Evidence map›Paper›PMID 35126518›Full record

ArticleJournal of oncology2022

Application Value of Serum Multi-Antibody Combined Detection in Differential Diagnosis and Typing of Lung Cancer.

Tian Cai, Weishen Yao, Fuyou Liang, Qingshui Yang, Fulang Han

Open access · hybridAbstract read
In one paragraph

Article in Journal of oncology, 2022. 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
0.1field-weighted citation impact, top 64% of its field
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, 1 citations in OpenAlex.

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

5 authors at 1 institution in 1 country.

Tian CaiNanhai District People's Hospital of Foshan, Foshan, Guangdong, China.ORCID https://orcid.org/0000-0003-1330-1075
Weishen YaoNanhai District People's Hospital of Foshan, Foshan, Guangdong, China.
Fuyou LiangNanhai District People's Hospital of Foshan, Foshan, Guangdong, China.
Qingshui YangNanhai District People's Hospital of Foshan, Foshan, Guangdong, China.
Fulang HanNanhai District People's Hospital of Foshan, Foshan, Guangdong, China.
Foshan Second People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the most prevalent malignant tumours is lung cancer. Circulating microRNAs (miRNAs) have shown to have significant promise for lung cancer diagnosis and prognosis, according to a growing body of research. The researchers wanted to explore if serum exosomal miR-1246 has any treatment significance in patients with non-small-cell lung cancer (NON-SCLC). Real-time PCR was used to determine the stage of exosomal miR-1246 serum expression in NON-SCLC patients. The researchers next looked into the link regarding exosomal miR-1246 serum stages and NON-SCLC prognosis. In NON-SCLC patients, exosomal miR-1246 serum appearance was considerably higher. According to a receiver operating characteristic (ROC) research, serum exosomal miR-1246 was effective in discriminating NON-SCLC patients from normal controls and non-malignant respiratory illness patients. Following treatment, the amount of serum exosomal miR-1246 reduced but increased in cases of recurrence. Furthermore, the level of serum exosomal miR-1246 was connected to distant metastases and TNM stages in a significant way. According to a survival analysis, cases with severe levels of exosomal miR-1246 serum had reduced overall or disease-free survival. The level of exosomal miR-1246 serum was found to be an autonomous predictive issue for NON-SCLC in multi-variate analysis. Finally, exosomal miR-1246 serum may be a useful prognosis biomarker for non-small-cell lung cancer.

Identifiers

PMID35126518
PMCPMC8816536
OpenAlexW4210686306

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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.