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

IF 3.9 3区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Frontiers in Molecular Biosciences Pub Date : 2025-08-01 eCollection Date: 2025-01-01 DOI:10.3389/fmolb.2025.1617787
Liang Chu, Han Wang, Tao Ling, Shuhan Feng, Yucheng Ding, Yan Zhang, Ying Pan, Cenzhu Wang, Xiaohong Wang, Lei Liu
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引用次数: 0

Abstract

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.

淋巴结阳性比例、肿瘤沉积及周围神经浸润对晚期结直肠印戒细胞癌的预后价值。
背景:本研究的目的是评估淋巴结阳性比率(LNR)、肿瘤沉积(TD)和神经周围浸润(PNI)在晚期结直肠癌印戒细胞癌(SRCC)中的预后意义。方法:对677例晚期结直肠癌患者进行多中心回顾性队列分析。采用Kaplan-Meier法和多变量Cox比例风险模型分析各变量与CSS和OS的相关性。采用nomogram模型来预测结果。结果:在训练组和验证组中,高lnr、td阳性和pni阳性与较差的CSS和OS相关。多因素Cox分析发现T期、M期、TD、CEA、化疗和LNR是独立的预后因素。结合这些变量的预测模态图模型显示了良好的校准和令人满意的预测精度。由个体化nomogram评分生成的生存曲线能够有效地区分预后结果(P < 0.001)。LNR、TD和PNI的组合变量显著提高了预测性能。具体而言,联合变量对OS的相对贡献最高,为23.4%,超过了T期和M期。CSS的相对贡献率为21.4%,仅次于T和M阶段。结论:LNR、TD和PNI是晚期结直肠癌的预后因素。联合分析显示有较高的预后预测价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Molecular Biosciences
Frontiers in Molecular Biosciences Biochemistry, Genetics and Molecular Biology-Biochemistry
CiteScore
7.20
自引率
4.00%
发文量
1361
审稿时长
14 weeks
期刊介绍: Much of contemporary investigation in the life sciences is devoted to the molecular-scale understanding of the relationships between genes and the environment — in particular, dynamic alterations in the levels, modifications, and interactions of cellular effectors, including proteins. Frontiers in Molecular Biosciences offers an international publication platform for basic as well as applied research; we encourage contributions spanning both established and emerging areas of biology. To this end, the journal draws from empirical disciplines such as structural biology, enzymology, biochemistry, and biophysics, capitalizing as well on the technological advancements that have enabled metabolomics and proteomics measurements in massively parallel throughput, and the development of robust and innovative computational biology strategies. We also recognize influences from medicine and technology, welcoming studies in molecular genetics, molecular diagnostics and therapeutics, and nanotechnology. Our ultimate objective is the comprehensive illustration of the molecular mechanisms regulating proteins, nucleic acids, carbohydrates, lipids, and small metabolites in organisms across all branches of life. In addition to interesting new findings, techniques, and applications, Frontiers in Molecular Biosciences will consider new testable hypotheses to inspire different perspectives and stimulate scientific dialogue. The integration of in silico, in vitro, and in vivo approaches will benefit endeavors across all domains of the life sciences.
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