Anoikis-related genes linked with patient outcome in pancreatic cancer.

Anoikis is programmed cell death occurring upon cell detachment from the extracellular matrix. Cancer cells need to evade anoikis to be able to metastasize to distant sites. However, the molecular features and prognostic value of anoikis-related genes (ARGs) in pancreatic cancer remain unclear. In this study, we utilized transcriptome data from the TCGA and GSE102238 databases to identify 64 ARGs significantly associated with prognosis. We used the "ConsensusClusterPlus" R package to stratify patients into high and low-risk prognostic subgroups. The KEGG and GSEA analyses revealed that the clusters with poor prognosis were enriched for the ECM receptor interaction pathway, the TP53 signaling pathway, and the galactose metabolism pathway, and that the cell cycle pathway was upregulated. A prognostic model consisting of seven ARGs (SERPINE1, EGF, E2F1, MSLN, RAB27B, ETV7, MST1) was constructed using LASSO regression and when combined with clinicopathological parameters using Cox regression, a prognostic Nomogram was created, which demonstrated high prognostic utility. Among the biomarker candidates, we report ETV7 as a novel, independent prognostic marker in pancreatic cancer. ETV7 was highly expressed in KRAS and TP53 co-occurrent mutant TCGA patients, indicating that it may be regulated by the two major driver genes of pancreatic cancer. Therefore, targeting ETV7 could be a potential focus for future therapeutic studies.

Utilizing machine learning to integrate single-cell and bulk RNA sequencing data for constructing and validating a novel cell adhesion molecules related prognostic model in gastric cancer.

BACKGROUND: Cell adhesion molecules (CAMs) play a vital role in cell-cell interactions, immune response modulation, and tumor cell migration. However, the unique role of CAMs in gastric cancer (GC) remains largely unexplored. METHODS: This study characterized the genetic alterations and mRNA expression of CAMs. The role of CD34, a representative molecule, was validated in 375 GC tissues. The activity of the CAM pathway was further tested using single-cell and bulk characterization. Next, data from 839 patients with GC from three cohorts was analyzed using univariate Cox and random survival forest methods to develop and validate a CAM-related prognostic model. RESULTS: Most CAM-related genes exhibited multi-omics alterations and were associated with clinical outcomes. There was a strong correlation between increased CD34 expression and advanced clinical staging (P = 0.026), extensive vascular infiltration (P = 0.003), and unfavorable prognosis (Log-rank P = 0.022). CD34 expression was also found to be associated with postoperative chemotherapy and tumor immunotherapy response. Furthermore, the CAM pathway was significantly activated and mediated poor prognosis. Additionally, eight prognostic signature genes (PSGs) were identified in the training cohort. There was a substantial upregulation of the expression of immune checkpoints and a pronounced infiltration of immune cells in GC tissues with high PSG score, which is consistent with the prediction of increased sensitivity to immunotherapy. Moreover, 9 compounds from the CTRPv2 database and 13 from the Profiling Relative Inhibition Simultaneously in Mixture (PRISM) database were identified as potential therapeutic drugs for patients with GC with high PSG score. CONCLUSION: Thorough understanding of CAM pathways regulation and the innovative PSG score model hold significant implications for medical diagnosis, potentially enhancing personalized treatment strategies and improving patient outcomes in GC management.

RNA epigenetic modifications in digestive tract cancers: Friends or foes.

Digestive tract cancers are among the most common malignancies worldwide and have high incidence and mortality rates. Thus, the discovery of more effective diagnostic and therapeutic targets is urgently required. The development of technologies to accurately detect RNA modification has led to the identification of numerous RNA chemical modifications in humans (epitranscriptomics) that are involved in the occurrence and development of digestive tract cancers. RNA modifications can cooperatively regulate gene expression to facilitate normal physiological functions of the digestive system. However, the dysfunction of relevant RNA-modifying enzymes ("writers," "erasers," and "readers") can lead to the development of digestive tract cancers. Consequently, targeting dysregulated enzyme activity could represent a potent therapeutic strategy for the treatment of digestive tract cancers. In this review, we summarize the most widely studied roles and mechanisms of RNA modifications (m6A, m1A, m5C, m7G, A-to-I editing, pseudouridine [Ψ]) in relation to digestive tract cancers, highlight the crosstalk between RNA modifications, and discuss their roles in the interactions between the digestive system and microbiota during carcinogenesis. The clinical significance of novel therapeutic methods based on RNA-modifying enzymes is also discussed. This review will help guide future research into digestive tract cancers that are resistant to current therapeutics.

