receiver operating characteristic (ROC) curve, and the multivariate analysis of the risk factors for FB in HBeAg-negative CHB patients was performed using the Logistic regression model. This study found that serum CHI3L1 was positively correlated not only with LF markers (FIB-4, GPR), but also with LF staging. Serum CHI3L1 had high diagnostic efficiency for LF staging, with the sensitivity and specificity of 80.00% and 71.05%, respectively. In addition, CHI3L1, FIB-4, and GPR were identified to be the risk factors for LF in HBeAg-negative CHB. In conclusion, serum CHI3L1 can be used as a diagnostic marker and risk factor for LF in patients with HBeAg-negative CHB.

Multi-Size Deep Learning Based Preoperative Computed Tomography Signature for Prognosis Prediction of Colorectal Cancer.

Background: Preoperative and postoperative evaluation of colorectal cancer (CRC) patients is crucial for subsequent treatment guidance. Our study aims to provide a timely and rapid assessment of the prognosis of CRC patients with deep learning according to non-invasive preoperative computed tomography (CT) and explore the underlying biological explanations. Methods: A total of 808 CRC patients with preoperative CT (development cohort: n = 426, validation cohort: n = 382) were enrolled in our study. We proposed a novel end-to-end Multi-Size Convolutional Neural Network (MSCNN) to predict the risk of CRC recurrence with CT images (CT signature). The prognostic performance of CT signature was evaluated by Kaplan-Meier curve. An integrated nomogram was constructed to improve the clinical utility of CT signature by combining with other clinicopathologic factors. Further visualization and correlation analysis for CT deep features with paired gene expression profiles were performed to reveal the molecular characteristics of CRC tumors learned by MSCNN in radiographic imaging. Results: The Kaplan-Meier analysis showed that CT signature was a significant prognostic factor for CRC disease-free survival (DFS) prediction [development cohort: hazard ratio (HR): 50.7, 95% CI: 28.4-90.6, p < 0.001; validation cohort: HR: 2.04, 95% CI: 1.44-2.89, p < 0.001]. Multivariable analysis confirmed the independence prognostic value of CT signature (development cohort: HR: 30.7, 95% CI: 19.8-69.3, p < 0.001; validation cohort: HR: 1.83, 95% CI: 1.19-2.83, p = 0.006). Dimension reduction and visualization of CT deep features demonstrated a high correlation with the prognosis of CRC patients. Functional pathway analysis further indicated that CRC patients with high CT signature presented down-regulation of several immunology pathways. Correlation analysis found that CT deep features were mainly associated with activation of metabolic and proliferative pathways. Conclusions: Our deep learning based preoperative CT signature can effectively predict prognosis of CRC patients. Integration analysis of multi-omic data revealed that some molecular characteristics of CRC tumor can be captured by deep learning in CT images.

DNA Repair-Related Gene Signature in Predicting Prognosis of Colorectal Cancer Patients.

Background: Increasing evidence have depicted that DNA repair-related genes (DRGs) are associated with the prognosis of colorectal cancer (CRC) patients. Thus, the aim of this study was to evaluate the impact of DNA repair-related gene signature (DRGS) in predicting the prognosis of CRC patients. Method: In this study, we retrospectively analyzed the gene expression profiles from six CRC cohorts. A total of 1,768 CRC patients with complete prognostic information were divided into the training cohort (n = 566) and two validation cohorts (n = 624 and 578, respectively). The LASSO Cox model was applied to construct a prediction model. To further validate the clinical significance of the model, we also validated the model with Genomics of Drug Sensitivity in Cancer (GDSC) and an advanced clear cell renal cell carcinoma (ccRCC) immunotherapy data set. Results: We constructed a prognostic DRGS consisting of 11 different genes to stratify patients into high- and low-risk groups. Patients in the high-risk groups had significantly worse disease-free survival (DFS) than those in the low-risk groups in all cohorts [training cohort: hazard ratio (HR) = 2.40, p < 0.001, 95% confidence interval (CI) = 1.67-3.44; validation-1: HR = 2.20, p < 0.001, 95% CI = 1.38-3.49 and validation-2 cohort: HR = 2.12, p < 0.001, 95% CI = 1.40-3.21). By validating the model with GDSC, we could see that among the chemotherapeutic drugs such as oxaliplatin, 5-fluorouracil, and irinotecan, the IC50 of the cell line in the low-risk group was lower. By validating the model with the ccRCC immunotherapy data set, we can clearly see that the overall survival (OS) of the objective response rate (ORR) with complete response (CR) and partial response (PR) in the low-risk group was the best. Conclusions: DRGS is a favorable prediction model for patients with CRC, and our model can predict the response of cell lines to chemotherapeutic agents and potentially predict the response of patients to immunotherapy.

Research on the Stationarity of Hexapod Robot Posture Adjustment.

