TO THE EDITOR
We are delighted to have read the article titled “Helicobacter pylori-induced miR-136 is a potential predictor of early-stage gastric cancer” authored by Chen et al[1] in the World Journal of Gastrointestinal Oncology. Gastric cancer (GC) remains a critical global public health challenge, with substantial disease burden and unresolved hurdles in early diagnosis. According to GLOBOCAN 2022[2], there were 969000 new GC cases and 660000 deaths worldwide in 2022, accounting for 4.9% of all new cancer cases and 6.8% of all cancer deaths globally, with a striking geographical imbalance - Asia contributed over 70% of both new cases and deaths, and China alone accounted for 37.0% of new global cases (359000 cases) and 39.4% of global deaths (260000 cases), with age-standardized incidence rate (13.7 per 100000) and mortality rate (9.4 per 100000) far exceeding the global average. Notably, despite an overall global decline in GC incidence, age and sex disparities persist and even widen[3]: A systematic analysis of young-onset GC reveals that the incidence of young-onset GC (< 40 years) showed a significant upward trend from 2015 to 2019 (annual percentage change: 1.39; 95% confidence interval: 0.06-2.74; P = 0.041), with males experiencing slower declines in incidence and mortality than females, and high socio-demographic index countries showing a more pronounced decline in late-onset GC incidence but no such trend in young-onset cases. Meanwhile, acg-119-454.pdf [focusing on GC in adolescents and young adults (15-39 years)] highlights that GC in adolescents and young adults deaths are mainly concentrated in the 35-39 years age group (accounting for approximately half of total deaths), with males having 2.3-fold higher age-standardized incidence rate (22.4 per 100000) and 2.1-fold higher age-standardized mortality rate (16.6 per 100000) than females; additionally, early diagnosis faces severe “technology-resource-population” dilemmas - high-income countries achieve over 50% early diagnosis rate via national endoscopic screening, but most low-socio-demographic index regions lack basic screening facilities (e.g., early diagnosis rate < 10% in sub-Saharan Africa), serological tests (e.g., pepsinogen detection) have only 60%-80% sensitivity, new biomarkers are not widely used, traditional screening strategies focusing on people ≥ 50 years misalign with the rising young-onset cases, and endoscopic coverage is less than 20% in rural western China (a high-burden area), further exacerbating the imbalance toward late-stage diagnosis[4].
In this study, the findings of Chen et al’s study[1] provide valuable insights and innovative perspectives for the early diagnosis and mechanistic research of Helicobacter pylori (H. pylori)-associated GC. This study is the first to systematically demonstrate that the nuclear factor kappa B (NF-κB)-microRNA-136 (miR-136)-programmed cell death protein 11 (PDCD11) axis plays a critical role in H. pylori infection-induced gastric carcinogenesis. Meanwhile, it clearly identifies H. pylori-induced miR-136 as a potential biomarker for early-stage GC, particularly in the phase of precancerous lesions of GC (PLGC), thereby offering a novel target for the early intervention of this disease. By integrating multi-level validation including clinical specimen analysis [covering non-active gastritis (NAG), chronic atrophic gastritis (CAG), PLGC, and GC tissues], in vitro cell experiments (using SGC-7901 and BGC-823 GC cell lines), and in vivo nude mouse xenograft models, the study not only clearly delineates the molecular cascade of “H. pylori infection to NF-κB activation to miR-136 upregulation to PDCD11 inhibition to GC cell proliferation and migration” but also significantly enhances the scientific rigor and credibility of its conclusions. Furthermore, a key discovery of this study is that the expression level of miR-136 increases in a gradient manner with the progression of gastric mucosal lesions (from NAG to CAG, then to PLGC, and finally to GC), and its function depends on the targeted inhibition of PDCD11. This provides direct experimental evidence for the “inflammation-carcinogenesis” pathway of GC (H. pylori infection to chronic gastric inflammation to molecular abnormalities to GC development) and the “gastric mucosal inflammation origin” hypothesis of GC[5-7]. Overall, these findings offer important theoretical support for miR-136 to serve as a diagnostic biomarker and therapeutic target for H. pylori-associated early-stage GC. Nevertheless, we have several exploratory comments regarding certain aspects of the study.
