Direct Measurements of Turbulence in the Upper Western Pacific North Equatorial Current over a 25-h Period.

Measurements of the turbulent kinetic energy dissipation rate (ε) were conducted by a free-fall microstructure profiler in the western Pacific North Equatorial Current (WPNEC) during a continuous period of 25 h, from the sea surface to about 160 m depth. In the mixed layer (ML), ε values were typically on the order of 10-8∼10-7 W kg-1, and an obvious diurnal cycle existed in the upper 40 m of the surface mixing layer. Below the ML, ε was reduced to 10-9∼10-8 W kg-1 with some patches of high ε reaching 10-7.5 W kg-1. The barrier layer was identified in the nighttime with a maximum thickness of 20 m, and it was eroded by the advection of freshwater within the lower part of the isothermal layers associated with an anticyclonic eddy in the afternoon. A simple scaling relevant to shear (S2) instability and stratification (N2) that can predict turbulent dissipation rates in the transition layer, between the well-mixed layer and the thermocline below, was obtained through the scaling ε∼S-0.40N0.20. Besides turbulence, double-diffusive processes also contributed to the vertical mixing levels in the upper WPNEC.

Comprehensive analysis of a chemokine- and chemokine receptor family-based signature for patients with lung adenocarcinoma.

The clinical significance and comprehensive features of chemokines and their receptors in lung adenocarcinoma (LUAD) have not been clarified. We aimed to characterize the expression profiles of chemokine and chemokine receptor family members and construct a chemokine- and chemokine receptor-based prognosis signature. A total of 1511 patients with LUAD from seven independent cohorts were included in the study. The training set collected from The Cancer Genome Atlas (TCGA) database containing 468 cases. The validation was performed on the basis of six different cohorts downloaded from Gene Expression Omnibus (GEO) database. A five-chemokine- and chemokine receptor-(CXCL2, CXCL13, CCL26, CCL20, CX3CR1) based prognosis signature was constructed with TCGA dataset using LASSO Cox regression and Cox proportional hazards regression analysis. A multivariate analysis verified that this signature was an independent prognostic factor. The predictive value of this signature was further verified by other six independent cohorts and multiple clinical subtypes. We performed immune cell infiltration analysis and biological pathway analysis which provided more insight into this signature-related immune and inflammatory landscape and clarified the intrinsic molecular mechanism by which this signature could be used to predict clinical prognosis. Furthermore, we explored the close relationship between this signature and tumor mutation burden (TMB), neoantigen burden, PD-1, PD-L1, CTLA4, TIDE score, T cell-inflamed score. This signature provided a robust prognostic biomarker for LUAD and could serve as a predictor for immunotherapy response, which may be used as an important supplement to immunotherapy to achieve individualized tumor treatment by optimizing the prognostic management and immunotherapy for patients with LUAD.

Emergent multiferroicity and strain-driven metal-semiconductor transitions in LaMnO3/RMnO3 superlattices (R = Pr, Pm, Sm and Gd).

It is known that rare-earth manganites LnMnO3 with Ln = La to Gd are typical Mott insulators favoring the A-type antiferromagnetic (A-AFM) state. Certainly no ferroelectricity can be possible although the alternatively stacked LnO layers are both polar. Nevertheless, under the inspiration that one plus one is more than two, it is appreciated that by combining two components of this manganite series into a superlattice functionality is added. In this work, we construct a (001)-oriented LaMnO3/RMnO3 (R = Pr, Pm, Sm and Gd) superlattice and investigate the possible emergent ferroelectricity by means of first-principles calculations. It is revealed that the lattice matching in these superlattices may generate lattice distortions to each component based on the scenario of hybrid improper ferroelectricity, resulting in spontaneous ferroelectric polarization, which is larger than the traditional type II Ln'MnO3 (Ln' radius is smaller than that of Gd) polarization. In the meantime, the A-AFM state remains the magnetic ground state of these superlattices. Furthermore, it is predicted that the externally imposed in-plane compressive strain can trigger the semiconductor to half-metal transitions accompanying the A-AFM to ferromagnetic (FM) transitions. The present work sheds light on the possibility to design multiferroic materials and functionality by tailoring artificial superlattices/heterostructures from those non-ferroelectric systems, and to design electronic devices by utilizing the electronic transport properties under epitaxial strain.

