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  1. SciScore for 10.1101/2021.05.10.443377: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    The primary antibodies used in the research were as following: anti-ACE2 (1:3000 dilution, Cat. ab15348, Abcam, Cambridge, UK)
    suggested: (Abcam Cat# ab15348, RRID:AB_301861)
    Software and Algorithms
    Public datasets retrieval: The Cancer Genome Atlas (TCGA) data: The pan-cancer normalized RNA-seq datasets, copy number variant (CNV) data processed by GISTIC algorithm, 450K methylation data, mutation profiles, the activities of transcription factor (TF) calculated by RABIT, and clinical information were obtained from UCSC Xena data portal (
    suggested: (GISTIC, RRID:SCR_000151)
    The somatic mutation data were obtained from TCGA ( and then used to calculate the tumor mutation burden (TMB) by R package “maftools”.
    suggested: (The Cancer Genome Atlas, RRID:SCR_003193)
    Prognostic analysis using PrognoScan: PrognoScan database ( was applied to assess the prognostic value of ACE2 in BRCA across a large cohort of public microarray datasets [17].
    PrognoScan: PrognoScan
    suggested: None
    The functional roles of ACE2 in BRCA was predicted using the Linked Omics tool in term of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways by the gene set enrichment analysis (GSEA).
    suggested: (KEGG, RRID:SCR_012773)
    Moreover, in order to avoid calculation errors resulted from various algorithms which were developed to explore the relative abundance of TIICs in TME, we comprehensively estimated the infiltration levels of TIICs using the following independent algorithms: TIMER [21], EPIC [22], MCP-counter [23], quanTIseq [24] and TISIDB [25].
    suggested: (TIMER, RRID:SCR_018737)
    First, BRCA-related drug-target genes were screened using the Drugbank database (
    suggested: (DrugBank, RRID:SCR_002700)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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