Evidence map›Paper›PMID 42671675›Full record

ReviewBioresources and bioprocessing2026

Improving recombinant protein productivity in CHO cells via multi-omics data integration.

Yuan Shen, Lei Shi, Xi Zhang, Xiao Guo, Wei-Hua Dong, Tian-Yun Wang

Abstract readReview
In one paragraph

Review in Bioresources and bioprocessing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Yuan ShenSchool of Pharmacy, Henan Medical University, Xinxiang, 453003, People's Republic of China.
Lei ShiSchool of Basic Medical Sciences, Henan Medical University, Xinxiang, 453003, People's Republic of China. doudou246@163.com.ORCID http://orcid.org/0000-0001-5243-4191
Xi ZhangSchool of Pharmacy, Henan Medical University, Xinxiang, 453003, People's Republic of China.
Xiao GuoHenan Higher Medical Education Development Research Center, Henan Medical University, Xinxiang, 453003, People's Republic of China.
Wei-Hua DongSchool of Basic Medical Sciences, Henan Medical University, Xinxiang, 453003, People's Republic of China.
Tian-Yun WangHenan Key Laboratory of Neurorestoratology and Protein Modification, Henan Medical University, Xinxiang, 453003, China. wtianyuncn@126.com.ORCID http://orcid.org/0000-0002-0793-1006

Funding

National Natural Science Foundation of China U23A20270Project of Technology Innovation Leading Talent in Central Plain 234200510003Science and Technology Research and Development Plan Joint Fund (Discipline) of Henan Province 252103810373
6 · The paper itself

Abstract

Chinese hamster ovary (CHO) cells represent the dominant host system for the production of recombinant therapeutic proteins. In recent decades, extensive research has focused on process/media optimization and cell line engineering to improve both the productivity and quality of biopharmaceutical proteins produced in CHO cells. Nevertheless, the inherent complexity of biological pathways and the heterogeneous cellular responses to different environmental conditions have posed substantial challenges to traditional methodologies. Recent advances in omics technologies have enabled comprehensive characterization of CHO cell physiology, providing multidimensional molecular and phenotypic insights that facilitate the enhancement of recombinant protein production. This review first summarizes the methodologies and advances in CHO omics research, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics. It then examines contemporary approaches to integrate and analyze multi-omics data in CHO cells. The review further elucidates how these multi-omics datasets can be strategically applied across various developmental stages, including cell line selection, genetic engineering, expression vector design, and bioprocess optimization. Finally, we explore the transformative potential of integrating multi-omics with artificial intelligence and discuss promising future research directions in CHO cell studies. These emerging paradigms offer novel opportunities for data-driven cell engineering and bioprocess optimization in CHO-based biomanufacturing.

Indexed as

BioprocessingCell engineeringCHO cellsOmicsProcess optimization

Identifiers

PMID42671675
PMCPMC13529640

What OpenQuestion holds

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Registered trials

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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.