Lightweight Convolutional Neural Network and Its Application in Rolling Bearing Fault Diagnosis under Variable Working Conditions.

The rolling bearing is an important part of the train's running gear, and its operating state determines the safety during the running of the train. Therefore, it is important to monitor and diagnose the health status of rolling bearings. A convolutional neural network is widely used in the field of fault diagnosis because it does not require feature extraction. Considering that the size of the network model is large and the requirements for monitoring equipment are high. This study proposes a novel bearing fault diagnosis method based on lightweight network ShuffleNet V2 with batch normalization and L2 regularization. In the experiment, the one-dimensional time-domain signal is converted into a two-dimensional Time-Frequency Graph (TFG) using a short-time Fourier transform, though the principle of graphics to enhance the TFG dataset. The model mainly consists of two units, one for extracting features and one for spatial down-sampling. The building units are repeatedly stacked to construct the whole model. By comparing the proposed method with the origin ShuffleNet V2, machine learning model and state-of-the-art fault diagnosis model, the generalization of the proposed method for bearing fault diagnosis is verified.

Bayesian additive decision trees of biomarker by treatment interactions for predictive biomarker detection and subgroup identification.

Personalized medicine, or tailored therapy, has been an active and important topic in recent medical research. Many methods have been proposed in the literature for predictive biomarker detection and subgroup identification. In this article, we propose a novel decision tree-based approach applicable in randomized clinical trials. We model the prognostic effects of the biomarkers using additive regression trees and the biomarker-by-treatment effect using a single regression tree. Bayesian approach is utilized to periodically revise the split variables and the split rules of the decision trees, which provides a better overall fitting. Gibbs sampler is implemented in the MCMC procedure, which updates the prognostic trees and the interaction tree separately. We use the posterior distribution of the interaction tree to construct the predictive scores of the biomarkers and to identify the subgroup where the treatment is superior to the control. Numerical simulations show that our proposed method performs well under various settings comparing to existing methods. We also demonstrate an application of our method in a real clinical trial.

A Bayesian three-parameter logistic model for early- and late-onset DLTs in oncology Phase I studies.

We introduce a three-parameter logistic model to analyze the dose limiting toxicity (DLT) as a time-to-event endpoint in oncology Phase I trials. In the proposed model, patients are allowed to stay on trial without the constraint of a maximum follow-up time. Our model accommodates late-onset DLT as well as early-onset DLT, by both of which the dose recommendation is informed. A Bayesian approach is used to incorporate prior knowledge of the test treatment into dose recommendation. Simulation examples show that our proposed model has good operating characteristics in assessing the maximum tolerated dose (MTD).

Prognostic model for predicting the survival benefit of adjuvant chemotherapy for elderly patients with stage II colon cancer: a population-based study.

OBJECTIVES: Adjuvant chemotherapy benefits in elderly patients with stage II colon cancer (CC) remain controversial. We aimed to construct a nomogram to estimate the chemotherapy survival benefits in elderly patients. METHODS: The training and testing cohort were patients with stage II CC older than 70 years from the Surveillance, Epidemiology, and End Results (SEER) database, while the external validation cohort included patients from the National Cancer Center (NCC). Cox proportional hazard models were used to determine the covariates associated with overall survival (OS). Using the risk factors identified by Cox proportional hazards regression, a nomogram was developed to predict OS. Nomogram precision was assessed using receiver operating characteristic and calibration curves. RESULTS: The present study recruited 42 097 and 504 patients from the SEER database and NCC, respectively. The OS of patients who underwent surgery plus adjuvant chemotherapy was considerably longer than patients who underwent surgery alone. The nomogram included variables related to OS, including age, year of diagnosis, sex, AJCC T stage, tumor location, tumor size, harvested lymph nodes, and chemotherapy. According to the nomogram score, the elderly patients were separated into high- and low-risk groups, with high-risk group nomogram scores being greater than the median value, and vice versa. Patients in the high-risk group witnessed worse prognosis and were more likely to benefit from postoperative chemotherapy. CONCLUSION: This nomogram can be regarded as a useful clinical tool for assessing the potential adjuvant chemotherapy benefits and for predicting survival in elderly patients with stage II CC.

