bims-netuvo 2025-08-24 papers

Front Mol Biosci. 2025 ;12 1617787

Prognostic value of positive lymph node ratio, tumor deposit, and perineural invasion in advanced colorectal signet-ring cell carcinoma.

Liang Chu, Han Wang, Tao Ling, Shuhan Feng, Yucheng Ding, Yan Zhang, Ying Pan, Cenzhu Wang, Xiaohong Wang, Lei Liu.

Background: The aim of this study was to assess the prognostic significance of positive lymph node ratio (LNR), tumor deposits (TD), and perineural invasion (PNI) in advanced colorectal signet-ring cell carcinoma (SRCC).
Methods: A multicenter retrospective cohort analysis was conducted involving 677 patients with advanced colorectal SRCC. The associations of variables with CSS and OS were analyzed using the Kaplan-Meier method and multivariable Cox proportional hazards models. A nomogram model was developed to predict outcomes.
Results: High-LNR, TD-positive, and PNI-positive were associated with poorer CSS and OS in both the training and validation cohorts. Multivariate Cox analysis identified T stage, M stage, TD, CEA, chemotherapy, and LNR as independent prognostic factors. A prognostic nomogram model incorporating these variables demonstrated excellent calibration and satisfactory predictive accuracy. Survival curves generated from individualized nomogram scores effectively discriminated prognostic outcomes (P < 0.001). The combined variable of LNR, TD, and PNI significantly enhanced the predictive performance. Specifically, the combined variable exhibited the highest relative contribution to OS at 23.4%, surpassing that of T and M stages. For CSS, its relative contribution was 21.4%, ranking second only to T and M stages.
Conclusion: LNR, TD, and PNI served as prognostic factors for advanced colorectal SRCC. The combined analysis demonstrated a higher prognostic predictive value.

Keywords: colorectal signet ring cell carcinoma; lymph node ratio; perineural invasion; prognosis; tumor deposit

DOI: https://doi.org/10.3389/fmolb.2025.1617787

Proc Natl Acad Sci U S A. 2025 Aug 26. 122(34): e2503779122

Unraveling the neuroimmune mechanisms in cancer-induced bone pain: New horizons for therapeutic intervention of the two-phase paradigm.

Changming Wang, Haiwang Ji, Hanwen Wang, Ziyun Chen, Lan Zhou, Yan Yang, Yucui Jiang, Guang Yu, Ling Jiang, Zongxiang Tang.

Cancer-induced bone pain (CIBP) is a severely painful condition that profoundly impacts patients' quality of life. However, the neuroimmune mechanisms underlying CIBP remain largely elusive. Substance P (SP), which is known to play a pivotal role in pain perception, became the focal point of our study. To this end, we adopted a comprehensive approach combining behavioral and physiological methods to investigate its role in neuroimmune interactions in CIBP. The results showed that SP released by dorsal root ganglion (DRG) neurons via exocytosis initiates CIBP, with its release peaking on the 14th day and correlating with pain behavior. Macrophages were found to infiltrate the DRGs and the sciatic nerves. Notably, in mice with CIBP, the population of macrophage type I was significantly augmented. Significantly, we found that the deletion of macrophages led to a notable alleviation of CIBP, while the blockade of the SP-neurokinin 1 receptor pathway effectively mitigated the infiltration of macrophages and alleviated CIBP. In the advanced phase, DRGs released C-C Motif Chemokine Ligand 3 and C-C Motif Chemokine Ligand 2 to recruit macrophages. A two-phase model for CIBP progression in mice was proposed, with SP-induced macrophage infiltration in the primary phase and chemokine-mediated macrophage recruitment in the advanced phase. Our investigation has unearthed a previously unrecognized mechanism governing the neuroimmune interaction in CIBP, which highlights a critical target for impeding the progression of this debilitating pain, potentially opening up broad avenues for the development of effective therapeutic interventions at different stages of CIBP with cancer development.

Keywords: DRG; SP; cancer-induced bone pain; macrophage; recruitment

DOI: https://doi.org/10.1073/pnas.2503779122

BMC Microbiol. 2025 Aug 16. 25(1): 514

Exploring the gut microbiota associated with peripheral nerve invasion in colorectal cancer patients and constructing predictive models.

Chuanbin Chen, Qingmin Chen, Shenghai Liu, Guoxi Li, Jiawei Zhao, Jingting Huang, Tianyi Ye, Xinting Yang, Zigui Huang, Zhen Wang, Fuhai He, Mingjian Qin, Chenyan Long, Binzhe Tang, Yongqi Huang, Weizhong Tang, Jungang Liu, Xiaoliang Huang.

OBJECTIVE: This study aims to explore the differences in composition, abundance, and biological functions of the gut microbiota between colorectal cancer (CRC) patients with peripheral nerve invasion (PNI) and those without peripheral nerve invasion (NPNI). Additionally, we tried to construct a machine-learning predictive model incorporating the identified microbiota characteristics to explore the impact of gut microbiota on CRC-PNI progression and to search for new non-invasive microbiological indicators for CRC-PNI. Finally, we successfully developed a predictive model to predict PNI in CRC patients through leveraging microbial biomarkers. This innovative approach is expected to offer a novel strategy for the early detection of CRC metastasis, thereby facilitating more informed decisions regarding treatment options.
METHOD: This study included 132 colorectal cancer (CRC) patients, who were divided into two separate groups according to whether they exhibited PNI. The gut microbiota of these participants were subjected to 16S rRNA gene sequencing, followed by a thorough analysis to identify any significant differences between the groups. We applied a cell sorting algorithm to convert the transcriptome sequencing data obtained from 8 colorectal cancer patients into a matrix representing immune cell abundance. Following this, the matrix was utilized to investigate the associations among the PNI-related distinct gut microbiota, immune cells, and immune-related genes, and PNI-related differentially expressed genes (or molecular markers, pathways), as well as their associations with KEGG pathways. Based on the differential gut microbiota, we constructed Random Forest (RF) and Multilayer Perceptron (MLP) models to predict PNI in CRC patients.
RESULT: Comparative analysis of α-diversity and β-diversity in the gut microbiota of CRC patients with and without PNI revealed no statistically significant differences (P > 0.05). However, Linear Discriminant Analysis effect size (LEfSe) identified 35 distinct gut microbiota, with 28 species enriched in the PNI group and 7 species significantly enriched in the NPNI group. By analyzing the gut microbiota significantly associated with PNI, we successfully constructed predictive models using RF and MLP that can predict the occurrence of PNI in CRC patients. Both models have demonstrated robust performance.
CONCLUSIONS: In the PNI and NPNI groups, 35 gut microbiota species exhibited significant variations in abundance. The differential intestinal microbiota associated with PNI in colorectal cancer may modulate the neuroinvasion process via a variety of potential biological mechanisms. The RF and MLP predictive models show considerable accuracy in predicting CRC-PNI status and are of reference value.

Keywords: 16S rRNA; Colorectal cancer; Gut microbiota; Machine learning; PNI

DOI: https://doi.org/10.1186/s12866-025-04179-x