Exploration Of Causal Relationship Between Lung Adenocarcinoma-related Depression From Perspective Of “all Qi Stagnation And Depression Related To Lung” And Prediction Of Intervention Traditional Chinese Medicine

Abstract: Objective To explore the genetic causal relationship between lung adenocarcinoma (LUAD) and major depressive disorder (MDD) through Mendelian randomization (MR) analysis, based on the theory of "all qi stagnation and depression related to lung ", and predict potential traditional Chinese medicines for the intervention of lung adenocarcinoma-related depression (LAD). Methods Genome-wide association studies (GWAS) datasets for LUAD and MDD were obtained from the IEU OpenGWAS database. MR analysis and robustness assessment were primarily conducted using the TwoSampleMR package in R software. Instrumental variable nearby genes (IVNGs) were predicted using the Ensembl database. Functional enrichment analysis was performed based on IVNGs, and the CTD and ITCM databases were used to predict relevant chemical components and potential traditional Chinese medicine (TCM) interventions. The Classical and Modern Medical Cases Cloud Platform (V2.3.9) was used for statistical analysis of TCM, focusing on the therapeutic principles and prescriptions. Results MR analysis using inverse variance weighted (IVW) method (β = 0.002 05, P = 0.018 99) suggested a weak positive causal effect between LUAD and MDD. Robustness analysis showed no significant heterogeneity or horizontal pleiotropy, confirming the reliability of the results. Enrichment analysis revealed a significant association with the cholinergic synaptic signaling pathway. Clustering analysis categorized the top 30 most frequent TCM into five main groups. The four qi (temperature properties) of all potential TCM interventions were predominantly warm, followed by neutral and cold; The five flavors were primarily sweet, with bitter and pungent flavors following. The meridians were mainly attributed to the liver, followed by the lung, spleen, stomach, kidney, and heart. The primary therapeutic effects focused on clearing heat and detoxifying, cooling blood and stopping bleeding, detoxifying, and clearing heat and dampness. Classical prescriptions corresponding to the top 15 frequent TCM included Mahuang (Ephedrae Herba), Zhizi (Gardeniae Fructus), Yiyiren (Coicis Semen), Pugongying (Taraxaci Herba), and Roucongrong (Cistanches Herba), with similar classical prescriptions being Mahuang-based formulas, Huaihua Powder (槐花散), Huanglian Jiedu Decoction (黄连解毒汤), Weijing Decoction (苇茎汤), Wuwei Xiaodu Drink (五味消毒饮), Jichuan Decoction (济 川煎), Huagan Decoction (化肝煎), and Sangxing Decoction (桑杏汤). Conclusion Based on the theory "all qi stagnation and depression related to lung ", a weak positive genetic causal effect between LUAD and MDD was found. LUAD may promote the development of MDD by influencing neural signaling transmission and regulatory mechanisms, particularly through the cholinergic pathway. Additionally, combining the "Target-Status" medical theory and the predicted TCM results, this study explored potential TCM treatments for LAD, aiming to provide references for the TCM treatment of LAD patients to improve the overall therapeutic outcomes and mental health of LUAD patients.

Key words: lung adenocarcinoma-related depression; Mendelian randomization; prediction of traditional Chinese medicine; target-status; Mahuang-based formulas; Huaihua Powder; Huanglian Jiedu Decoction; Weijing Decoction; Wuwei Xiaodu Drink; Jichuan Decoction; Huagan Decoction; Sangxing Decoction; Ephedrae Herba; Gardeniae Fructus; Coicis Semen; Taraxaci Herba; Cistanches Herba

 

 

 

High Level Herbal Cistanche Formula For Treating Lung Adenocarcinoma-related Depression

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Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer worldwide. Although there are significant regional differences, the incidence of LUAD is highest among both men and women in East Asia [1]. The psychological burden associated with malignant tumors, particularly the occurrence of major depressive disorder (MDD) in patients, poses significant challenges to individual health. This not only affects their quality of life but also negatively impacts the effectiveness of disease treatment [2]. Therefore, timely identification and treatment of MDD are of great importance for improving the overall prognosis of patients with malignant tumors.