A biomechanics and energetics dataset of neurotypical adults walking with and without kinematic constraints.

Numerous studies have explored the biomechanics and energetics of human walking, offering valuable insights into how we walk. However, prior studies focused on changing external factors (e.g., walking speed) and examined group averages and trends rather than individual adaptations in the presence of internal constraints (e.g., injury-related muscle weakness). To address this gap, this paper presents an open dataset of human walking biomechanics and energetics collected from 21 neurotypical young adults. To investigate the effects of internal constraints (reduced joint range of motion), the participants are both the control group (free walking) and the intervention group (constrained walking - left knee fully extended using a passive orthosis). Each subject walked on a dual-belt treadmill at three speeds (0.4, 0.8, and 1.1 m/s) and five step frequencies ( - 10% to 20% of their preferred frequency) for a total of 30 test conditions. The dataset includes raw and segmented data featuring ground reaction forces, joint motion, muscle activity, and metabolic data. Additionally, a sample code is provided for basic data manipulation and visualisation.

CXXC finger protein 1 (CFP1) bridges the reshaping of genomic H3K4me3 signature to the advancement of lung adenocarcinoma.

Histone H3 lysine 4 trimethylation (H3K4me3) is a canonical chromatin modification associated with active gene transcription, playing a pivotal role in regulating various cellular functions. Components of the H3K4me3 methyltransferase complex, known as the proteins associated with SET1 (COMPASS), have been implicated in exerting cancer-protective or cancer-inhibitory effects through inducive H3K4me3 modification. However, the role of the indispensable non-catalytic component of COMPASS CXXC-type zinc finger protein 1 (CFP1) in malignant progression remains unclear. We have unveiled that CFP1 promote lung adenocarcinoma (LUAD) cell proliferation, migration, and invasion while impairing cell apoptosis through in vitro and in vivo models. In addition, high CFP1 expression was identified as emerged as an adverse prognostic indicator across multiple public and in-house LUAD datasets. Notably, CFP1 deficiency led to dual effects on cancer cell transcriptome including extensive inactivation of cancer-promoting as well as activation of cancer repressors. Combining this with the chromatin immunoprecipitation sequencing (ChIP-seq) analysis, we showed that CFP1 ablation reshaped the genomic H3K4me3 distribution signature, with prominent effects on TGF-ß and WNT signaling pathways. Collectively, our study proposes that CFP1 mediates tumorigenesis by genomic histone methylation reprogramming, offering insights for future investigations into epigenetic modifications in cancer progression and potential therapeutic advancements.

Pan-cancer analysis identifies PD-L2 as a tumor promotor in the tumor microenvironment.

Background: Programmed cell death protein 1 (PD-1) receptor has two ligands,programmed death-ligand 1 (PD-L1) and PD-L2. When compared with PD-L1, PD-L2 has not received much attention, and its role remains unclear. Methods: The expression profiles of pdcd1lg2 (PD-L2-encoding gene) mRNA and PD-L2 protein were analyzed using TCGA, ICGC, and HPA databases. Kaplan-Meier and Cox regression analyses were used to assess the prognostic significance of PD-L2. We used GSEA, Spearman's correlation analysis and PPI network to explore the biological functions of PD-L2. PD-L2-associated immune cell infiltration was evaluated using the ESTIMATE algorithm and TIMER 2.0. The expressions of PD-L2 in tumor-associated macrophages (TAMs) in human colon cancer samples, and in mice in an immunocompetent syngeneic setting were verified using scRNA-seq datasets, multiplex immunofluorescence staining, and flow cytometry. After fluorescence-activated cell sorting, flow cytometry and qRT-PCR and transwell and colony formation assays were used to evaluate the phenotype and functions of PD-L2+TAMs. Immune checkpoint inhibitors (ICIs) therapy prediction analysis was performed using TIDE and TISMO. Last, a series of targeted small-molecule drugs with promising therapeutic effects were predicted using the GSCA platform. Results: PD-L2 was expressed in all the common human cancer types and deteriorated outcomes in multiple cancers. PPI network and Spearman's correlation analysis revealed that PD-L2 was closely associated with many immune molecules. Moreover, both GSEA results of KEGG pathways and GSEA results for Reactome analysis indicated that PD-L2 expression played an important role in cancer immune response. Further analysis showed that PD-L2 expression was strongly associated with the infiltration of immune cells in tumor tissue in almost all cancer types, among which macrophages were the most positively associated with PD-L2 in colon cancer. According to the results mentioned above, we verified the expression of PD-L2 in TAMs in colon cancer and found that PD-L2+TAMs population was not static. Additionally, PD-L2+TAMs exhibited protumor M2 phenotype and increased the migration, invasion, and proliferative capacity of colon cancer cells. Furthermore, PD-L2 had a substantial predictive value for ICIs therapy cohorts. Conclusion: PD-L2 in the TME, especially expressed on TAMs, could be applied as a potential therapeutic target.

