ArticleAmerican journal of cancer research2024
Integrative analysis of autophagy-related genes reveals that CAPNS1 is a novel prognostic biomarker and promotes the malignancy of melanoma via Notch signaling pathway.
Article in American journal of cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Autophagy and mitophagy in dermatological disease: a comprehensive review from molecular pathways to therapeutic frontiers.Biology direct · 2025Review
- Proteomic Analysis of FFPE Tissue Samples Identifies Potential Molecular Mechanisms Mediating Resistance to Radiotherapy in Rectal Cancer.Journal of proteome research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Skin cutaneous melanoma (SKCM) is a highly fatal form of skin cancer that develops from the malignant transformation of epidermal melanocytes. There is substantial evidence linking autophagy to cancer etiology and immunotherapy efficacy. This study aimed to conduct a comprehensive analysis of autophagy-related genes (ARGs) using TCGA datasets and further explore the potential function of critical ARGs in SKCM progression. We performed comprehensive bioinformatics analysis uses the TCGA dataset. RT-PCR was applied to examine the expression of CAPNS1 in SKCM cells. Lost-of-function experiments were performed to detect the expression of the related proteins. In this search, we screed 70 differentially expressed autophagy-related genes (DE-ARGs), including 33 up-DE-ARGs and 37 down-DE-ARGs. Enrichment assays revealed that these 70 DE-ARGs may exert influence on critical cellular processes such as autophagy, protein kinase activity, and signaling pathways, impacting cell growth, differentiation, survival, and tumor development. Then, we further explore the prognostic value of 70 DE-ARGs and confirmed 18 survival-related DE-ARGs in SKCM patients. Nearly all the 18 DE-ARGs' methylation was negatively correlated with their corresponding expression in SKCM. The 12 survival-related DE-ARGs were used to develop a unique predictive model that effectively classified SKCM patients into high- and low-risk groups with regard to overall survival. Furthermore, tumor environment analysis indicated that the risk score was associated with several immune cells. Among the 12 survival-related DE-ARGs, our attention focused on CAPNS1 which was highly expressed in SKCM patients and predicted a poor prognosis. In addition, we confirmed that knockdown of CAPNS1 distinctly suppressed the proliferation, metastasis and EMT of SKCM cells, and promoted autophagy via regulating Notch signaling pathway. Overall, this study enhances our understanding of the intricate molecular landscape of SKCM progression and presents promising avenues for future research and clinical applications.
Indexed as
Identifiers
What OpenQuestion holds
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.