An exploratory study on TCM syndrome differentiation in preoperative patients with colorectal cancer assisted by laboratory indicators

Acute radiation skin injury in stage III-IV head and neck cancer: scale correlates and predictive model

PURPOSE

Active radiation skin injury (ARSI) has the highest incidence of acute adverse reactions caused by radiotherapy (RT) in patients with head and neck cancer (HNC). This study aimed to screen risk factors that can facilitate the identification of HNC patients at high risk of ARSI.

METHODS

Data from 255 stage III-IV HNC patients who underwent intensity-modulated radiation therapy (IMRT) were collected. The data from our medical records, including clinical characteristics and hematological indices before RT, were retrospectively collected and arranged. The Common Terminology Criteria for Adverse Events Criteria (CTCAE), Radiation Therapy Oncology Group Criteria (RTOG), World Health Organization Criteria (WHO), Oncology Nursing Society (ONS), Acute Radiation Dermatitis Graduation Scale, Douglas & Fowler and Radiation Dermatitis Severity Scale (RDSS) were used to assess ARSI. Of these, CTCAE was used for further analysis. Binary logistic regression analyses were used to identity risk factors. To establish the correction between each risk factor and the ARSI score, the odds ratio (OR) and 95% confidence interval (CI) were computed.

RESULTS

The assessment results of the CTCAE with RTOG, WHO, ONS, Graduation Scale, Douglas & Fowler and RDSS have good consistency. After radiotherapy, 18.4% of patients had at least 3 (3 +) grade ARSI. Multivariate logistic regression analysis revealed that the KPS score, blood glucose level, white blood cell count, and plasma free thyroxine (FT4) concentration were independent risk factors for 3 + grade ARSI. A nomogram was constructed on the basis of these risk factors, which demonstrated good predictive power according to the area under the ROC curve (AUC). The satisfactory consistency and clinical efficacy of the nomogram were confirmed by calibration curves and decision curve analysis (DCA).

CONCLUSION

A low KPS score, high blood glucose level, high white blood cell count, and high thyroid hormone prior to radiotherapy for stage III-IV HNC are independent risk factors for grade 3 + RSI.

Targeting key RNA methylation enzymes to improve the outcome of colorectal cancer chemotherapy (Review)

RNA methylation modifications are closely linked to tumor development, migration, invasion and responses to various therapies. Recent studies have shown notable advancements regarding the roles of RNA methylation in tumor immunotherapy, the tumor microenvironment and metabolic reprogramming. However, research on the association between tumor chemoresistance and N6‑methyladenosine (m6A) methyltransferases in specific cancer types is still scarce. Colorectal cancer (CRC) is among the most common gastrointestinal cancers worldwide. Conventional chemotherapy remains the predominant treatment modality for CRC and chemotherapy resistance is the primary cause of treatment failure. The expression levels of m6A methyltransferases, including methyltransferase‑like 3 (METTL3), METTL14 and METTL16, in CRC tissue samples are associated with patients' clinical outcomes and chemotherapy efficacy. Natural pharmaceutical ingredients, such as quercetin, have the potential to act as METTL3 inhibitors to combat chemotherapy resistance in patients with CRC. The present review discussed the various roles of different types of key RNA methylation enzymes in the development of CRC, focusing on the mechanisms associated with chemotherapy resistance. The progress in the development of certain inhibitors is also listed. The potential of using natural remedies to develop antitumor medications that target m6A methylation is also outlined.

A radiomics approach based on MR imaging for classification of deficiency and excess syndrome of traditional Chinese medicine in prostate cancer

Objective

To explore the potential imaging biomarkers for predicting Traditional Chinese medicine (TCM) deficiency and excess syndrome in prostate cancer (PCa) patients by radiomics approach based on MR imaging.

Methods

A total of 121 PCa patients from 2 centers were divided into 1 training cohort with 84 PCa patients and 1 validation cohort with 37 PCa patients. The PCa patients were divided into deficiency and excess syndrome group according to TCM syndrome differentiation. Radiomic features were extracted from T2-weighted imaging (T2WI), diffusion-weighted imaging and apparent diffusion coefficient images originated from diffusion-weighted imaging. A radiomic signature was constructed after reduction of dimension in training group by the minimum redundancy maximum relevance and the least absolute shrinkage and selection operator. The performance of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve.