Traditional Chinese Medicine (TCM) has a theory stating, "All forms of qi stagnation and oppression belong to the lungs," leading to the hypothesis that the occurrence of MDD in LUAD patients may be associated with dysfunction in the lung's dispersing and descending functions. Studies have confirmed a bidirectional relationship between malignant tumors and MDD. For example, there is a genetic correlation between MDD and an increased risk of lung cancer [3], and the incidence of MDD is higher in lung cancer patients compared to non-cancer populations [4]. However, whether LUAD, as a subtype of lung cancer, has a genetic basis for the occurrence of MDD has not been fully elucidated.

This study is the first to use two-sample Mendelian randomization (MR) analysis to explore the genetic causal relationship between LUAD and MDD. By identifying instrumental variable nearby genes (IVNGs) and conducting enrichment analyses, the study aims to elucidate the potential pathogenic mechanisms underlying the association between LUAD and MDD. Furthermore, based on IVNGs and public databases, the study predicts potential compounds and Chinese medicinal herbs for intervening in LUAD-related depression (LAD). Combining these predictions with "state-targeted" medicine [5], the study explores TCM approaches for treating LAD, aiming to achieve precise targeted treatment under the guidance of fundamental TCM theories and syndrome differentiation and treatment. This provides innovative ideas and methods for TCM-based interventions for LAD, promoting the inheritance, innovation, and modernization of TCM.

1 Materials and Methods

1.1 Study Design

First, two-sample MR analysis was used to assess the genetic causal effect between LUAD and MDD. IVNGs were then obtained, and enrichment analysis using gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted to reveal the potential pathogenic mechanisms of LAD. Additionally, compounds and Chinese medicinal herbs associated with LAD were predicted based on IVNGs. Finally, the Ancient and Modern Medical Records Cloud Platform (V2.3.9) (https://www.yiankb.com/) was used for statistical analysis, clustering analysis, and identification of similar classical prescriptions for the predicted Chinese medicinal herbs. This provided new ideas and methods for TCM-based interventions in LAD. The research workflow is shown in Figure 1.

 

1.2 MR Analysis

1.2.1 Acquisition of Genome-Wide Association Studies (GWAS) Datasets

GWAS datasets for LUAD and MDD were obtained using the TwoSampleMR package from the IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/). The LUAD GWAS dataset (ieu-a-965) and the MDD dataset (ebi-a-GCST90038650) were used to extract instrumental variables related to LUAD and their effect information in the ebi-a-GCST90038650 dataset for causal inference in MR analysis.

The ieu-a-965 dataset includes 3,442 LUAD cases and 14,894 healthy controls, totaling 18,336 individuals. The ebi-a-GCST90038650 dataset consists of GWAS summary data from European populations, published by Dönertaş et al. [6] in Nature Aging in 2021.

1.2.2 Selection of Instrumental Variables

MR studies must satisfy three core assumptions:

Instrumental variables (IVs) must be significantly associated with the exposure factor.

IVs must not be associated with any confounding factors.

IVs must influence the outcome only through the exposure factor.

In this study, single nucleotide polymorphism (SNP) information was retrieved from the GWAS Catalog (https://gwas.mrcieu.ac.uk/), and SNPs associated with potential confounding factors (e.g., schizophrenia, bipolar disorder, manic episodes, post-traumatic stress disorder, mental illness, MDD medications, age of MDD onset, hyperthyroidism, insomnia, etc.) were excluded. The following steps were performed using the TwoSampleMR package:

Selecting SNPs closely related to the instrumental variables: A selection threshold of P < 1.0×10⁻⁵ was applied. This P-value threshold is commonly used in GWAS studies to ensure that the selected SNPs have sufficient statistical significance with the trait, thereby reducing the false positive rate and ensuring the reliability of the IVs [7].

Removing linkage disequilibrium: Parameters were set as r² = 0.001 and kb = 10,000. These thresholds ensure the independence of the selected SNPs within the genome, avoiding excessive correlation among IVs due to strong linkage disequilibrium, thus improving the validity of the IVs [8].

