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Review
. 2023 Jul 28;13(8):1201.
doi: 10.3390/jpm13081201.

Personalized Medicine in Cancer Pain Management

Mohammad Raad  1 , William Omar Contreras López  2   3 , Alireza Sharafshah  4 , Marjan Assefi  5 , Kai-Uwe Lewandrowski  6   7   8
Affiliations
Review

Personalized Medicine in Cancer Pain Management

Mohammad Raad et al. J Pers Med. .

Abstract

Background: Previous studies have documented pain as an important concern for quality of life (QoL) and one of the most challenging manifestations for cancer patients. Thus, cancer pain management (CPM) plays a key role in treating pain related to cancer. The aim of this systematic review was to investigate CPM, with an emphasis on personalized medicine, and introduce new pharmacogenomics-based procedures for detecting and treating cancer pain patients.

Methods: This study systematically reviewed PubMed from 1990 to 2023 using keywords such as cancer, pain, and personalized medicine. A total of 597 publications were found, and after multiple filtering processes, 75 papers were included. In silico analyses were performed using the GeneCards, STRING-MODEL, miRTargetLink2, and PharmGKB databases.

Results: The results reveal that recent reports have mainly focused on personalized medicine strategies for CPM, and pharmacogenomics-based data are rapidly being introduced. The literature review of the 75 highly relevant publications, combined with the bioinformatics results, identified a list of 57 evidence-based genes as the primary gene list for further personalized medicine approaches. The most frequently mentioned genes were CYP2D6, COMT, and OPRM1. Moreover, among the 127 variants identified through both the literature review and data mining in the PharmGKB database, 21 variants remain as potential candidates for whole-exome sequencing (WES) analysis. Interestingly, hsa-miR-34a-5p and hsa-miR-146a-5p were suggested as putative circulating biomarkers for cancer pain prognosis and diagnosis.

Conclusions: In conclusion, this study highlights personalized medicine as the most promising strategy in CPM, utilizing pharmacogenomics-based approaches to alleviate cancer pain.

Keywords: cancer; cancer pain management; pain; personalized medicine; pharmacogenomics; variant.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
PRISMA flow diagram for systematic reviews. Reasons 1 and 2 were a lack of covering cancer pain management (CPM) and reports with no novel data.
Figure 2
Figure 2
Networks of string model (A) and concentric model by miRTargetLink2 (B) of first-level genes common in the pain and cancer reports obtained from GeneCards.
Figure 3
Figure 3
Networks found in the second-level analyses of 46 genes involved in cancer pain management (CPM) extracted from the review literature, represented as a string model (A) and concentric model (B).
Figure 4
Figure 4
In silico findings of third-level investigation resulting from the combination of first and second levels, displayed in a string model, including 57 completely connected genes (A), and gene–miRNA interactions in a concentric model (B).
See this image and copyright information in PMC

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