Abstract
Background
Existing studies on the relationship between tea intake and lung
diseases have yielded inconsistent results, leading to an ongoing
dispute on this issue. The impact of tea consumption on the respiratory
system remained elucidating.
Materials and methods
We conducted a two-sample Mendelian randomization (MR) study to
evaluate the associations between five distinct tea intake phenotypes
and 15 different respiratory outcomes using open Genome-wide
association study (GWAS) data. The inverse variance weighted (IVW) was
used for preliminary screening and a variety of complementary methods
were used as sensitivity analysis to validate the robustness of MR
estimates. Pathway enrichment analysis was used to explore possible
mechanisms.
Results
IVW found evidence for a causal effect of standard tea intake on an
increased risk of lung squamous cell cancer (LSCC) (OR = 1.004; 95% CI
= 1.001–1.007; P = 0.00299). No heterogeneity or pleiotropy was
detected. After adjustment for potential mediators, including smoking,
educational attainment, and time spent watching television, the
association was still robust in multivariable MR. KEGG and GO
enrichment predicted proliferation and activation of B lymphocytes may
play a role in this causal relation. No causalities were observed when
evaluating the effect of other kinds of tea intake on various pulmonary
diseases.
Conclusion
Our MR estimates provide causal evidence of the independent effect of
standard tea intake (black tea intake) on LSCC, which may be mediated
by B lymphocytes. The results implied that the population preferring
black tea intake should be wary of a higher risk of LSCC.
Keywords: standard tea intake, black tea intake, green tea intake, lung
diseases, squamous cell lung cancer, Mendelian randomization, causal
relationship
Introduction
According to data published by the Global Burden of Disease (GBD),
Injuries, and Risk Factors Study, respiratory diseases were responsible
for the fourth position in terms of leading causes of death (lower
respiratory infections), the sixth position (COPD), the twelfth
position (tuberculosis), and the seventeenth position (lung cancer)
among people of all ages worldwide ([31]1). In 2017, chronic
respiratory disease affected approximately 544.9 million individuals
globally, marking a 39.8 percent rise compared to 1990, as reported by
the chronic respiratory disease collaborators of GBD ([32]2). Between
2010 and 2019, there was a global rise in the incidence and death rates
of lung cancer ([33]3). These statistics clearly demonstrate the
widespread prevalence of pulmonary ailments and their significant
impact on the quality of human life. Therefore, understanding the
causes of these respiratory diseases is crucial in order to minimize
their occurrence. While the risk factors or beneficial factors of
respiratory system diseases are not completely clear.
Tea is widely consumed across the globe for its numerous health
advantages such as cancer prevention, protection against heart disease
and diabetes ([34]4), and the potential to reduce the risk of all-cause
mortality ([35]5–[36]7). Sometimes tea is used for medical care for
COVID-19 ([37]8), particulate matter-induced lung injury ([38]9), and
acute pancreatitis-induced lung injury ([39]10). Previous research has
demonstrated that drinking tea can provide a safeguard against the
development of lung cancer, as indicated by multiple studies ([40]11,
[41]12). Conversely, certain studies have failed to establish a
correlation between tea consumption and the occurrence of lung cancer
([42]13–[43]16). Additionally, a few other studies have suggested that
tea consumption might increase the risk of developing lung cancer
([44]17, [45]18). Taken together, the association between tea
consumption and pulmonary diseases remains equivocal, partially due to
the constraints of conventional observational studies, including
potential reverse causality, and confounding factors.
Using genetic variations as instrumental variables (IVs), Mendelian
randomization (MR) analysis investigates the causal relationship
between exposure and disease outcome ([46]19). Benefiting from publicly
available genome-wide association study (GWAS) data reporting the
genetic effect of single nucleotide polymorphisms (SNPs) on a variety
of phenotypes, MR has been widely carried out for causality inference.
Given that genetic variants are randomly assigned at conception and are
typically free from environmental influence, MR is less influenced by
residual confounding and reverse causation. Therefore, in this study,
we aimed to utilize the MR technique to evaluate the potential impact
of tea intake on respiratory diseases.
Materials and methods
Study design
In our study, we utilized a two-sample MR approach to examine the
potential causal relationship between tea intake and respiratory
diseases. Considering different kinds of tea intake might exert various
effects on health outcomes, we used several tea intake phenotypes for
our MR analysis, including tea intake, green tea intake, herbal tea
intake, standard tea intake, and decaffeinated tea intake. For
respiratory outcomes, totally 15 phenotypes were included, which could
be grouped into 5 categories, including lung cancer (overall lung
cancer, lung adenocarcinoma, squamous cell lung cancer, and small cell
lung carcinoma), infectious pulmonary disease (bacterial pneumonia,
viral pneumonia, pneumonia, and respiratory tuberculosis), airway
disorders [asthma, bronchiectasis, chronic obstructive pulmonary
disease (COPD)], lung function indexes [forced vital capacity (FVC),
and forced expiratory volume in 1 second/FVC (FEV1/FVC)], and
additional respiratory conditions [idiopathic pulmonary fibrosis (IPF),
and pulmonary embolism].
