Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 365

The DOI matches its Crossref record; 99 of the 99 references it cites were found in a registry or an index.

Cell membrane modulation as adjuvant in cancer therapy

Open access
Zalba, Sara, ten Hagen, Timo L.M.·2017·Cancer Treatment Reviews

Abstract

Cancer is a complex disease involving numerous biological processes, which can exist in parallel, can be complementary, or are engaged when needed and as such can replace each other. This redundancy in possibilities cancer cells have, are fundamental to failure of therapy. However, intrinsic features of tumor cells and tumors as a whole provide also opportunities for therapy. Here we discuss the unique and specific makeup and arrangement of cell membranes of tumor cells and how these may help treatment. Interestingly, knowledge on cell membranes and associated structures is present already for decades, while application of membrane modification and manipulation as part of cancer therapy is lagging. Recent developments of scientific tools concerning lipids and lipid metabolism, opened new and previously unknown aspects of tumor cells and indicate possible differences in lipid composition and membrane function of tumor cells compared to healthy cells. This field, coined Lipidomics, demonstrates the importance of lipid components in cell membrane in several illnesses. Important alterations in cancer, and specially in resistant cancer cells compared to normal cells, opened the door to new therapeutic strategies. Moreover, the ability to modulate membrane components and/or properties has become a reality. Here, developments in cancer-related Lipidomics and strategies to interfere specifically with cancer cell membranes and how these affect cancer treatment are discussed. We hypothesize that combination of lipid or membrane targeted strategies with available care to improve chemotherapy, radiotherapy and immunotherapy will bring the much needed change in treatment in the years to come.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.ctrv.2016.10.008

Cites

99 of 99 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Cited by

See all 365

Metadata

Authors
Zalba, S., & ten Hagen, T. L. M.
Venue
Cancer Treatment Reviews
Publisher
Elsevier BV
Published
2017-01
Volume
52
Pages
48-57
ISSN
0305-7372 (ISSN-L)
OpenAlex
W2549531301
Cited by
365
References
99
FWCI
6.67
Type
journal article
Language
en
Open access
hybrid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.