Results

The radiomic scores of PCa with TCM excess syndrome group were statistically higher than those of PCa with TCM deficiency syndrome group among T2WI, diffusion-weighted imaging and apparent diffusion coefficient imaging models. The area under ROC curves for T2WI, diffusion-weighted imaging and apparent diffusion coefficient imaging models were 0.824, 0.824, 0.847 in the training cohort and 0.759, 0.750, 0.809 in the validation cohort, respectively. The apparent diffusion coefficient imaging model had the best discrimination in separating patients with TCM excess syndrome and deficiency syndrome, and its accuracy was 0.788, 0.778 in the training and validation cohort, respectively. The calibration curve demonstrated that there was a high consistency between the prediction of radiomic scores and the actual classification of TCM's deficiency and excess syndrome in PCa.

Conclusion

The radiomic signature based on MR imaging can be performed as a non-invasive, potential approach to discriminate TCM deficiency syndrome from excess syndrome in PCa, in which apparent diffusion coefficient imaging model has the best diagnostic efficiency.

TCM syndrome differentiation in colorectal cancer patients assisted by differences in gut microbiota: An exploratory study

Objective

To explore the difference in gut microbiota between different traditional Chinese Medicine (TCM) syndromes in patients with colorectal cancer (CRC) and its internal relationship.

Methods

From June 2020 to August 2021, 109 colorectal cancer patients with a clear pathological diagnosis who had not yet undergone surgery or chemotherapy were classified according to the TCM syndrome classification, and the feces samples of 109 patients with preoperative colorectal cancer were collected. 16s rRNA gene sequencing was used to determine gut microbiota diversity and abundance in CRC patients with different TCM syndrome, and LEfSe analysis was made to screen different TCM syndrome for differential representative microbiota.

Results

109 patients were divided into 5 syndromes by TCM syndrome classification, which were Liver and Kidney Yin Deficiency Syndrome (LKYDS, n = 19), Spleen Deficient Qi Stagnation Syndrome (SDQSS, n = 30), Stasis and Poison Obstruction Syndrome (SPOS, n = 17), Damp-Heat Syndrome (DHS, n = 30), Qi and Blood Deficiency Syndrome (QBDS, n = 13). Alpha diversity index showed significant differences among the five groups of TCM syndromes, with Shannon index being highest in the SDQSS group and lowest in the LKYDS (p = 0.003). ACE index being highest in the SDQSS group and lowest in the SPOS (p = 0.010). PD whole tree index being highest in the SDQSS group and lowest in the SPOS (p = 0.017). Similarly, beta diversity showed significant differences among the five groups of TCM syndromes, with principal coordinate analysis (PCo1 = 31.86 %, PCo2 = 5.62 %) showing separation and coincidence between the groups, and Adonis group differences showing coincidence between the QBDS-LKYDS (p = 0.702), QBDS-DHS (p = 0.133), and SDQSS-DHS (p = 0.260) groups. LEfSe analysis revealed that the representative microbiota of DHS patients was Dialister sp Marseille P5638 (LDA = 3.05, p＜0.001), the representative microbiota of SPOS patients was Oscillospirales (LDA = 4.78, p = 0.029), the representative microbiota of SDQSS patients was Selenomonadaceae (LDA = 3.94, p = 0.003), the representative microbiota of LKYDS patients was Dialister (LDA = 4.19, p = 0.001), and the representative microbiota of QBDS patients was Akkermansia muciniphila (LDA = 4.23, p = 0.006).

Conclusions

There are significant differences in gut microbiota between different TCM syndromes in CRC patients. The five microbiota, Dialister sp Marseille P5638, Oscillospirales, Selenomonadaceae, Dialister, and Akkermansia muciniphila, may be differential markers of TCM syndrome in CRC and are expected to be one of the bases for accurate TCM syndrome differentiation of CRC.

