ReviewZhongguo fei ai za zhi = Chinese journal of lung cancer2019
[Therapeutic Relevance of Human Microbiota and Lung Cancer].
Review in Zhongguo fei ai za zhi = Chinese journal of lung cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Impact of Pulmonary microbiota on lung cancer treatment-related pneumonia.Journal of Cancer · 2024Article
- Host Transcriptional Regulatory Genes and Microbiome Networks Crosstalk through Immune Receptors Establishing Normal and Tumor Multiomics Metafirm of the Oral-Gut-Lung Axis.International journal of molecular sciences · 2023Review
- Interaction between gut microbiota and tumour chemotherapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2022Review
- Distribution of microbiota in cervical preneoplasia of racially disparate populations.BMC cancer · 2022Article
- Microbiota and IPF: hidden and detected relationships.Sarcoidosis, vasculitis, and diffuse lung diseases : official journal of WASOG · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The human microbiome is closely related to human health status. Disruption of the symbiotic balance of the human microbiome is commonly found in systematic diseases such as diabetes, obesity, and chronic gastric diseases. The human microbiome confers benefits or disease susceptibility to the human body through multiple pathways, associated with approximately 20% of malignancies. The incidence and mortality of lung cancer (LC) in men in China are the highest among all malignancies, which is a serious threat to human health. Emerging evidence has suggested that the human microbiota may be closely related to lung cancer at multiple levels, e.g., by affecting metabolic, inflammatory, or immune pathways. At the same time, the human microbiota affects the efficacy of lung cancer on chemoradiotherapy, gene therapy, immunotherapy and other treatments. Immunotherapy is a promising method for the treatment of malignancies such as lung cancer, but the efficacy of immune checkpoint inhibitors in patients is heterogeneous. Preclinical studies based on lung cancer cell lines suggest that the intestinal microbiota can modulate responses to anti--PD-1 therapy through interactions with the host immune system. But for lung cancer patients, whether the intestinal flora can still regulate immunotherapy remains controversial. In this mini-review, we summarize current research findings describing therapeutic relevance of human microbiota and lung cancer. A better knowledge of the interplay between the human microbiome and lung cancer may promote the development of innovative strategies for prevention and personalized treatment in lung cancer.
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Registered trials
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.