Letter to the editor comments on ʻPBK as a Novel Biomarker with Excellent Diagnostic and Prognostic Value in HCC Associated with Immune Infiltration and Methylationʼ

Journal of Molecular Histology (2025) 56:360 https://doi.org/10.1007/s10735-025-10651-9 COMMENTARY Letter to the editor comments on ʻPBK as a Novel Biomarker with Excellent Diagnostic and Prognostic Value in HCC Associated with Immune Infiltration and Methylationʼ Guobing Wang1 · Guoying Jiang1 · Yongqiang Xu1 · Wenqi Feng1 · Gang Tian2 Received: 13 July 2025 / Accepted: 13 October 2025 / Published online: 4 November 2025 © The Author(s) 2025 Abstract This manuscript presents a letter to the editor addressing the study by Lv et al. (J Mol Histol 56(2):129, 2025), which identifies PDZ-binding kinase (PBK) as a diagnostic and prognostic biomarker for hepatocellular carcinoma (HCC). While commending the article’s comprehensive approach—including bioinformatics integration and validation of PBK's role in immune infiltration and methylation—the authors highlight three critical areas for improvement to enhance the study's rigor and clarity. First, they identify methodological issues in differential gene analysis, recommending RNA-seq-specific tools (e.g., DESeq2) over misapplied microarray frameworks like limma. Second, they critique the presentation of statistical significance, urging clearer reporting of P-values (e.g., P < 0.001) instead of P < 0.000. Third, they dispute the unrealistic survival curve in Fig. 2, suggesting evidence-based adjustments to reflect clinical plausibility. The letter emphasizes that addressing these concerns would strengthen the findings' impact on HCC research. Dear Editor, I read with great interest in the article by Lv et al. (2025), which identifies PBK as a promising biomarker for hepatocellular carcinoma (HCC) (Lv et al. 2025). The study’s strengths lie in its comprehensive approach, integrating bioinformatics, clinical correlation, and functional validation to demonstrate PBK’s diagnostic (AUC = 0.98) and prognostic significance (Lv et al. 2025). The authors elegantly link PBK’s dysregulation to methylation-driven overexpression, immune microenvironment modulation, and pro-tumorigenic behaviors in HCC cells. The authors found that PBK Guobing Wang and Guoying Jiang have contributed equally to this work and share first authorship. This comment refers to the article available online at https://doi.org/1 0.1007/s10735-024-10324-z. Wenqi Feng Gang Tian 1 Yibin Traditional Chinese Medicine Hospital, Yibin, China 2 Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China could be used as a biomarker of significant value in the diagnosis and prognosis of HCC. However, I wish to highlight three areas for improvement to enhance the study’s rigor and clarity: 1. Differential gene analysis methodology While Lv et al. used limma’s t-test framework on normalized counts, limma was originally designed for microarrays unless coupled with its voom transformation. For raw RNA-seq reads, count-based methods such as DESeq2 (Love et al. 2014) or edgeR explicitly model the mean–variance relationship and should be preferred. Additionally, the original manuscript inappropriately employed the normalizeBetweenArrays method – designed for microarray data – rather than RNA-seq-specific normalization approaches such as TMM (trimmed mean of M-values). This would mislead the readers of research in this field (Fig. 1). 2. Presentation of statistical significance The Kaplan–Meier (K-M) analysis reports P < 0.000 (Fig. 2), which may mislead readers by implying an exact zero probability. Reporting P < 0.001 or the exact P-value would align with statistical best practices and enhance transparency. We believe this was an error made by the author, but we still think it will cause confusion for the readers. 3. Validation of prognostic model in Fig. 2 13 360 Page 2 of 3 Fig. 1 The differentially expressed genes in HCC The survival curve for the high-risk group drops to 0% at 5 years (Fig. 2), an unlikely clinical scenario. Recent studies Fig. 2 Prognostic KM curves for high and low risk groups of the validation set (The prognosis of patients in the higher risk group and lower risk group validated in GSE54236 and GSE76427 datasets) 13 Journal of Molecular Histology (2025) 56:360 emphasize the importance of realistic survival curve representation (Naher et al. 2024). And revised data analysis or figure adjustments reflecting evidence-based survival trends would strengthen the model’s credibility. (The methods for for reproducing ‘Fig. 2Dʼ:The variance analysis algorithm used was DEseq2,Search GSE54236 and GSE76427 datasets, click to download the two “series matrix.txt.gz” documents and Platform annotation files. Next, the preprocessing, merging and deduplication of the two datasets will be carried out. Carry out chip batch processing and quantile normalization of columns for the two datasets. Finally, for Xiantao (https://www.helixlife.cn/) [bioinformatics tool], select [Clinical Significance], and in the right side’s [Prognosis Category], choose [Survival Curve (KM Chart)]). In summary, this study makes a significant contribution to HCC biomarker research. Addressing these methodological and presentation issues would further solidify its impact. I commend the authors for their work and look forward to future advancements in this field. Author contributions Guobing Wang and Guoying Jiang have contributed equally to this work and share first authorship. Page 3 of 3 360 Journal of Molecular Histology (2025) 56:360 Data availability No datasets were generated or analysed during the current study. Declarations Conflict of interest The authors declare no competing interests. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. References Lv B, Zhang F, Zhang X, Wang Z, Hao S, Ye N, He N (2025) PBK as a novel biomarker performed excellent diagnostic and prognostic value in HCC associated with immune infiltration and methylation. J Mol Histol 56(2):129 Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESe (...truncated)