Nomograms predicting cancer-specific survival for stage IV colorectal cancer with synchronous lung metastases.

This study aimed to establish a nomogram for the prediction of cancer-specific survival (CSS) of CRC patients with synchronous LM. The final prognostic nomogram based on prognostic factors was evaluated by concordance index (C-index), time-dependent receiver operating characteristic curves, and calibration curves. In the training and validation groups, the C-index for the nomogram was 0.648 and 0.638, and the AUC was 0.793 and 0.785, respectively. The high quality of the calibration curves in the nomogram models for CSS at 1-, 3-, and 5-year was observed. The nomogram model provided a conventional and useful tool to evaluate the 1-, 3-, and 5-year CSS of CRC patients with synchronous LM.

Varying Joint Patterns and Compensatory Strategies Can Lead to the Same Functional Gait Outcomes: A Case Study.

This paper analyses joint-space walking mechanisms and redundancies in delivering functional gait outcomes. Multiple biomechanical measures are analysed for two healthy male adults who participated in a multi-factorial study and walked during three sessions. Both participants employed varying intra- and inter-personal compensatory strategies (e.g., vaulting, hip hiking) across walking conditions and exhibited notable gait pattern alterations while keeping task-space (functional) gait parameters invariant. They also preferred various levels of asymmetric step length but kept their symmetric step time consistent and cadence-invariant during free walking. The results demonstrate the importance of an individualised approach and the need for a paradigm shift from functional (task-space) to joint-space gait analysis in attending to (a)typical gaits and delivering human-centred human-robot interaction.

Risk and prognosis of secondary bladder cancer after radiation therapy for pelvic cancer.

Background: Radiation therapy (RT) is a crucial modality for the local control of pelvic cancer (PC), but the effect of pelvic RT on the development of secondary malignancy is still unclear. This study aimed to identify the relationship between radiation therapy received for the treatment of primary PC and subsequent secondary bladder cancer (SBC). Methods: The Surveillance, Epidemiology, and End Results (SEER) database (from 1975 to 2015) was queried for PC. Fine-gray competing risk regression and Cox regression analyses were employed to assess the cumulative incidence of SBC. Poisson regression and multiple primary standardized incidence ratios (SIR) were used to evaluate the radiotherapy-associated risk for patients receiving RT. Subgroup analyses of patients stratified by latency time since PC diagnosis, calendar year of PC diagnosis stage, and age at PC diagnosis were also performed. Overall survival (OS) was compared among different treatment groups with SBC by Kaplan-Meier analysis. Results: A total of 318,165 observations showed that the primary cancers were located in pelvic cavity, 256,313 patients did not receive radiation therapy (NRT), 51,347 patients who underwent external beam radiation therapy (EBRT), and 10,505 patients receiving a combination of EBRT and brachytherapy (EBRT-BRT) who developed SBC. Receiving two types of radiotherapy was strongly consistent with a higher risk of developing SBC for PC patients in Fine-Gray competing risk regression (NRT vs. EBRT, adjusted HR= 1.71, 95% CI: 1.54-1.90, P<0.001; NRT vs. EBRT-BRT, adjusted HR= 2.16, 95% CI: 1.78-2.63, P<0.001). The results of the dynamic SIR and Poisson regression analysis for SBC revealed that a slightly increased risk of SBC was observed after RT in the early latency and was significantly related to the variations of age at PC diagnosis and decreased with time progress. For OS, the SBC after NRT, SBC after EBRT, and SBC after EBRT-BRT of 10-year survival rates were 37.9%, 29.2%, and 22.2%, respectively. Conclusion: Radiotherapy for primary PC was associated with higher risks of developing SBC than patients unexposed to radiotherapy. Different pelvic RT treatment modalities had different effects on the risk of SBC.

