Evidence map›Paper›PMID 16193335›Full record

ArticleCancer immunology, immunotherapy : CII2006

Serological identification and bioinformatics analysis of immunogenic antigens in multiple myeloma.

F L Zhou, W G Zhang, G Chen, W H Zhao, X M Cao, Y X Chen, W Tian, J Liu, S H Liu

Abstract read
In one paragraph

Article in Cancer immunology, immunotherapy : CII, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

F L ZhouDepartment of Hematology, The Second Hospital, School of Medicine, Xi'an Jiaotong University, The west five road, No. 157, Xi'an 710004, China.
W G Zhang
G Chen
W H Zhao
X M Cao
Y X Chen
W Tian
J Liu
S H Liu

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying appropriate tumor antigens is critical to the development of successful specific cancer immunotherapy. Serological analysis of tumor antigens by a recombinant cDNA expression library (SEREX) allows the systematic cloning of tumor antigens recognized by the spontaneous autoantibody repertoire of cancer patients. We applied SEREX to the cDNA expression library of cell line HMy2, which led to the isolation of six known characterized genes and 12 novel genes. Known genes, including ring finger protein 167, KLF10, TPT1, p02 protein, cDNA FLJ46859 fis, and DNMT1, were related to the development of different tumors. Bioinformatics was performed to predict 12 novel MMSA (multiple myeloma special antigen) genes. The prediction of tumor antigens provides potential targets for the immunotherapy of patients with multiple myeloma (MM) and help in the understanding of carcinogenesis. Crude lysate ELISA methodology indicated that the optical density value of MMSA-3 and MMSA-7 were significantly higher in MM patients than in healthy donors. Furthermore, SYBR Green real-time PCR showed that MMSA-1 presented with a high number of copy messages in MM. In summary, the antigens identified in this study may be potential candidates for diagnosis and targets for immunotherapy in MM.

Indexed as

Computational BiologyAntigens, NeoplasmBase SequenceCell Line, TumorCloning, MolecularEnzyme-Linked Immunosorbent AssayGene LibraryHumansMolecular Sequence DataMultiple MyelomaReverse Transcriptase Polymerase Chain ReactionTumor Protein, Translationally-Controlled 1Antigens, NeoplasmTPT1 protein, humanTumor Protein, Translationally-Controlled 1

Identifiers

PMID16193335
PMCPMC11030602

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.