Excluding SNPs with minor allele frequency (MAF) < 0.01: The maf_threshold parameter was applied to exclude rare SNPs, ensuring that the selected SNPs have better statistical power and biological representativeness, consistent with the selection standards for common variants [9].

Excluding palindromic SNPs: This step avoids analysis bias caused by palindromic sequences, further improving the accuracy of the results [10].

1.2.3 Statistical Analysis

This study primarily used the inverse variance weighted (IVW) method to evaluate genetic causal effects, while MR-Egger, Simple Mode, Weighted Median, and Weighted Mode methods were used to assess the robustness of the IVW results. The potential causal relationship between LUAD and MDD was presented using odds ratios (OR) and 95% confidence intervals (CI).

Heterogeneity tests were conducted using the MR-Egger and IVW methods to evaluate heterogeneity among IVs. If strong heterogeneity was detected (Q_pval < 0.05), a random effects model was used to obtain more robust effect estimates. Horizontal pleiotropy of SNPs and the stability of IVW results were assessed using the MR-Egger intercept test and the leave-one-out method, respectively. A P < 0.05 was considered statistically significant. All statistical analyses were performed using R version 4.4.2.

1.3 Functional Enrichment Analysis Based on IVNGs

The location and chromosome sequence information of SNPs were queried in the Ensembl database (https://www.ensembl.org/) using SNP IDs. The BioMart tool was then used to identify all instrumental variable nearby genes (IVNGs) within ±200 kb of these SNPs. Functional enrichment analysis of IVNGs was conducted using the clusterProfiler package [11], with the enrichment results visualized using the ggplot2 package. Key pathway diagrams were obtained from the KEGG database (https://www.kegg.jp/).

 

1.4 Prediction and Statistical Analysis of Potential Chinese Medicinal Herbs

IVNGs were submitted to the Comparative Toxicogenomics Database (CTD, https://ctdbase.org/) to identify compounds regulating these genes. Compounds were filtered for those associated with humans (Homo sapiens) and supported by ≥2 IVNGs or ≥2 publications. Related Chinese medicinal herbs were then retrieved using the ITCM database (http://itcm.biotcm.net/) [12].

Chinese medicinal herbs containing the same compounds were grouped together [13-15]. If a single herb contained multiple compounds identified in this study, it could appear in multiple groups. The grouped herbs were uploaded to the Ancient and Modern Medical Records Cloud Platform (V2.3.9, https://www.yiankb.com/) for standardization processing (e.g., "Perilla" was unified as "Perilla leaf," "Artemisia" as "Liu Ji Nu," "Flat Plantain" as "Plantago Herb," etc.). Cluster analysis, statistical analysis of herbal properties and meridian tropism, and efficacy analysis were then conducted. The high-frequency herbs were further analyzed in the classical prescription identification module to identify similar classical formulas.

The compounds with corresponding potential Chinese medicinal herbs, their related IVNGs, and the standardized potential herb information were uploaded to Cytoscape (V3.10.3) to construct an "IVNG-compound-herb" mapping network.

 

2 Results

2.1 Causal Effect Analysis Results Between LUAD and MDD

The LUAD GWAS dataset (ieu-a-965) contained a total of 8,881,354 SNPs, and 12 SNPs were retained as instrumental variables after screening for subsequent MR analysis. Information on the instrumental variables is shown in Table 1.

The results of the IVW method indicated that the β value was 0.00205 (positive) and statistically significant (P < 0.05), suggesting a potential weak positive causal effect between LUAD and the occurrence of MDD. However, other MR analysis methods did not show statistical significance (P > 0.05). Detailed MR analysis results are provided in Table 2 and Figure 2.

 

Table 2 Analyses rsults of Mendelian rndomization

Method	SNPs	β	SE	P
MR Egger	12	0.00233	0.00195	0.26045
Weighted Median	12	0.00219	0.00190	0.42978
IVW	12	0.00205	0.00087	0.01899*
Simple Mode	12	0.00171	0.00181	0.92606
Weighted Mode	12	0.00074	0.00103	0.58903

Note: P < 0.05 is considered statistically significant.