Lipid metabolism-related lncRNA SLC25A21-AS1 promotes the progression of oesophageal squamous cell carcinoma by regulating the NPM1/c-Myc axis and SLC25A21 expression.

BACKGROUND: Obesity alters metabolic microenvironment and is thus associated with several tumours. The aim of the present study was to investigate the role, molecular mechanism of action, and potential clinical value of lipid metabolism-related long non-coding RNA (lncRNA) SLC25A21-AS1 in oesophageal squamous cell carcinoma (ESCC). METHODS: A high-fat diets (HFDs)-induced obesity nude mouse model was established, and targeted metabolomics analysis was used to identify critical medium-long chain fatty acids influencing the growth of ESCC cells. Transcriptomic analysis of public dataset GSE53625 confirmed that lncRNA SLC25A21-AS1 was a lipid metabolism-related lncRNA. The biological function of lncRNA SLC25A21-AS1 in ESCC was investigated both in vivo and in vitro. Chromatin immunoprecipitation(ChIP)assay, RNA-pull down, mass spectrometry, co-IP, and RNA IP(RIP) were performed to explore the molecular mechanism. Finally, an ESCC cDNA microarray was used to determine the clinical prognostic value of SLC25A21-AS1 by RT-qPCR. RESULTS: Palmitic acid (PA) is an important fatty acid component of HFD and had an inhibitory effect on ESCC cell lines. LncRNA SLC25A21-AS1 expression was downregulated by PA and associated with the proliferation and migration of ESCC cells in vitro and in vivo. Mechanistically, SLC25A21-AS1 interacted with nucleophosmin-1 (NPM1) protein to promote the downstream gene transcription of the c-Myc in the nucleus. In the cytoplasm, SLC25A21-AS1 maintained the stability of SLC25A21 mRNA and reduced the intracellular NAD+ /NADH ratio by influencing tryptophan catabolism. Finally, we demonstrated that high expression of SLC25A21-AS1 promoted resistance to cisplatin-induced apoptosis and was correlated with poor tumour grade and overall survival. CONCLUSIONS: HFD/PA has an inhibitory effect on ESCC cells and SLC25A21-AS1 expression. SLC25A21-AS1 promotes the proliferation and migration of ESCC cells by regulating the NPM1/c-Myc axis and SLC25A21 expression. In addition, lncRNA SLC25A21-AS1 may serve as a favourable prognostic biomarker and a potential therapeutic target for ESCC.

Transmembrane Protein-Based Risk Model and H3K4me3 Modification Characteristics in Lung Adenocarcinoma.

The role and mechanism of transmembrane proteins (TMEMs) in tumorigenesis remain unclear. Based on 4 independent cohorts containing 1,208 cases, we identified 3 TMEMs (TMEM273, TMEM164, and TMEM125), which were used to construct a risk model to predict the prognosis of LUAD. The two patterns based on the risk score exhibited a high degree of consistency with the characteristics of immune cell infiltration and epigenetic distribution. Patients with a low-risk score, characterized by an increased activation of immunity, H3K4me3 modification, tumor cell apoptosis, chemokine secretion, and TMB, had better disease-free survival (DFS) and overall survival (OS). Obvious immunosuppression, increased epithelial-mesenchymal transition, a low H3K4me3 level, shortened cell cycle, and accelerated cell division manifested in high-risk patients, with poorer DFS and OS. The model showed a better prognostic value than the tumor immune dysfunction and exclusion score. Correlation analysis told us that patients with high scores were suitable for treatment with CD276 inhibitors for their higher levels of CD276 expression. The risk score had a strong negative correlation with HAVCR2 and ICOS among patients with EGFR-WT, KRAS-WT, STK11-WT, or TP53-MUT, and patients with these mutation types with low scores were suitable for treatment with HAVCR2 or ICOS inhibitors. This work comprehensively analyzed the role and mechanism of TMEMs in LUAD and revealed the characteristics of histone methylation modification. The TMEM-based signature gave us deep insight into immune cell infiltration profiles and provided an individualized immunotherapy strategy.

Risk and prognosis of second corpus uteri cancer after radiation therapy for pelvic cancer: A population-based analysis.

