Yazhuo Zhang

Background/Objectives: Pituitary neuroendocrine tumors (PitNETs) frequently exhibit invasive behaviors that complicate clinical treatment. While cyclin-dependent kinase 5 (CDK5) and lymphokine-activated killer T-cell-originated protein kinase (PBK, also known as PDZ-binding kinase) are implicated in tumor progression, their reciprocal regulatory mechanism remains unclear. This study aims to elucidate the CDK5-PBK interaction in PitNETs and identify potential therapeutic agents targeting this pathway. Methods: We utilized proximity labeling and phospho-specific assays to characterize the CDK5 and PBK interaction in PitNET cell lines. Immunohistochemical analysis was performed on patient tumor tissues to evaluate clinical relevance. Artificial intelligence (AI)-based virtual screening was employed to discover dual-target inhibitors. The therapeutic efficacy of the identified compound, proguanil hydrochloride, was subsequently evaluated using in vitro functional assays, alongside in vivo xenograft animal models. Results: We identified a mutual phosphorylation loop between CDK5 (at S159) and PBK (at T9) that activates insulin signaling, thereby promoting cellular proliferation and invasion in PitNETs. Patient tumor analysis revealed that the co-expression of phosphorylated CDK5 (S159) and PBK (T9) significantly correlates with tumor invasiveness (p < 0.001). Through AI screening, proguanil hydrochloride was identified as a candidate dual-target inhibitor. In vitro assays confirmed that it effectively reduces tumor cell growth, while in vivo xenograft studies validated its capacity to inhibit tumor progression. Conclusions: The CDK5-PBK mutual phosphorylation axis serves as a key driver of invasiveness in PitNETs. Proguanil hydrochloride represents a promising candidate dual-target therapeutic agent capable of disrupting this pathway to suppress tumor growth.

Silent corticotroph adenomas (SCAs) represent a high-risk subtype of pituitary neuroendocrine tumors, characterized by significant clinical heterogeneity and unclear pathogenesis. The multimicrocystic sign is a typical radiological feature of SCAs; however, its biological significance remains unclear. Here, we show that the characteristics of cystic changes reflect tumor evolutionary stages and are correlated with invasiveness and proliferative activity. We performed multi-omics analysis to identify key molecular drivers and revealed that serum amyloid A1 (SAA1) is highly expressed in microcystic tumors corresponding to the bone invasion stage. Our results demonstrate that SAA1 activates the TLR4/NF-κB pathway in macrophages, promoting pro-inflammatory macrophage polarization, TNF-α and CSF-1 secretion, and osteoclast differentiation. Spatial transcriptomics confirmed a significant correlation between the proportion of SAA1+ tumor cells and that of TNF-α+ macrophages. Serum amyloid A (SAA) levels in serum exhibited strong diagnostic value for SCAs. These findings highlight the potential clinical and biological significance of cyst size in SCAs and uncover a novel mechanism by which SAA1 drives tumor bone invasiveness through macrophage-mediated osteoclast differentiation. Our findings highlight that serum SAA levels may serve as a promising biomarker for the preoperative identification of SCAs, advancing our understanding of SCA heterogeneity.

Following the publication of the above article and an expression of concern statement (doi: 10.3892/ijmm.2025.5680) after it had been drawn to the Editor's attention by an interested reader that, regarding the western blot data shown in Fig. 5 on p. 507, the first set of GAPDH bands for the GH3 cell line were strikingly similar to the EGFR protein bands shown for the GT1‑1 cell line in the adjacent set of gels, the authors have now replied to the Editorial Office to explain the apparently anomalous appearance of this figure. After having examined their original data, the authors have realized that this figure was assembled incorrectly; essentially, the wrong data were included in this figure to portray the GAPDH bands for the GH3 cell line. The revised version of Fig. 5, now showing the correct GAPDH data for the GH3 cell line, is featured on the next page. The authors can confirm that the error made during the assembly of Fig. 5 did not have a significant impact on either the results or the conclusions reported in this study, and all the authors agree with the publication of this Corrigendum. The authors are grateful to the Editor of International Journal of Molecular Medicine for allowing them the opportunity to publish this Corrigendum; furthermore, they apologize to the readership of the Journal for any inconvenience caused. [International Journal of Molecular Medicine 47: 500‑510, 2021; DOI: 10.3892/ijmm.2020.4807]

Figure S2. A. Contributions of small indel (ID) signatures (A and E) between CC1 and CC2; B. Number of mutations in small indel (ID) signatures (A and E) between CC1 and CC2.

Figure S5. SNAI1, SNAI3, and TWIST expression in CC1 and CC2 tumors.

Table S1. Genes included in the targeted DNA panel sequencing. Table S2. Differences in clinical characteristics by molecular subtype. Table S3. Distributions of mutation status in PBRM1 and SETD2 by molecular subtype in RNASeq and Nanostring datasets. Table S4. Differentially expressed genes among molecular subtypes. Table S5. GSEA results for up- and down-regulated gene sets comparing CC2 to CC1.

Figure S3. Gene expression profiling based on the NanoString panel in 23 skull-base (top) and 25 non-skull-base (bottom) chordoma patients from North America.

Figure S4. Expression of selected markers in relation to overall survival (left) and recurrence-free survival (right) in Chinese chordoma patients with long-term follow-up data. A. CLDN11; B. GLI1; C. EPHA3; D. LEF1.

Figure S1. A. PBRM1 and SETD2 expression between CC1 and CC2; B. PBRM1 and SETD2 expression between NCC1 and NCC2.