 

2.2 Robustness Analysis Results of MR Analysis

The heterogeneity test results showed no significant heterogeneity among the instrumental variables using both the MR Egger and IVW methods (P > 0.05), indicating consistent effects of the instrumental variables on the occurrence of MDD. This supports the reliability of the MR analysis results (Table 3).

The MR Egger intercept term (P = 0.8761) indicated no significant horizontal pleiotropy, suggesting that the effects of the instrumental variables on the occurrence of MDD were primarily mediated through the direct causal pathway of LUAD rather than other confounding effects (Egger intercept = −0.00056, standard error = 0.00352, P = 0.8761).

The leave-one-out method results showed that none of the instrumental variables caused a significant change in the overall causal effect, and the direction of the effect remained consistent. This reflects the robustness and validity of the instrumental variables (Figure 3).

The funnel plot revealed that the data points were generally symmetrical, consistent with the lack of significant heterogeneity and horizontal pleiotropy, further validating the rationality of the instrumental variable selection and the robustness of the causal effect estimation (Figure 4).

 

 

Fig. 2 Causal effect estimates of LUAD and MDD occurrence using different MR methods

 

 

2.3 Functional Enrichment Analysis Results of IVNGs

Based on the chromosomal sequences and loci of the 12 SNPs, 120 IVNGs were identified from the Ensembl database. Functional enrichment analysis of these IVNGs revealed the following results:

GO Functional Enrichment: IVNGs were primarily enriched in biological processes such as response to nicotine, cholinergic synaptic transmission, and neurotransmitter transport. They were also associated with cellular components like plasma membrane signaling receptor complexes, the cytoplasmic side of the membrane, and postsynaptic membrane. In terms of molecular functions, they were enriched in activities such as symporter activity, secondary active transmembrane transporter activity, passive transmembrane transporter activity, and channel activity (Figure 5-A).

KEGG Pathway Enrichment: The enriched pathways were mainly related to cholinergic signaling pathways (Figure 5-A, B).

The enriched GO functions and KEGG pathways were primarily associated with the following IVNGs:
CHRNA3 (cholinergic receptor nicotinic alpha 3), CHRNA5, CHRNB4, EEF1A2 (elongation factor 1 alpha 2), KCNQ2 (potassium voltage-gated channel subfamily Q member 2), LIME1 (Lck interacting membrane protein 1), PIK3CG (phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma), PTK6 (protein tyrosine kinase 6), SLC12A7 (solute carrier family 12 member 7), SLC6A18, SLC6A19, SLC6A3, SRMS (Src related kinase Lck interacting membrane protein), and TRAF3 (TNF receptor associated factor 3) (Figure 5-B).

 

2.4 Prediction and Statistical Analysis of Potential Chinese Medicinal Herbs

IVNGs were submitted to the CTD database to retrieve related compounds, yielding 1,023 compounds after deduplication, including representatives such as retinoic acid, rotenone, resveratrol, ascorbic acid, camptothecin, and citric acid. Further filtering reduced the list to 170 compounds. Using the ITCM database, 41 of these compounds were mapped to corresponding Chinese medicinal herbs.

The identified herbs were grouped and uploaded to the Ancient and Modern Medical Records Cloud Platform for standardization and deduplication, resulting in 580 potential medicinal herbs. The most frequently mentioned herbs included Sha Yuan Zi (Astragalus seed), Shan Zha Ye (Hawthorn leaf), Ju Ju (Chicory), Feng Mi (Honey), Guang Zao (Jujube), Zhi Zi (Gardenia), Luo Xuan Zao (Spirulina), Rou Cong Rong (Cistanche), Pu Gong Ying (Dandelion), Qiao Mai (Buckwheat), Ma Huang (Ephedra), Wu Mei (Mume fruit), and Yi Yi Ren (Coix seed) (Table 4).

Cluster analysis of the top 30 herbs resulted in five major categories:

Da Huang (Rhubarb), Sha Ji (Sea Buckthorn).