Identification of potential novel N6-methyladenosine effector-related lncRNA biomarkers for serous ovarian carcinoma: a machine learning-based exploration in the framework of 3P medicine.

Background: Serous ovarian carcinoma (SOC) is considered the most lethal gynecological malignancy. The current lack of reliable prognostic biomarkers for SOC reduces the efficacy of predictive, preventive, and personalized medicine (PPPM/3PM) in patients with SOC, leading to unsatisfactory therapeutic outcomes. N6-methyladenosine (m6A) modification-associated long noncoding RNAs (lncRNAs) are effective predictors of SOC. In this study, an effective risk prediction model for SOC was constructed based on m6A modification-associated lncRNAs. Methods: Transcriptomic data and clinical information of patients with SOC were downloaded from The Cancer Genome Atlas. Candidate lncRNAs were identified using univariate and multivariate and least absolute shrinkage and selection operator-penalized Cox regression analyses. The molecular mechanisms of m6A effector-related lncRNAs were explored via Gene Ontology, pathway analysis, gene set enrichment analysis, and gene set variation analysis (GSVA). The extent of immune cell infiltration was assessed using various algorithms, including CIBERSORT, Microenvironment Cell Populations counter, xCell, European Prospective Investigation into Cancer and Nutrition, and GSVA. The calcPhenotype algorithm was used to predict responses to the drugs commonly used in ovarian carcinoma therapy. In vitro experiments, such as migration and invasion Transwell assays, wound healing assays, and dot blot assays, were conducted to elucidate the functional roles of candidate lncRNAs. Results: Six m6A effector-related lncRNAs that were markedly associated with prognosis were used to establish an m6A effector-related lncRNA risk model (m6A-LRM) for SOC. Immune microenvironment analysis suggested that the high-risk group exhibited a proinflammatory state and displayed increased sensitivity to immunotherapy. A nomogram was constructed with the m6A effector-related lncRNAs to assess the prognostic value of the model. Sixteen drugs potentially targeting m6A effector-related lncRNAs were identified. Furthermore, we developed an online web application for clinicians and researchers (https://leley.shinyapps.io/OC_m6A_lnc/). Overexpression of the lncRNA RP11-508M8.1 promoted SOC cell migration and invasion. METTL3 is an upstream regulator of RP11-508M8.1. The preliminary regulatory axis METTL3/m6A/RP11-508M8.1/hsa-miR-1270/ARSD underlying SOC was identified via a combination of in vitro and bioinformatic analyses. Conclusion: In this study, we propose an innovative prognostic risk model and provide novel insights into the mechanism underlying the role of m6A-related lncRNAs in SOC. Incorporating the m6A-LRM into PPPM may help identify high-risk patients and personalize treatment as early as possible.

Multi-omics analysis uncovered systemic lupus erythematosus and COVID-19 crosstalk.

