Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysis

BackgroundColon cancer is a highly heterogeneous disease, and identifying molecular subtypes can provide insights into deregulated pathways within tumor subsets, which may lead to personalized treatment options. However, most prognostic models are based on single-pathway genes.MethodsIn this study, we aimed to identify three clinically relevant subtypes of colon cancer based on multiple signaling pathways-related genes. Integrative multi-omics analysis was used to explain the biological processes contributing to colon cancer aggressiveness, recurrence, and progression. Machine learning methods were employed to identify the subtypes and provide medication guidance for distinct subtypes using the L1000 platform. We developed a robust prognostic model (MKPC score) based on gene pairs and validated it in one internal test set and three external test sets. Risk-related genes were extracted and verified by qPCR.ResultsThree clinically relevant subtypes of colon cancer were identified based on multiple signaling pathways-related genes, which had significantly different survival state (Log-Rank test, p<0.05). Integrative multi-omics analysis revealed biological processes contributing to colon cancer aggressiveness, recurrence, and progression. The developed MKPC score, based on gene pairs, was robust in predicting prognosis state (Log-Rank test, p<0.05), and risk-related genes were successfully verified by qPCR (t test, p<0.05). An easy-to-use web tool was created for risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.ConclusionIn conclusion, our study identified three clinically relevant subtypes of colon cancer and developed a robust prognostic model based on gene pairs. The developed web tool is a valuable resource for researchers and clinicians in risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types

Mlsp : A bioinformatics tool for predicting molecular subtypes and prognosis in patients with breast cancer

The molecular landscape in breast cancer is characterized by large biological heterogeneity and variable clinical outcomes. Here, we performed an integrative multi-omics analysis of patients diagnosed with breast cancer. Using transcriptomic analysis, we identified three subtypes (cluster A, cluster B and cluster C) of breast cancer with distinct prognosis, clinical features, and genomic alterations: Cluster A was asso-ciated with higher genomic instability, immune suppression and worst prognosis outcome; cluster B was associated with high activation of immune-pathway, increased mutations and middle prognosis out-come; cluster C was linked to Luminal A subtype patients, moderate immune cell infiltration and best prognosis outcome. Combination of the three newly identified clusters with PAM50 subtypes, we pro-posed potential new precision strategies for 15 subtypes using L1000 database. Then, we developed a robust gene pair (RGP) score for prognosis outcome prediction of patients with breast cancer. The RGP score is based on a novel gene-pairing approach to eliminate batch effects caused by differences in heterogeneous patient cohorts and transcriptomic data distributions, and it was validated in ten cohorts of patients with breast cancer. Finally, we developed a user-friendly web-tool (https://sujiezhulab.shi-nyapps.io/BRCA/) to predict subtype, treatment strategies and prognosis states for patients with breast cancer.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creative-commons.org/licenses/by-nc-nd/4.0/).Peer reviewe

An immunity and pyroptosis gene-pair signature predicts overall survival in acute myeloid leukemia

Treatment responses of patients with acute myeloid leukemia (AML) are known to be heterogeneous, posing challenges for risk scoring and treatment stratification. In this retrospective multi-cohort study, we investigated whether combining pyroptosis- and immune-related genes improves prognostic classification of AML patients. Using a robust gene pairing approach, which effectively eliminates batch effects across heterogeneous patient cohorts and transcriptomic data, we developed an immunity and pyroptosis-related prognostic (IPRP) signature that consists of 15 genes. Using 5 AML cohorts (n = 1327 patients total), we demonstrate that the IPRP score leads to more consistent and accurate survival prediction performance, compared with 10 existing signatures, and that IPRP scoring is widely applicable to various patient cohorts, treatment procedures and transcriptomic technologies. Compared to current standards for AML patient stratification, such as age or ELN2017 risk classification, we demonstrate an added prognostic value of the IPRP risk score for providing improved prediction of AML patients. Our web-tool implementation of the IPRP score and a simple 4-factor nomogram enables practical and robust risk scoring for AML patients. Even though developed for AML patients, our pan-cancer analyses demonstrate a wider application of the IPRP signature for prognostic prediction and analysis of tumor-immune interplay also in multiple solid tumors.Peer reviewe

