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

Meta-analysis of effects of long-term exposure to PM2.5 on C-reactive protein levels

BackgroundFine particulate matter (PM2.5) is a serious air pollutant associated with elevated levels of C-reactive protein (CRP), an inflammatory indicator.ObjectiveTo assess the potential impacts of long-term exposure to PM2.5 on CRP levels based on previous epidemiological studies.MethodsPubMed, Embase, Web of Science, CNKI, and Wanfang databases were searched to screen the cohort studies published from January 1, 2000 to January 1, 2022 on the effects of long-term exposure to PM2.5 on CRP levels. "Fine Particulate Matter", "PM2.5", "Particulate Air Pollutants", "Ambient Particulate Matter", "CRP", "C-reactive Protein", and "High Sensitivity C-reactive Protein" in English or Chinese were the key words used in the search. The percentage change in CRP level per 10 μg·m−3 increase in PM2.5 concentration in each study was extracted, followed by meta-analysis, subgroup analysis, and sensitivity analysis.ResultsA total of 1241 articles were retrieved, and 7 articles were included. Random-effects models were used to merge the included data, and it was found that the percentage of CRP level increased by 10.41% (95%CI: 2.24%-18.57%, P<0.05), when PM2.5 concentration increased by 10 μg·m−3, І2=84.2%. The subgroup analysis conducted with grouping based on the annual mean concentration of PM2.5 long-term exposure showed that the intra-group heterogeneity was significantly reduced in the <15 μg·m−3 and the 15- μg·m−3 groups, and the subgroup forest analysis showed differences between the two groups. The results of sensitivity analysis showed that there was a high degree of heterogeneity among the 7 studies, and the 2 papers with the highest annual average PM2.5 concentration were the sources of heterogeneity. The Egger test and the funnel plot indicated that no obvious publication bias was found.ConclusionLong-term exposure to PM2.5 can raise levels of CRP in human body

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&lt;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&lt;0.05), and risk-related genes were successfully verified by qPCR (t test, p&lt;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

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

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

Transcriptome Profiling of Louisiana iris Root and Identification of Genes Involved in Lead-Stress Response

Louisiana iris is tolerant to and accumulates the heavy metal lead (Pb). However, there is limited knowledge of the molecular mechanisms behind this feature. We describe the transcriptome of Louisiana iris using Illumina sequencing technology. The root transcriptome of Louisiana iris under control and Pb-stress conditions was sequenced. Overall, 525,498 transcripts representing 313,958 unigenes were assembled using the clean raw reads. Among them, 43,015 unigenes were annotated and their functions classified using the euKaryotic Orthologous Groups (KOG) database. They were divided into 25 molecular families. In the Gene Ontology (GO) database, 50,174 unigenes were categorized into three GO trees (molecular function, cellular component and biological process). After analysis of differentially expressed genes, some Pb-stress-related genes were selected, including biosynthesis genes of chelating compounds, metal transporters, transcription factors and antioxidant-related genes. This study not only lays a foundation for further studies on differential genes under Pb stress, but also facilitates the molecular breeding of Louisiana iris

Adaptive best subset selection algorithm and genetic algorithm aided ensemble learning method identified a robust severity score of COVID‐19 patients

Non peer reviewe