Background: Radiation therapy (RT) is a standard treatment for the local control of primary pelvic cancers (PPC), yet the risk of second corpus uteri cancer (SCUC) in PPC patients undergoing RT is still controversial. This study investigated the impact of RT on the risk of SCUC and assessed the survival outcome. Methods: We queried nine cancer registries for PPC cases in the Surveillance, Epidemiology, and End Results (SEER) database. The cumulative incidence of SCUC was analyzed using Cox regression and Fine-Gray competing risk regression analysis. The Poisson regression analysis was employed to assess the standardized incidence ratios (SIRs) and radiation-attributed risk (RR) for SCUC. We evaluated the overall survival of patients with SCUC using the Kaplan-Meier method. Results: Receiving radiotherapy was strongly associated with a higher risk of developing SCUC for PPC patients in Fine-Gray competing risk regression (No-RT vs. RT: adjusted HR = 1.77; 95% CI, 1.40-2.28; p < 0.001). The incidence of SCUC in PPC patients who received RT was higher than in the US general population (SIR, 1.66; 95% CI, 1.41-1.93; p < 0.05), but the incidence of SCUC in patients who did not receive RT was lower than with the US general population (SIR, 0.68; 95% CI, 0.61-0.75; p < 0.05). The dynamic SIR and RR for SCUC decreased with decreasing age at PPC diagnosis and decreased with time progress. In terms of overall survival, 10-year survival rates with SCUC after No-RT (NRT) and SCUC after RT were 45.9% and 25.9% (HR = 1.82; 95% CI, 1.46-2.29; p < 0.001), respectively. Conclusion: Radiotherapy for primary pelvic cancers is associated with a higher risk of developing SCUC than patients unexposed to radiotherapy. We suggest that patients with pelvic RT, especially young patients, should receive long-term monitoring for the risk of developing SCUC.

Prognostic and immunotherapeutic significance of mannose receptor C type II in 33 cancers: An integrated analysis.

Background: The type 2 mannose receptor C (MRC2) is involved in tumor biological processes and plays a new role in the remodeling of the extracellular matrix turnover. Previous studies have demonstrated MRC2 expression profiling and prognostic relevance in some tumor types. However, the clinical and immunotherapeutic value of MRC2 in pan-cancers remains controversial. Our study aimed to evaluate MRC2 expression pattern, clinical characteristics and prognostic significance in 33 cancers, explore the relationship between MRC2 and immune-related characteristics, and assess the prediction of MRC2 for the immunotherapeutic response. Methods: Transcriptional and clinical data of 33 cancers were downloaded from The Cancer Genome Atlas database (TCGA) database and two independent immunotherapeutic cohorts were obtained from GSE67501 and the IMvigor210 study. Next, patients stratified by MRC2 expression levels were displayed by Kaplan-Meier plot to compare prognosis-related indexes. Meanwhile, immune infiltrates of different cancers were estimated by tumor immune estimation resources (TIMER) and CIBERSORT. The ESTIMATE algorithm was used to estimate the immune and stromal scores in tumor tissues. MRC2 expression and immunological modulators, including immune inhibitors, immune stimulators, and MHC molecules, were screened through the TISIDB portal. Gene-set enrichment analysis analyses were performed to explore the underlying biological process of MRC2 across different cancers. The immunotherapeutic response prediction was performed in two independent cohorts (GSE78220: metastatic melanoma with pembrolizumab treatment and IMvigor210: advanced urothelial cancer with atezolizumab intervention). Results: MRC2 is expressed differently in many cancers and has been shown to have potential prognostic predicting significance. MRC2 was significantly associated with immune cell infiltration, immune modulators, and immunotherapeutic markers. Notably, the immunotherapeutic response group was associated with lower MRC2 expression in metastatic melanoma and advanced urothelial carcinoma cohort. Conclusion: This study demonstrated that MRC2 could be a prognostic indicator for certain cancer and is critical for tumor immune microenvironments. MRC2 expression level may influence and predict immune checkpoint blockade response as a potential indicator.

Integrative Analysis of Biomarkers Through Machine Learning Identifies Stemness Features in Colorectal Cancer.