BACKGROUND: Studies have highlighted a possible crosstalk between the pathogeneses of COVID-19 and systemic lupus erythematosus (SLE); however, the interactive mechanisms remain unclear. We aimed to elucidate the impact of COVID-19 on SLE using clinical information and the underlying mechanisms of both diseases. METHODS: RNA-seq datasets were used to identify shared hub gene signatures between COVID-19 and SLE, while genome-wide association study datasets were used to delineate the interaction mechanisms of the key signaling pathways. Finally, single-cell RNA-seq datasets were used to determine the primary target cells expressing the shared hub genes and key signaling pathways. RESULTS: COVID-19 may affect patients with SLE through hematologic involvement and exacerbated inflammatory responses. We identified 14 shared hub genes between COVID-19 and SLE that were significantly associated with interferon (IFN)-I/II. We also screened and obtained four core transcription factors related to these hub genes, confirming the regulatory role of the IFN-I/II-mediated Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway on these hub genes. Further, SLE and COVID-19 can interact via IFN-I/II and IFN-I/II receptors, promoting the levels of monokines, including interleukin (IL)-6/10, tumor necrosis factor-α, and IFN-γ, and elevating the incidence rate and risk of cytokine release syndrome. Therefore, in SLE and COVID-19, both hub genes and core TFs are enriched within monocytes/macrophages. CONCLUSIONS: The interaction between SLE and COVID-19 promotes the activation of the IFN-I/II-triggered JAK-STAT signaling pathway in monocytes/macrophages. These findings provide a new direction and rationale for diagnosing and treating patients with SLE-COVID-19 comorbidity.

Elesclomol Loaded Copper Oxide Nanoplatform Triggers Cuproptosis to Enhance Antitumor Immunotherapy.

The induction of cuproptosis, a recently identified form of copper-dependent immunogenic cell death, is a promising approach for antitumor therapy. However, sufficient accumulation of intracellular copper ions (Cu2+) in tumor cells is essential for inducing cuproptosis. Herein, an intelligent cuproptosis-inducing nanosystem is constructed by encapsulating copper oxide (CuO) nanoparticles with the copper ionophore elesclomol (ES). After uptake by tumor cells, ES@CuO is degraded to release Cu2+ and ES to synergistically trigger cuproptosis, thereby significantly inhibiting the tumor growth of murine B16 melanoma cells. Moreover, ES@CuO further promoted cuproptosis-mediated immune responses and reprogrammed the immunosuppressive tumor microenvironment by increasing the number of tumor-infiltrating lymphocytes and secreted inflammatory cytokines. Additionally, combining ES@CuO with programmed cell death-1 (PD-1) immunotherapy substantially increased the antitumor efficacy in murine melanoma. Overall, the findings of this study can lead to the use of a novel strategy for cuproptosis-mediated antitumor therapy, which may enhance the efficacy of immune checkpoint inhibitor therapy.

Transcription Factor MAZ Potentiates the Upregulated NEIL3-mediated Aerobic Glycolysis, Thereby Promoting Angiogenesis in Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is characterized by high vascularity and notable abnormality of blood vessels, where angiogenesis is a key process in tumorigenesis and metastasis. The main functions of Nei Like DNA Glycosylase 3 (NEIL3) include DNA alcoholization repair, immune response regulation, nervous system development and function, and DNA damage signal transduction. However, the underlying mechanism of high expression NEIL3 in the development and progression of HCC and whether the absence or silencing of NEIL3 inhibits the development of cancer remain unclear. Therefore, a deeper understanding of the mechanisms by which increased NEIL3 expression promotes cancer development is needed. METHODS: Expression of NEIL3 and its upstream transcription factor MAZ in HCC tumor tissues was analyzed in bioinformatics efforts, while validation was done by qRT-PCR and western blot in HCC cell lines. The migration and tube formation capacity of HUVEC cells were analyzed by Transwell and tube formation assays. Glycolytic capacity was analyzed by extracellular acidification rate, glucose uptake, and lactate production levels. Chromatin immunoprecipitation (ChIP) and dual-luciferase reporter gene assays were utilized to investigate specific interactions between MAZ and NEIL3. RESULTS: NEIL3 and MAZ were substantially upregulated in HCC tissues and cells. NEIL3 was involved in modulating the glycolysis pathway, suppression of which reversed the stimulative impact of NEIL3 overexpression on migration and angiogenesis in HUVEC cells. MAZ bound to the promoter of NEIL3 to facilitate NEIL3 transcription. Silencing MAZ reduced NEIL3 expression and suppressed the glycolysis pathway, HUVEC cell migration, and angiogenesis. CONCLUSION: MAZ potentiated the upregulated NEIL3-mediated glycolysis pathway and HCC angiogenesis. This study provided a rationale for the MAZ/NEIL3/glycolysis pathway as a possible option for anti-angiogenesis therapy in HCC.

