Evidence map›Paper›PMID 40606983›Full record

ArticleFrontiers in oncology2025

Deciphering the impact of intra-tumoral bacterial infiltration on multi-omics profiles in low-grade gliomas.

Wenshu Li, Zixiang Zhu, Longyuan Li, Xin Wu, Jiaxuan Li, Yi Zhou, Lingwen Gu, Pranathi Vittal, Zhouqing Chen, Zhong Wang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

11 authors.

Wenshu Li *Department of Pathology, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, Jiangsu, China.
Zixiang Zhu *Department of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.
Longyuan Li *Department of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.
Xin WuDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.
Jiaxuan LiDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.
Yi ZhouDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.
Lingwen GuDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.
Pranathi VittalSoochow University Medical College, Soochow University, Suzhou, China.
Zhouqing ChenDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.
Zhong WangDepartment of Neurosurgery & Brain and Nerve Research Laboratory, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.
Lingchuan GuoDepartment of Pathology, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Low-grade gliomas (LGGs) exhibit diverse bacterial infiltrations. This study delves into the intricate relationship between microbial infiltration in glioma samples and tumor multi-omics characteristics, aiming to elucidate its impact on tumor behavior and patient prognosis. Methods: We included low-grade glioma (LGG) samples from The Cancer Genome Atlas (TCGA) as analysis cohort and used LGG tumor samples from patients who underwent surgical treatment as validation cohort. For the TCGA samples, utilizing advanced machine learning algorithms, this study identified distinct patterns of bacterial infiltration within the LGG population and constructed a prognostically relevant intra-tumoral bacteria risk model (PRIBR Index). For the clinically derived samples, we performed 16S rRNA sequencing, bulk RNA sequencing, and proteomics analysis. Subsequently, the samples were stratified into high-risk and low-risk groups. We then explored clinical information, tumor microenvironment, methylation status, and sensitivity to targeted therapies between these groups to elucidate the impact of varying bacterial infiltration levels on glioma behavior. Results: A total of 32 common differentially expressed genes were identified between the TCGA-LGG samples and the clinical samples when comparing the high-risk and low-risk groups. The high-risk group demonstrated elevated bacterial infiltration levels, which were associated with increased infiltration of inflammatory factors. Patients in this group exhibited shorter survival periods, potentially attributable to the heightened expression of negative immune checkpoints. Predictive analysis for targeted drugs indicated that certain agents might achieve a lower half maximal inhibitory concentration (IC50) in the low-risk group compared to the high-risk group. Furthermore, while no significant differences were observed in tumor mutation burden or copy number variation between the two groups, the high-risk group showed increased methylation levels across multiple pathways. Conclusion: These findings offer new insights into the biological characteristics of gliomas and provide novel avenues for exploring new therapeutic approaches, bringing fresh perspectives to the field of intra-tumoral bacteria.

Indexed as

gliomaimmune checkpointintra-tumoral bacterialLGGmachine learning

Identifiers

PMID40606983
PMCPMC12213438

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