Nature Cancer Template
Write in a clean editor, then format for Nature Cancer in one click — DocuGuru applies the official Springer Nature template with superscript references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the Nature Cancer format
Nature Cancer is a peer-reviewed journal published by Springer Nature, covering Cancer Immunotherapy and Biomarkers, Cancer Genomics and Diagnostics, CAR-T cell therapy research. Over its lifetime it has published 1,191 papers, which have received 58,618 citations.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. Nature Cancer 12, 45–58 (2023).
Formats any DOI in Nature Cancer style. No sign-up. |
| Publishes research in | Cancer Immunotherapy and Biomarkers Cancer Genomics and Diagnostics CAR-T cell therapy research Immunotherapy and Immune Responses Immune Cell Function and Interaction |
| ISSN | 2662-1347 |
| Citation impact (2-yr) | 15.23 |
| h-index | 129 |
| i10-index | 588 |
| Article processing charge | $11,690 |
| Top institutions publishing here | Harvard University |
| Journal website | www.nature.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in Nature Cancer per year
Citation impact of Nature Cancer by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in Nature Cancer
Molecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environment. Routine histopathology tissue slides - which are ubiquitously available - can reflect such morphological changes. Here, we show that deep learning can consistently infer a wide range of genetic mutations, molecular tumor subtypes, gene expression signatures and standard pathology biomarkers directly…
Inferring single-cell compositions and their contributions to global gene expression changes from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology. Here we develop Bayesian cell proportion reconstruction inferred using statistical marginalization (BayesPrism), a Bayesian method to predict cellular composition and gene expression in individual cell types from bulk RNA-seq, using patient-derived, scRNA-seq…