Research progress on the biological basis of Traditional Chinese Medicine syndromes of gastrointestinal cancers

Gastrointestinal cancers account for 11.6 % of all cancers, and are the second most frequently diagnosed type of cancer worldwide. Traditional Chinese medicine (TCM), together with Western medicine or alone, has unique advantages for the prevention and treatment of cancers, including gastrointestinal cancers. Syndrome differentiation and treatment are basic characteristics of the theoretical system of TCM. TCM syndromes are the result of the differentiation of the syndrome and the basis of treatment. Genomics, transcriptomics, proteomics, metabolomics, intestinal microbiota, and serology, generated around the central law, are used to study the biological basis of TCM syndromes in gastrointestinal cancers. This review summarizes current research on the biological basis of TCM syndrome in gastrointestinal cancers and provides useful references for future research on TCM syndrome in gastrointestinal cancers.

Network pharmacology and molecular docking-based analyses to predict the potential mechanism of Huangqin decoction in treating colorectal cancer

BACKGROUND

To analyze the potential action mechanism of Huangqin decoction (HQD) in colorectal cancer (CRC) treatment on the basis of network pharmacology and molecular docking.

AIM

To investigate the molecular mechanisms of HQD for CRC treatment by using network pharmacology and molecular docking.

METHODS

All HQD active ingredients were searched using the Systematic Pharmacology and Traditional Chinese Medicine Systems Pharmacology databases and the Bioinformatics Analysis Tool for Molecular Mechanisms in traditional Chinese medicine. Then, the targets of the active ingredients were screened. The abbreviations of protein targets were obtained from the UniProt database. A “drug–compound–target” network was constructed to screen for some main active ingredients. Some targets related to the therapeutic effect of CRC were obtained from the GeneCards, DisGeNET, Therapeutic Target Database, and Online Mendelian Inheritance in Man databases. The intersection of targets of Chinese herbs and CRC was taken. A Venn diagram was drawn to construct the intersection target interactions network by referring to the STRING database. Topological analysis of the protein interaction network was performed using Cytoscape 3.7.2 software to screen the core HQD targets for CRC. The core targets were imported into the DAVID 6.8 analysis website for gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses and visualization. Finally, molecular docking was performed using AutoDockTool and PyMOL for validation.

RESULTS

In total, 280 potential drug-active ingredients were present in HQD, including 1474 targets of the drug-active ingredients. The main active ingredients identified were betulin, tetrahydropalmatine, and quercetin. In total, 10249 CRC-related targets and 1014 drug-disease intersecting targets were identified, including 28 core targets of action such as Jun proto-oncogene, AP-1 transcription factor subunit, signal transducer and activator of transcription 3, tumor protein p53, vascular endothelial growth factor, and AKT serine/threonine kinase 1. The gene ontology enrichment functional analysis yielded 503 enrichment results, including 406 biological processes that were mainly related to the positive regulation of both gene expression and transcription and cellular response to hypoxia, etc. In total, 38 cellular components were primarily related to polymer complexes, transcription factor complexes, and platelet alpha granule lumen. Then, 59 molecular functions were closely related to the binding of enzymes, homologous proteins, and transcription factors. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis yielded 139 enrichment results, involving epidermal growth factor receptor tyrosine kinase inhibitor resistance and HIF-1 and mitogen-activated protein kinase signaling pathways.

CONCLUSION

HQD can play a role in CRC treatment through the “multi-component-target–pathway”. The active ingredients betulin, tetrahydropalmatine, and quercetin may act on targets such as Jun proto-oncogene, AP-1 transcription factor subunit, signal transducer and activator of transcription 3, tumor protein p53, vascular endothelial growth factor, and AKT serine/threonine kinase 1, which in turn regulate HIF-1 and mitogen-activated protein kinase signaling pathways in CRC treatment. The molecular docking junction clarified that all four key target proteins could bind strongly to the main HQD active ingredients. This indicates that HQD could slow down CRC progression by modulating multiple targets and signaling pathways.