First, the study proposes the role of the NF-κB-miR-136-PDCD11 axis in H. pylori-induced gastric carcinogenesis, yet there are obvious gaps in the analysis of the molecular mechanisms underlying this axis. On the one hand, as a critical initiating step for triggering inflammation and carcinogenesis, the specific molecular events through which H. pylori activates NF-κB are not elaborated in this article. Relevant literatures have mentioned that this occurs, for example, via the effector ADP-L-glycero-β-D-manno-heptose (ADP-heptose)[8] or by activating signal transducer and activator of transcription 3 to upregulate the expression of MYB proto-oncogene like 2[9], which in turn results in NF-κB activation. These events were not explored in the study, leading to a lack of molecular-level support for the initiating link of “H. pylori infection → NF-κB activation”. On the other hand, the downstream mechanisms by which the inhibition of PDCD11 by miR-136 regulates the functions of GC cells have not been further explored: The study only confirmed that PDCD11 is a target gene of miR-136 and that the downregulation of PDCD11 is associated with enhanced proliferation and migration of GC cells. It is suggested to adopt experimental methods such as protein-protein interaction network prediction, pathway enrichment analysis of differential genes/proteins, and molecular docking simulation between PDCD11 and downstream proteins to explore the downstream mechanism of PDCD11[10]. However, as a functional protein, the specific effector molecules (e.g., regulating the G2/M checkpoint[11]) through which PDCD11 affects the malignant phenotype of GC cells have not been clarified via pathway validation experiments (such as western blot for detecting the expression of downstream proteins). This results in a break in the downstream link of “PDCD11 inhibition → abnormal GC cell function” and ultimately makes the molecular logic of the NF-κB-miR-136-PDCD11 axis insufficiently complete. Furthermore, the study did not elaborate on the process of discovering the regulatory relationship between miR-136 and NF-κB. Although experiments confirmed the presence of an NF-κB binding site in the miR-136 promoter, key information, such as the screening criteria for predicting this binding site using TargetScan 7.1 and the validation of the reliability of prediction results, is missing. This makes it difficult for readers to trace the research origin and rationality of this regulatory relationship. Furthermore, PDCD11, as an apoptotic protein, had its trafficking properties not detected in this study. PDCD11 is a member of the PDCD family and is also known as apoptosis-linked gene 4[12]. However, this study did not detect whether the NF-κB-miR-136-PDCD11 axis ultimately led to functional changes in PDCD11.
Second, the study has limitations in the in vitro model design, which may introduce bias and weaken the generalizability of the results. In terms of in vitro experimental models, the study only used two GC cell lines (SGC-7901 and BGC-823), which have obvious limitations: First, it did not cover GC cells of different differentiation degrees (e.g., well-differentiated and moderately differentiated adenocarcinoma) and different pathological subtypes (e.g., signet ring cell carcinoma and mucinous adenocarcinoma). However, GC cells with different differentiation degrees and subtypes exhibit significant differences in molecular phenotypes and responses to regulatory signals[13,14], so results from a single type of cell line cannot reflect the pathological diversity of clinical GC. The different GC cell lines can have distinct molecular profiles, and a finding in one may not generalize to others, thus necessitating the use of a panel of cell lines to make a more robust claim. Second, the study did not verify the differences in the regulation of miR-136 by different H. pylori strains - differences in the virulence of H. pylori lead to variations in its ability to induce carcinogenesis in host cells[15]. The core mechanism of H. pylori strain specificity lies in the 171S/L single-nucleotide polymorphism of its serine protease HtrA. The 171 L-type HtrA tends to form stable trimers, with proteolytic activity 3-5 times stronger than that of the 171S-type; the 171S-type HtrA mostly exists as monomers and has weak activity. The 171 L-type HtrA can efficiently cleave occludin and E-cadherin to disrupt the epithelial barrier, promote bacterial transmigration, and enhance the injection of the oncoprotein cytotoxin-associated gene A mediated by the type IV secretion system. Additionally, the 171 L-type strains can activate the NF-κB and β-catenin pathways and increase DNA damage, while the pathogenic effects of the 171S-type strains are significantly weaker[16]. Nevertheless, the study only used the standard strain 26695 and did not compare the changes in miR-136 expression and the effects on the NF-κB-miR-136-PDCD11 axis after infection with H. pylori strains of different virulence. This may result in the omission of strain-specific molecular effects and failure to fully explain the association between infections with different H. pylori strains and GC risk in clinical practice. Therefore, it is necessary to conduct research using different strains of H. pylori to draw more reliable conclusions. Additionally, several flaws exist in the labeling, completeness, and consistency of the experimental result figures in the study, which may hinder readers’ accurate understanding of the results.