Structural insights into Ca2+-activated long-range allosteric channel gating of RyR1

Ryanodine receptors (RyRs) are a class of giant ion channels with molecular mass over 2.2 mega-Daltons. These channels mediate calcium signaling in a variety of cells. Since more than 80% of the RyR protein is folded into the cytoplasmic assembly and the remaining residues form the transmembrane domain, it has been hypothesized that the activation and regulation of RyR channels occur through an as yet uncharacterized long-range allosteric mechanism. Here we report the characterization of a Ca2+-activated open-state RyR1 structure by cryo-electron microscopy. The structure has an overall resolution of 4.9 angstrom and a resolution of 4.2 angstrom for the core region. In comparison with the previously determined apo/closed-state structure, we observed long-range allosteric gating of the channel upon Ca2+ activation. In-depth structural analyses elucidated a novel channel-gating mechanism and a novel ion selectivity mechanism of RyR1. Our work not only provides structural insights into the molecular mechanisms of channel gating and regulation of RyRs, but also sheds light on structural basis for channel-gating and ion selectivity mechanisms for the six-transmembrane-helix cation channel family.Strategic Priority Research Program of Chinese Academy of Sciences [XDB08030202]; National Basic Research Program (973 Program); Ministry of Science & Technology of China [2012CB917200, 2014CB910700]; National Natural Science Foundation of China [31270768]; Ministry of Education of China (111 Program China)SCI(E)PubMed中国科技核心期刊(ISTIC)[email protected]; [email protected]

Expression and Prognostic Characteristics of m6A RNA Methylation Regulators in Colon Cancer

Colon cancer is a common and leading cause of death and malignancy worldwide. N6-methylation of adenosine (m6A) is the most common reversible mRNA modification in eukaryotes, and it plays a crucial role in various biological functions in vivo. Dysregulated expression and genetic changes of m6A regulators have been correlated with tumorigenesis, cancer cell proliferation, tumor microenvironment, and prognosis in cancers. This study used RNA-seq and colon cancer clinical data to explore the relationship between N6-methylation and colon cancer. Based on the seven m6A regulators related to prognosis, three molecular subgroups of colon cancer were identified. Surprisingly, we found that each subgroup had unique survival characteristics. We then identified three subtypes of tumors based on 299 m6A phenotype-related genes, and one subtype was characterized as an immunosuppressive tumor and patients in this subtype may be more suitable for immunotherapy than other subtypes. Finally, using m6A-related genes and clinical information from The Cancer Genome Atlas cohort, we constructed a prognosis model, and this model could be used to predict the prognosis of patients in clinics

Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysis

Background Colon cancer is a highly heterogeneous disease, and identifying molecular subtypes can provide insights into deregulated pathways within tumor subsets, which may lead to personalized treatment options. However, most prognostic models are based on single-pathway genes. MethodsIn this study, we aimed to identify three clinically relevant subtypes of colon cancer based on multiple signaling pathways-related genes. Integrative multi-omics analysis was used to explain the biological processes contributing to colon cancer aggressiveness, recurrence, and progression. Machine learning methods were employed to identify the subtypes and provide medication guidance for distinct subtypes using the L1000 platform. We developed a robust prognostic model (MKPC score) based on gene pairs and validated it in one internal test set and three external test sets. Risk-related genes were extracted and verified by qPCR. ResultsThree clinically relevant subtypes of colon cancer were identified based on multiple signaling pathways-related genes, which had significantly different survival state (Log-Rank test, pPeer reviewe

South Boom Boom

South Boom Boom is an attempt to survive in apnea, a continuous labor, a possibility to re-imagine the world, a way to escape captivity, to draw fugitive lines, to resist, to fight and fly, to conceive resistances, to migrate, to reinvent a catastrophe, to deform the shapes of oppression across time. This magazine aims to function as an anticolonial statement expressed from that absurd position of studying and living here as someone who both has to pay the highest tuition fee while being considered as “poor and underdeveloped”. The SOUTH BOOM BOOM project included performative conferences, which aimed to discuss the importance of dissident invisibility in art and education to explore how institutions can articulate and enact an anti-racist and anti-colonial agenda