Shan Zhu Yu (Cornus), Gou Gu Ye (Holly leaf), Wu Zhu Yu (Evodia), Ma Huang (Ephedra), Mu Gua (Papaya).

Long Kui (Solanum nigrum), Ren Shen (Ginseng), Chuan Xiong (Ligusticum), Ju Ju (Chicory), Jin Qiao Mai (Golden buckwheat), Feng Mi (Honey), Wu Mei (Mume fruit).

Yi Yi Ren (Coix seed), Ji Shi Teng (Paederia), Sha Yuan Zi (Astragalus seed), Guang Zao (Jujube).

E Jiao (Donkey-hide gelatin), Shi Hu (Dendrobium), Luo Xuan Zao (Spirulina), Sang Piao Xiao (Mantis egg-case), Da Suan (Garlic), Shan Zha Ye (Hawthorn leaf), Qiao Mai (Buckwheat), Ning Meng (Lemon), Zhi Zi (Gardenia), Rou Cong Rong (Cistanche), Pu Gong Ying (Dandelion), Da Ji (Thistle) (Figure 6).

Characteristics of Predicted Herbs

Four Natures: Warm herbs were the most common (17.46%), followed by neutral (15.80%) and cold (13.81%) herbs (Figure 7-A).

Five Flavors: Sweet was the dominant flavor (29.28%), followed by bitter (26.19%) and pungent (20.88%) (Figure 7-B).

Meridian Tropism: Herbs mainly acted on the liver, followed by the lungs, spleen, stomach, kidneys, and heart (Figure 7-C).

Main Efficacies: The primary efficacy was clearing heat and detoxifying (7.96%), followed by cooling the blood and stopping bleeding (3.43%), detoxification (3.31%), hemostasis (2.43%), and clearing heat and dampness (2.10%) (Figure 7-D).

Identification of Classical Prescriptions

Among the top 15 herbs, key herbs such as Ma Huang (Ephedra), Zhi Zi (Gardenia), Yi Yi Ren (Coix seed), Pu Gong Ying (Dandelion), and Rou Cong Rong (Cistanche) were frequently identified. Similar classical prescriptions included Ma Huang-based formulas, Hua Hua San (Sophora Flower Powder), Huang Lian Jie Du Tang (Coptis Detoxification Decoction), Wei Jing Tang (Reed Decoction), Wu Wei Xiao Du Yin (Five-Ingredient Detoxification Drink), Ji Chuan Jian (Benefit River Decoction), Hua Gan Jian (Liver Transformation Decoction), and Sang Xing Tang (Mulberry and Apricot Decoction) (Table 5).

Using Cytoscape (V3.10.3), a mapping network of "IVNGs-compounds-herbs" was constructed, involving 41 chemical components, 54 IVNGs, and 580 medicinal herbs (Figure 7-E).

 

 

Fig. 5 Enrichment analysis results and schematic diagram of cholinergic signaling pathway

 

Table 4 Top 30 potential intervention traditional Chinese medicines

Chinese Herb	Frequency	Frequency %	Chinese Herb	Frequency	Frequency %
Astragalus seed	9	17.65%	Mume fruit	9	17.65%
Hawthorn leaf	9	17.65%	Coix seed	9	17.65%
Chicory	9	17.65%	Ephedra	9	17.65%
Honey	9	17.65%	Dandelion	9	17.65%
Jujube	8	15.69%	Buckwheat	8	15.69%
Gardenia	8	15.69%	Garlic	8	15.69%
Spirulina	8	15.69%	Thistle	8	15.69%
Cistanche	8	15.69%	Lemon	8	15.69%
Mulberry mistletoe	8	15.69%	Dendrobium	8	15.69%
Donkey-hide gelatin	8	15.69%	Sea buckthorn	8	15.69%
Sophora flower	8	15.69%	Solanum nigrum	8	15.69%
Ginseng	8	15.69%	Ligusticum	8	15.69%
Papaya	8	15.69%	Cornus	8	15.69%
Holly leaf	8	15.69%	Evodia	8	15.69%

 

 

Fig. 6 Cluster analysis results of traditional Chinese medicines