Emerging roles of the epitranscriptome in parasitic protozoan biology and pathogenesis.

RNA modifications (epitranscriptome) - such as N6-methyladenosine (m6A), 5-methylcytosine (m5C), and pseudouridine (Ψ) - modulate RNA processing, stability, interaction, and translation, thereby playing critical roles in the development, replication, virulence, metabolism, and life cycle adaptations of parasitic protozoa. Here, we summarize potential homologs of the major human RNA modification regulatory factors in parasites, outline current knowledge on how RNA modifications affect parasitic protozoa, highlight the regulation of RNA modifications and their crosstalk, and discuss current progress in exploring RNA modifications as potential drug targets. This review contributes to our understanding of epitranscriptomic regulation of parasitic protozoa biology and pathogenesis and provides new perspectives for the treatment of parasitic diseases.

Unsupervised Object-Centric Learning From Multiple Unspecified Viewpoints.

Visual scenes are extremely diverse, not only because there are infinite possible combinations of objects and backgrounds but also because the observations of the same scene may vary greatly with the change of viewpoints. When observing a multi-object visual scene from multiple viewpoints, humans can perceive the scene compositionally from each viewpoint while achieving the so-called "object constancy" across different viewpoints, even though the exact viewpoints are untold. This ability is essential for humans to identify the same object while moving and to learn from vision efficiently. It is intriguing to design models that have a similar ability. In this article, we consider a novel problem of learning compositional scene representations from multiple unspecified (i.e., unknown and unrelated) viewpoints without using any supervision and propose a deep generative model which separates latent representations into a viewpoint-independent part and a viewpoint-dependent part to solve this problem. During the inference, latent representations are randomly initialized and iteratively updated by integrating the information in different viewpoints with neural networks. Experiments on several specifically designed synthetic datasets have shown that the proposed method can effectively learn from multiple unspecified viewpoints.

The relationship between GLIM-malnutrition, post-operative complications and long-term prognosis in elderly patients undergoing colorectal cancer surgery.

Background: Elderly people and patients with colorectal cancer (CRC) are both at high risk of malnutrition. Therefore, it is of great significance to explore suitable malnutrition screening and diagnostic indicators for elderly patients with CRC. Recently, the Global Leadership Initiative on Malnutrition (GLIM) proposed new diagnostic criteria for malnutrition. The aim of this article was to evaluate the diagnostic value of GLIM criteria for malnutrition in elderly colorectal patients. We explored the relationship between GLIM-malnutrition, post-operative complications and the long-term prognosis of elderly colorectal patients. Methods: Elderly patients (aged ≥65 years) who underwent CRC surgery from January 2015 to December 2018 were included. Malnutrition was diagnosed based on the GLIM criteria. The relationships between GLIM-malnutrition and clinical characteristics were analyzed by t-tests, Mann-Whitney U tests, and chi-squared tests. The relationships between GLIM-malnutrition and post-operative complications were analyzed by chi-squared tests, and logistic regression analyses. The relationships between GLIM-malnutrition and the long-term prognosis were analyzed by Kaplan-Meier analyses and logistic and Cox regression analyses. Results: A total of 385 elderly patients were included in this study, and 118 patients (30.65%) were diagnosed with malnutrition according to the GLIM criteria. GLIM-malnutrition was significantly associated with older age, lower body mass index (BMI), lower grip strength, tumor location, higher Nutrition Risk Screening 2002 (NRS-2002), and lower levels of albumin and hemoglobin. GLIM-malnutrition was an independent risk factor [odds ratio (OR): 1.753, 95% confidence interval (CI): 1.100-2.795, P=0.018] for post-operative complications. Cox regression analysis showed that GLIM-malnutrition was an independent risk factor for overall survival in elderly patients with CRC. Conclusions: The GLIM criteria are feasible diagnostic criteria for malnutrition of elderly patients with CRC. GLIM-malnutrition is significantly associated with post-operative complications and overall survival in elderly patients with CRC.