Flame-Retarded Rigid Polyurethane Foam Composites with the Incorporation of Steel Slag/Dimelamine Pyrophosphate System: A New Strategy for Utilizing Metallurgical Solid Waste

Rigid polyurethane (RPUF) was widely used in external wall insulation materials due to its good thermal insulation performance. In this study, a series of RPUF and RPUF-R composites were prepared using steel slag (SS) and dimelamine pyrophosphate (DMPY) as flame retardants. The RPUF composites were characterized by thermogravimetric (TG), limiting oxygen index (LOI), cone calorimetry (CCT), and thermogravimetric infrared coupling (TG-FTIR). The results showed that the LOI of the RPUF-R composites with DMPY/SS loading all reached the combustible material level (22.0 vol%~27.0 vol%) and passed UL-94 V0. RPUF-3 with DMPY/SS system loading exhibited the lowest pHRR and THR values of 134.9 kW/m2 and 16.16 MJ/m2, which were 54.5% and 42.7% lower than those of unmodified RPUF, respectively. Additionally, PO· and PO2· free radicals produced by pyrolysis of DMPY could capture high energy free radicals, such as H·, O·, and OH·, produced by degradation of RPUF matrix, effectively blocking the free radical chain reaction of composite materials. The metal oxides in SS reacted with the polymetaphosphoric acid produced by the pyrolysis of DMPY in combustion. It covered the surface of the carbon layer, significantly insulating heat and mass transport in the combustion area, endowing RPUF composites with excellent fire performance. This work not only provides a novel strategy for the fabrication of high-performance RPUF composites, but also elucidates a method of utilizing metallurgical solid waste

Image_5_Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysis.tiff

BackgroundColon cancer is a highly heterogeneous disease, and identifying molecular subtypes can provide insights into deregulated pathways within tumor subsets, which may lead to personalized treatment options. However, most prognostic models are based on single-pathway genes.MethodsIn this study, we aimed to identify three clinically relevant subtypes of colon cancer based on multiple signaling pathways-related genes. Integrative multi-omics analysis was used to explain the biological processes contributing to colon cancer aggressiveness, recurrence, and progression. Machine learning methods were employed to identify the subtypes and provide medication guidance for distinct subtypes using the L1000 platform. We developed a robust prognostic model (MKPC score) based on gene pairs and validated it in one internal test set and three external test sets. Risk-related genes were extracted and verified by qPCR.ResultsThree clinically relevant subtypes of colon cancer were identified based on multiple signaling pathways-related genes, which had significantly different survival state (Log-Rank test, pConclusionIn conclusion, our study identified three clinically relevant subtypes of colon cancer and developed a robust prognostic model based on gene pairs. The developed web tool is a valuable resource for researchers and clinicians in risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.</p

Image_1_Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysis.tiff

BackgroundColon cancer is a highly heterogeneous disease, and identifying molecular subtypes can provide insights into deregulated pathways within tumor subsets, which may lead to personalized treatment options. However, most prognostic models are based on single-pathway genes.MethodsIn this study, we aimed to identify three clinically relevant subtypes of colon cancer based on multiple signaling pathways-related genes. Integrative multi-omics analysis was used to explain the biological processes contributing to colon cancer aggressiveness, recurrence, and progression. Machine learning methods were employed to identify the subtypes and provide medication guidance for distinct subtypes using the L1000 platform. We developed a robust prognostic model (MKPC score) based on gene pairs and validated it in one internal test set and three external test sets. Risk-related genes were extracted and verified by qPCR.ResultsThree clinically relevant subtypes of colon cancer were identified based on multiple signaling pathways-related genes, which had significantly different survival state (Log-Rank test, pConclusionIn conclusion, our study identified three clinically relevant subtypes of colon cancer and developed a robust prognostic model based on gene pairs. The developed web tool is a valuable resource for researchers and clinicians in risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.</p