Construction of stomach adenocarcinoma prognostic signature based on anoikis-related lncRNAs and clinical significance

ABSTRACT

As a dominant type of gastric cancer, stomach adenocarcinoma (STAD) is characterized by high morbidity and mortality rates. Anoikis factors participate in tumor metastasis and invasion. This study was designed to identify prognostic risk factors in anoikis-related long non-coding RNAs (lncRNAs) for STAD. First, with STAD expression datasets and anoikis-related gene sets downloaded from public databases, anoikis-related prognostic lncRNA signatures (AC091057.1, ADAMTS9.AS1, AC090825.1, AC084880.3, EMX2OS, HHIP.AS1, AC016583.2, EDIL3.DT, DIRC1, LINC01614, and AC103702.2) were screened by Cox regression to establish a prognostic risk model. Kaplan–Meier and receiver operating characteristic curves were used to evaluate the survival status of patients and verify predictive accuracy of the model. Besides, risk score could be an independent prognostic factor to assess the prognosis of STAD patients. Nomograms of the prognostic model that combined clinical information and risk score could effectively predict survival of STAD patients, as validated by calibration curve. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses were performed for differentially expressed genes (DEGs) in high- and low-risk groups. These DEGs were related to neurotransmitter transmission, signal transmission, and endocytosis. Moreover, we analyzed immune status of different risk groups and found that STAD patients in low-risk group were more sensitive to immunotherapy. A prognostic risk assessment model for STAD using anoikis-related lncRNA genes was constructed here, which was proven to have high predictive accuracy and thus could offer a reference for prognostic evaluation and clinical treatment of STAD patients.

KEYWORDS:

• anoikis
• gastric adenocarcinoma
• lncRNA
• prognosis
• immunity

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contribution

Conceptualization: Lina Lu, Guofa Jiang

Data curation: Min Yu

Formal Analysis: Wei Huang

Acquisition: Wei Huang

Investigation: Hui Chen

Methodology: Hui Chen

Project administration: Guofa Jiang

Resources: Guofa Jiang

Software: Guofa Jiang

Supervision: Lina Lu

Validation: Gangxiu Li

Visualization: Gangxiu Li

Writing – original draft: Lina Lu, Min Yu

Writing – review & editing: All authors

Declaration of Conflicting Interests

The authors report no conflict of interest.

Ethics approval and consent to participate

Not applicable.

Data availability statement

The datasets generated and analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/19932820.2023.2220153.

Additional information

Funding

This study received no funding.