Construction of a prognostic model for colorectal adenocarcinoma based on Zn transport-related genes identified by single-cell sequencing and weighted co-expression network analysis.

Background: Colorectal cancer (CRC) is one of the most prevalent malignancies and the third most lethal cancer globally. The most reported histological subtype of CRC is colon adenocarcinoma (COAD). The zinc transport pathway is critically involved in various tumors, and its anti-tumor effect may be through improving immune function. However, the Zn transport pathway in COAD has not been reported. Methods: The determination of Zn transport-related genes in COAD was carried out through single-cell analysis of the GSE 161277 obtained from the GEO dataset. Subsequently, a weighted co-expression network analysis of the TCGA cohort was performed. Then, the prognostic model was conducted utilizing univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Functional enrichment, immune microenvironment, and survival analyses were also carried out. Consensus clustering analysis was utilized to verify the validity of the prognostic model and explore the immune microenvironment. Ultimately, cell experiments, including CCK-8,transwell and scratch assays, were performed to identify the function of LRRC59 in COAD. Results: According to the Zn transport-related prognostic model, the individuals with COAD in TCGA and GEO databases were classified into high- and low-risk groups. The group with low risk had a comparatively more favorable prognosis. Two groups had significant variations in the immune infiltration, MHC, and the expression of genes related to the immune checkpoint. The cell experiments indicated that the proliferation, migration, and invasion of the HCT-116, DLD-1, and RKO cell lines were considerably increased after LRRC59 knockdown. It proved that LRRC59 was indeed a protective factor for COAD. Conclusion: A prognostic model for COAD was developed using zinc transport-related genes. This model can efficiently assess the immune microenvironment and prognosis of individuals with COAD. Subsequently, the function of LRRC59 in COAD was validated via cell experiments, highlighting its potential as a biomarker.

DeepSFM: Robust Deep Iterative Refinement for Structure From Motion.

Structure from Motion (SfM) is a fundamental computer vision problem which has not been well handled by deep learning. One of the promising solutions is to apply explicit structural constraint, e.g. 3D cost volume, into the neural network. Obtaining accurate camera pose from images alone can be challenging, especially with complicate environmental factors. Existing methods usually assume accurate camera poses from GT or other methods, which is unrealistic in practice and additional sensors are needed. In this work, we design a physical driven architecture, namely DeepSFM, inspired by traditional Bundle Adjustment, which consists of two cost volume based architectures to iteratively refine depth and pose. The explicit constraints on both depth and pose, when combined with the learning components, bring the merit from both traditional BA and emerging deep learning technology. To speed up the learning and inference efficiency, we apply the Gated Recurrent Units (GRUs)-based depth and pose update modules with coarse to fine cost volumes on the iterative refinements. In addition, with the extended residual depth prediction module, our model can be adapted to dynamic scenes effectively. Extensive experiments on various datasets show that our model achieves the state-of-the-art performance with superior robustness against challenging inputs.

H4MER: Human 4D Modeling by Learning Neural Compositional Representation With Transformer.

Despite the impressive results achieved by deep learning based 3D reconstruction, the techniques of directly learning to model 4D human captures with detailed geometry have been less studied. This work presents a novel neural compositional representation for Human 4D Modeling with transformER (H4MER). Specifically, our H4MER is a compact and compositional representation for dynamic human by exploiting the human body prior from the widely used SMPL parametric model. Thus, H4MER can represent a dynamic 3D human over a temporal span with the codes of shape, initial pose, motion and auxiliaries. A simple yet effective linear motion model is proposed to provide a rough and regularized motion estimation, followed by per-frame compensation for pose and geometry details with the residual encoded in the auxiliary codes. We present a novel Transformer-based feature extractor and conditional GRU decoder to facilitate learning and improve the representation capability. Extensive experiments demonstrate our method is not only effective in recovering dynamic human with accurate motion and detailed geometry, but also amenable to various 4D human related tasks, including monocular video fitting, motion retargeting, 4D completion, and future prediction.

Effects of metalloprotease ADAMTS12 on cervical cancer cell phenotype and its potential mechanism.