The first type is “labeling issues”. Figure 2E is a immunofluorescent staining’s result figure, but it does not clearly label the cell lines (e.g., GES-1, SGC-7901, or BGC-823) corresponding to the bands in the figure, making it impossible for readers to determine the differences in PDCD11 expression among different cell lines, this lack of clear labeling in Figure 2E undermines the reproducibility of the experiment, making it difficult for other researchers to verify these crucial findings. Figure 4 does not specify the type and mechanism of action of the miR-136 inhibitor used, making it difficult to verify the specificity of the experimental intervention.
The second type is “complete issues”. Some result figures are incomplete: For the key experiment of predicting potential target genes of miR-136 (including PDCD11) using TargetScan 7.1, the prediction result figures (such as binding site diagrams and target gene enrichment analysis diagrams) were not presented, so the bioinformatics basis for confirming PDCD11 as a target gene of miR-136 cannot be intuitively verified, which makes the bioinformatics basis for confirming PDCD11 as a target gene of miR-136 lack intuitive support - readers cannot directly check the matching degree between miR-136 and PDCD11 binding sites, thus reducing the credibility of the target gene verification conclusion. To solve this, it is recommended to use TargetScan 7.1 to generate and supplement two types of figures: A schematic diagram of the binding site between miR-136 and PDCD11 mRNA 3’-untranslated region and a Gene Ontology/Kyoto Encyclopedia of Genes and Genomes enrichment analysis bubble diagram of all predicted target genes of miR-136. Figure 1D is a western blot experiment that only compared PDCD11 expression between groups, such as with H. pylori infection (Hp+) GC vs without H. pylori infection (Hp-) GC and Hp+ PLGC vs Hp- NAG, but lacked experimental data on Hp+ NAG and Hp- CAG samples. This makes it impossible to fully present the changing trend of PDCD11 expression during the progression of gastric mucosal lesions. The suggestion is to supplement western blot data of Hp+ NAG and Hp- CAG samples in Figure 1D, and add a line chart summarizing PDCD11 expression levels across all lesion stages to visualize the expression trend. Third, there are inconsistencies or unexplained contradictions between the result figures and the descriptions: The results presented in Figure 4I and Figure 5P (wound healing experiment) are visually inconsistent with the description in the article that “miR-136 inhibitor significantly inhibits the migration of GC cells”, and no supplementary explanation of quantitative data was provided. For this part of the experiment, suggest a positive control (a known migration inhibitor) and a negative control (an irrelevant small molecule) to validate the assay’s performance. Figure 1K is a tissue immunohistochemistry figure, but the magnification of sections in different groups varies, and the article did not explain the reason for the inconsistent magnification (e.g., whether it was adjusted due to the need to observe specific regions), which may affect the objective judgment of the intensity of positive signals. To unify the magnification of all sections in Figure 1K and state the magnification in the figure legend, if magnification adjustment is necessary to observe specific regions, clearly explain the reason and mark the specific observation area with a red box in the figure.