This paper proposes a smooth adjustment method for the instability problem that occurs during the start and stop of a multi-footed robot during attitude change. First, kinematics analysis is used to establish the mapping relationship between the joint angles of the robot support legs and the body posture. The leg joint angle is a known quantity that can be measured accurately and in real time. Therefore, when the position of the foot end of the support leg is unchanged, a unique set of joint angles can be obtained with the change of body posture at a certain moment. Based on the designed mapping model, the smooth adjustment of the posture can be achieved by the smooth adjustment of the support legs. Second, a constraint index that satisfies the requirements of the robot's steady adjustment of the robot is given. The S-curve acceleration/deceleration method is used to plan the body's attitude angle transformation curve, and then the mapping control relationship is used to obtain the control trajectory requirements of the joint to achieve smooth adjustment. In addition, this paper also gives a simple choice and motion control method for the redundancy problem caused by the number of support legs of a multi-footed robot when the attitude is changed. The simulation and prototype experiments verify and analyze the proposed method. The results of comparative experiments show that the posture adjustment method proposed in this paper has continuous acceleration without breakpoints, the speed changes gently during the start and stop phases of the attitude transformation, and there is no sudden change in the entire process, which improves the consistency of the actual values of the attitude planning curve with the target values. The physical prototype experiment shows that the maximum deviation between the actual value of the attitude angular velocity and the target value changes from 62.5% to 5.5%, and the degree of fit increases by 57.0%. Therefore, this study solves the problem of the instability of the fuselage when the robot changes its attitude, and it provides an important reference for the multi-footed robot to improve the terrain adaptability.

A Metabolism-Related Radiomics Signature for Predicting the Prognosis of Colorectal Cancer.

Background: Radiomics refers to the extraction of a large amount of image information from medical images, which can provide decision support for clinicians. In this study, we developed and validated a radiomics-based nomogram to predict the prognosis of colorectal cancer (CRC). Methods: A total of 381 patients with colorectal cancer (primary cohort: n = 242; validation cohort: n = 139) were enrolled and radiomic features were extracted from the vein phase of preoperative computed tomography (CT). The radiomics score was generated by using the least absolute shrinkage and selection operator algorithm (LASSO). A nomogram was constructed by combining the radiomics score with clinicopathological risk factors for predicting the prognosis of CRC patients. The performance of the nomogram was evaluated by the calibration curve, receiver operating characteristic (ROC) curve and C-index statistics. Functional analysis and correlation analysis were used to explore the underlying association between radiomic feature and the gene-expression patterns. Results: Five radiomic features were selected to calculate the radiomics score by using the LASSO regression model. The Kaplan-Meier analysis showed that radiomics score was significantly associated with disease-free survival (DFS) [primary cohort: hazard ratio (HR): 5.65, 95% CI: 2.26-14.13, P < 0.001; validation cohort: HR: 8.49, 95% CI: 2.05-35.17, P < 0.001]. Multivariable analysis confirmed the independent prognostic value of radiomics score (primary cohort: HR: 5.35, 95% CI: 2.14-13.39, P < 0.001; validation cohort: HR: 5.19, 95% CI: 1.22-22.00, P = 0.026). We incorporated radiomics signature with the TNM stage to build a nomogram, which performed better than TNM stage alone. The C-index of the nomogram achieved 0.74 (0.69-0.80) in the primary cohort and 0.82 (0.77-0.87) in the validation cohort. Functional analysis and correlation analysis found that the radiomic signatures were mainly associated with metabolism related pathways. Conclusions: The radiomics score derived from the preoperative CT image was an independent prognostic factor and could be a complement to the current staging strategies of colorectal cancer.

A signature of hypoxia-related factors reveals functional dysregulation and robustly predicts clinical outcomes in stage I/II colorectal cancer patients.

BACKGROUND: The hypoxic tumor microenvironment accelerates the invasion and migration of colorectal cancer (CRC) cells. The aim of this study was to develop and validate a hypoxia gene signature for predicting the outcome in stage I/II CRC patients that have limited therapeutic options. METHODS: The hypoxic gene signature (HGS) was constructed using transcriptomic data of 309 CRC patients with complete clinical information from the CIT microarray dataset. A total of 1877 CRC patients with complete prognostic information in six independent datasets were divided into a training cohort and two validation cohorts. Univariate and multivariate analyses were conducted to evaluate the prognostic value of HGS. RESULTS: The HGS consisted of 14 genes, and demarcated the CRC patients into the high- and low-risk groups. In all three cohorts, patients in the high-risk group had significantly worse disease free survival (DFS) compared with those in the low risk group (training cohort-HR = 4.35, 95% CI 2.30-8.23, P < 0.001; TCGA cohort-HR = 2.14, 95% CI 1.09-4.21, P = 0.024; meta-validation cohort-HR = 1.91, 95% CI 1.08-3.39, P = 0.024). Compared to Oncotype DX, HGS showed superior predictive outcome in the training cohort (C-index, 0.80 vs 0.65) and the validation cohort (C-index, 0.70 vs 0.61). Pathway analysis of the high- and low-HGS groups showed significant differences in the expression of genes involved in mTROC1, G2-M, mitosis, oxidative phosphorylation, MYC and PI3K-AKT-mTOR pathways (P < 0.005). CONCLUSION: Hypoxic gene signature is a satisfactory prognostic model for early stage CRC patients, and the exact biological mechanism needs to be validated further.