ADAMTS12 is a gene widely expressed in human tissues. We studied the expression level of ADAMTS12 in cervical cancer tissue and its relationship with clinicopathological features. We also explored the function of ADAMTS12 in cervical cancer cells and its underlying mechanisms. We found the higher expression level of ADAMTS12 in cancer tissues, which was associated with the worse overall survival rate. The immunofluorescence assay showed that the cytoplasm of cervical cancer cells is the main expression site of ADAMTS12. Overexpression of ADAMTS12 in HeLa and CaSki cells prominently promoted the cell proliferation, migration and invasion. We found that 2032 genes were correlated with ADAMTS12, which was mainly related to extracellular matrix, TGF-ß signaling pathway. The phosphorylation levels of mTOR and 4E-BP1 were upregulated in ADAMTS12-overexpressing cells. Co-Immunoprecipitation combined with protein mass spectrometry showed that TGF-ß signaling pathway-related proteins interacting with ADAMTS12 were screened from HeLa cells with ADAMTS12 overexpression. Therefore, we concluded that ADAMTS12 may affect the mTOR signaling pathway through the interacting with TGF-ß1, and then affect the biological function of cervical cancer cells.

Compositional Scene Representation Learning via Reconstruction: A Survey.

Visual scenes are composed of visual concepts and have the property of combinatorial explosion. An important reason for humans to efficiently learn from diverse visual scenes is the ability of compositional perception, and it is desirable for artificial intelligence to have similar abilities. Compositional scene representation learning is a task that enables such abilities. In recent years, various methods have been proposed to apply deep neural networks, which have been proven to be advantageous in representation learning, to learn compositional scene representations via reconstruction, advancing this research direction into the deep learning era. Learning via reconstruction is advantageous because it may utilize massive unlabeled data and avoid costly and laborious data annotation. In this survey, we first outline the current progress on reconstruction-based compositional scene representation learning with deep neural networks, including development history and categorizations of existing methods from the perspectives of the modeling of visual scenes and the inference of scene representations; then provide benchmarks, including an open source toolbox to reproduce the benchmark experiments, of representative methods that consider the most extensively studied problem setting and form the foundation for other methods; and finally discuss the limitations of existing methods and future directions of this research topic.

Butyrate inhibits the mitochondrial complex Ι to mediate mitochondria-dependent apoptosis of cervical cancer cells.

BACKGROUND: Cervical cancer (CC) is a common gynecological malignancy with high morbidity worldwide. Butyrate, a short-chain fatty acid produced by intestinal flora, has been reported to inhibit cervical carcinogenesis. This study aimed to investigate the pro-apoptotic effects of butyrate on CC and the underlying mechanisms. METHODS: Human HeLa and Ca Ski cells were used in this study. Cell proliferation, cell migration and invasion were detected by CCK-8 and EdU staining, transwell and wound healing assay, respectively. Cell cycle, mitochondrial membrane potential and apoptosis were evaluated by flow cytometry. Western blot and RT-qPCR were carried out to examine the related genes and proteins to the mitochondrial complex Ι and apoptosis. Metabolite changes were analyzed by energy metabolomics and assay kits. The association between G protein-coupled receptor 41, 43, 109a and CC prognosis was analyzed using data from The Cancer Genome Atlas (TCGA). RESULTS: CCK-8 results showed significant inhibition of CC cell proliferation induced by butyrate treatment, which was confirmed by EdU staining and cell cycle detection. Data from the transwell and wound healing assay revealed that CC cell migration was dramatically reduced following butyrate treatment. Additionally, invasiveness was also decreased by butyrate. Western blot analysis showed that cleaved Caspase 3 and cleaved PARP, the enforcers of apoptosis, were increased by butyrate treatment. The results of Annexin V/PI staining and TUNEL also showed an increase in butyrate-induced apoptotic cells. Expression of Cytochrome C (Cytc), Caspase 9, Bax, but not Caspase 12 or 8, were up-regulated under butyrate exposure. Mechanistically, the decrease in mitochondrial NADH and NAD + levels after treatment with butyrate was observed by energy metabolomics and the NAD+/NADH Assay Kit, similar to the effects of the complex Ι inhibitor rotenone. Western blot results also demonstrated that the constituent proteins of mitochondrial complex Ι were reduced by butyrate. Furthermore, mitochondria-dependent apoptosis has been shown to be initiated by inhibition of the complex Ι. CONCLUSION: Collectively, our results revealed that butyrate inhibited the proliferation, migration and invasion of CC cells, and induced mitochondrial-dependent apoptosis by inhibiting mitochondrial complex Ι.