Background: Cancer stem cells (CSCs), which are characterized by self-renewal and plasticity, are highly correlated with tumor metastasis and drug resistance. To fully understand the role of CSCs in colorectal cancer (CRC), we evaluated the stemness traits and prognostic value of stemness-related genes in CRC. Methods: In this study, the data from 616 CRC patients from The Cancer Genome Atlas (TCGA) were assessed and subtyped based on the mRNA expression-based stemness index (mRNAsi). The correlations of cancer stemness with the immune microenvironment, tumor mutational burden (TMB), and N6-methyladenosine (m6A) RNA methylation regulators were analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to identify the crucial stemness-related genes and modules. Furthermore, a prognostic expression signature was constructed using the Lasso-penalized Cox regression analysis. The signature was validated via multiplex immunofluorescence staining of tissue samples in an independent cohort of 48 CRC patients. Results: This study suggests that high-mRNAsi scores are associated with poor overall survival in stage IV CRC patients. Moreover, the levels of TMB and m6A RNA methylation regulators were positively correlated with mRNAsi scores, and low-mRNAsi scores were characterized by increased immune activity in CRC. The analysis identified 34 key genes as candidate prognosis biomarkers. Finally, a three-gene prognostic signature (PARPBP, KNSTRN, and KIF2C) was explored together with specific clinical features to construct a nomogram, which was successfully validated in an external cohort. Conclusion: There is a unique correlation between CSCs and the prognosis of CRC patients, and the novel biomarkers related to cell stemness could accurately predict the clinical outcomes of these patients.

A Multi-Center Validated Subtyping Model of Esophageal Cancer Based on Three Metabolism-Related Genes.

Metabolic reprogramming is a hallmark of malignancy. Understanding the characteristics of metabolic reprogramming in esophageal squamous cell carcinoma (ESCC) helps uncover novel targets for cancer progression. In this study, 880 metabolism-related genes were identified from microarray data and then filtered to divide patients into two subgroups using consensus clustering, which exhibits significantly different overall survival. After a differential analysis between two subtypes, 3 genes were screened out to construct a two subtypes decision model on the training cohort (GSE53624), defined as high-risk and low-risk subtypes. These risk models were then verified in two public databases (GSE53622 and TCGA-ESCC), an independent cohort of 49 ESCC patients by RT-qPCR and an external cohort of 95 ESCC patients by immunohistochemistry analysis (IHC). Furthermore, the immune cell infiltration of regulatory T cells (Tregs) and plasma cells showed a significant difference between the high and low-risk subtypes in the IHC experiment with 119 ESCC patients. In conclusion, our study indicated that three metabolism-related prognostic genes could stratify patients into subgroups and were associated with immune infiltration, clinical features and clinical outcomes.

Deciphering the Pyroptosis-Related Prognostic Signature and Immune Cell Infiltration Characteristics of Colon Cancer.

Background: Colon cancer (CC) remains one of the most common malignancies with a poor prognosis. Pyroptosis, referred to as cellular inflammatory necrosis, is thought to influence tumor development. However, the potential effects of pyroptosis-related regulators (PRRs) on the CC immune microenvironment remain unknown. Methods: In this study, 27 PRRs reported in the previous study were used to cluster the 1,334 CC samples into three pyroptosis-related molecular patterns. Through subtype pattern differential analysis and structure network mining using Weighted Gene Co-expression Network Analysis (WGCNA), 854 signature genes associated with the PRRs were discovered. Further LASSO-penalized Cox regression of these genes established an eight-gene assessment model for predicting prognosis. Results: The CC patients were subtyped based on three distinct pyroptosis-related molecular patterns. These pyroptosis-related patterns were correlated with different clinical outcomes and immune cell infiltration characteristics in the tumor microenvironment. The pyroptosis-related eight-signature model was established and used to assess the prognosis of CC patients with medium-to-high accuracy by employing the risk scores, which was named "PRM-scores." Greater inflammatory cell infiltration was observed in tumors with low PRM-scores, indicating a potential benefit of immunotherapy in these patients. Conclusions: This study suggests that PRRs have a significant effect on the tumor immune microenvironment and tumor development. Evaluating the pyroptosis-related patterns and related models will promote our understanding of immune cell infiltration characteristics in the tumor microenvironment and provide a theoretical basis for future research targeting pyroptosis in cancer.