One-shot Federated Learning without server-side training.

Federated Learning (FL) has recently made significant progress as a new machine learning paradigm for privacy protection. Due to the high communication cost of traditional FL, one-shot federated learning is gaining popularity as a way to reduce communication cost between clients and the server. Most of the existing one-shot FL methods are based on Knowledge Distillation; however, distillation based approach requires an extra training phase and depends on publicly available data sets or generated pseudo samples. In this work, we consider a novel and challenging cross-silo setting: performing a single round of parameter aggregation on the local models without server-side training. In this setting, we propose an effective algorithm for Model Aggregation via Exploring Common Harmonized Optima (MA-Echo), which iteratively updates the parameters of all local models to bring them close to a common low-loss area on the loss surface, without harming performance on their own data sets at the same time. Compared to the existing methods, MA-Echo can work well even in extremely non-identical data distribution settings where the support categories of each local model have no overlapped labels with those of the others. We conduct extensive experiments on two popular image classification data sets to compare the proposed method with existing methods and demonstrate the effectiveness of MA-Echo, which clearly outperforms the state-of-the-arts. The source code can be accessed in https://github.com/FudanVI/MAEcho.

Comprehensive analysis of the prognosis, tumor microenvironment, and immunotherapy response of SDHs in colon adenocarcinoma.

Background: Succinate dehydrogenase (SDH), one of the key enzymes in the tricarboxylic acid cycle, is mainly found in the mitochondria. SDH consists of four subunits encoding SDHA, SDHB, SDHC, and SDHD. The biological function of SDH is significantly related to cancer progression. Colorectal cancer (CRC) is one of the most common malignant tumors globally, whose most common histological subtype is colon adenocarcinoma (COAD). However, the correlation between SDH factors and COAD remains unclear. Methods: The data on pan-cancer was obtained from The Cancer Genome Atlas (TCGA) database. Kaplan-Meier survival analysis showed the prognostic ability of SDHs. The cBioPortal database reflected genetic variations of SDHs. The correlation analysis was conducted between SDHs and mitochondrial energy metabolism genes (MMGs) and the protein-protein interaction (PPI) network was built. Consequently, Univariate and Multivariate Cox Regression Analysis on SDHs and other clinical characteristics were conducted. A nomogram was established. The ssGSEA analysis visualized the association between SDHs and immune infiltration. Immunophenoscore (IPS) explored the correlation between SDHs and immunotherapy, and the correlation between SDHs and targeted therapy was investigated through Genomics of Drug Sensitivity in Cancer. Finally, qPCR and immunohistochemistry detected SDHs' expression. Results: After assessing SDHs differential expression in pan-cancer, we found that SDHB, SDHC, and SDHD benefit COAD patients. The cBioPortal database demonstrated that SDHA was the top gene in mutation frequency rank. Correlation analysis mirrored a strong link between SDHs and MMGs. We formulated a nomogram and found that SDHB, SDHC, SDHD, and clinical characteristics correlated with COAD patients' survival. For T helper cells, Th2 cells, and Tem, SDHA, SDHB, SDHC, and SDHD were significantly enriched in the high expression group. Moreover, COAD patients with high SDHA expression were more suitable for immunotherapy. And COAD patients with different SDHs' expression have different sensitivity to targeted drugs. Further verifying the gene and protein expression levels of SDHs, we found that the tissues were consistent with the bioinformatics analysis. Conclusions: Our study analyzed the expression and prognostic value of SDHs in COAD, explored the pathway mechanisms involved, and the immune cell correlations, indicating that SDHs might be biomarkers for COAD patients.