The conceptual MR framework is presented in [47]Figure 1 . The MR
design is based on three key assumptions: (A) Single-nucleotide
polymorphism (SNPs) are closely related to tea intake exposure; (B)
SNPs are uncorrelated with known confounding factors; (C) SNPs affect
respiratory outcomes only through tea intake exposure ([48]19). To meet
the assumptions presented above, we used a series of instruments
quality control steps, and complementary statistical methods under the
MR framework. All the analyses were performed depending on the R
program (version 4.0.0) utilizing the “TwoSampleMR” package (version
0.5.4) and the “MendelianRandomization” package (version 0.5.1).
Figure 1.
[49]Figure 1
[50]Open in a new tab
The MR framework of ideation for our study.
Data source
Exposures
The GWAS data for five tea intake phenotypes came from the United
Kingdom biobank (UKB) ([51]20), which could be retrieved from the IEU
Open GWAS website ([52]https://gwas.mrcieu.ac.uk/) using distinct GWAS
ID ([53] Table 1 ). Among them, tea intake encompasses the intake of
both green tea and black tea. Standard tea intake encompasses all kinds
of tea produced using black tea leaves (which is the most prevalent
form of tea), including teabags, loose-leaf tea, and decaffeinated
types. Essentially, standard tea intake is the representative intake of
black tea.
Table 1.
Detailed information for the public data.
[54]ID [55]Trait Ncase Ncontrol
Exposure ukb-b-6066 Tea intake 447,485 -
ukb-b-4078 Green tea intake 64,949 -
ukb-b-13344 Herbal tea intake 64,949 -
ukb-b-3291 Standard tea intake 64,949 -
ukb-b-8553 Decaffeinated tea intake 64,949 -
Outcome ieu-a-987 TRICL-Lung cancer 29863 55586
ieu-a-984 TRICL- lung adenocarcinoma 11,245 54,619
ieu-a-989 TRICL-Squamous cell lung cancer 7,704 54,763
ieu-a-988 TRICL-Small cell lung carcinoma 2,791 20,580
ebi-a-GCST90014325 Asthma 56,167 352,255
ebi-a-GCST007431 Lung function (FEV1/FVC) 321,047 -
ebi-a-GCST007429 Lung function (FVC) 321,047 -
finn-r9 COPD 18,266 311,286
finn-r9 Bronchiectasis 2,188 311,286
finn-r9 Idiopathic pulmonary fibrosis 2,018 373064
finn-r9 Pulmonary embolism 9,243 367,108
finn-r9 Pneumonia 58,174 319,103
finn-r9 Viral pneumonia 3,394 314,673
finn-r9 Bacterial pneumonia 16,244 314,673
finn-r9 Respiratory tuberculosis 1,793 374,922
[56]Open in a new tab
Outcomes
The GWAS data for lung cancer as well as subtypes came from the
Transdisciplinary Research in Cancer of the Lung (TRICL) consortium
([57]21), which could be obtained from the IEU consortium. For
infectious pulmonary diseases and additional respiratory conditions,
the GWAS data for all the phenotypes were obtained from the FinnGen
consortium (Round 9, website: [58]https://www.finngen.fi/fi). The
FinnGen study is a global project consisting of nearly 50,0000
participants, and all the endpoints follow the treelike subtyping
system of the ICD-10 classification systems ([59]22). For airway
disorders, except for bronchiectasis and COPD coming from the FinnGen
study, the genetic information for asthma came from the GWAS data
conducted by Valette et al ([60]23). Finally, we also obtained the GWAS
data for lung function indexes from the study conducted by Shrine et al
([61]24). To avoid sample overlapping that would bias the MR estimates,
the outcome phenotypes included in this study did not contain UKB
participants. Detailed information for the GWAS data used in this study
is presented in [62]Table 1 .
Quality control of instrumental variables
We performed IVs quality control to filtrate eligible SNPs based on the
following steps (1): For tea intake, green tea intake, and herbal tea
intake, SNPs significantly associated with them at the genome-wide
significance level (P< 5 × 10^-8) were extracted. For standard tea
intake and decaffeinated tea intake, we extracted SNPs at a P threshold
of 5 × 10^-6 due to a restricted number of SNPs that reached
genome-wide significance. (2) To make sure SNPs were unrelated to one
another, we pruned SNPs at linkage disequilibrium (LD) r^2< 0.001
within a range of 10000kb ([63]25). (3) The F-statistic was then
calculated to evaluate the strength of each instrument using the
formula
[MATH: F=(N−2)×R2(1−R2<
/mn>) :MATH]
, of which R^2 represents the proportion of variance in phenotype
explained by a single SNP, which could be calculated using the formula
[MATH:
R2=2×Beta2×EAF×(1−EAF)2×Beta2×E<
/mi>AF×(1−EAF)+2×S<
msup>E2×EAF×(1−EAF)×N :MATH]
([64]26) where EAF represents the effect allele frequency, Beta
represents the estimated genetic effect of the SNPs on exposure, N
represents the sample size, and SE represents the standard error of
Beta. SNPs with an F-statistic of less than 10 were identified as weak
IVs and were eliminated ([65]27). (4) We then extracted the SNPs from
the outcome data and removed those tightly associated with the outcome
phenotypes (P< 5 × 10^-6), and if the SNPs were not found in the
outcome data, we attempted to search and use proxies at LD r^2 > 0.8 in
priority, and for those without suitable proxies were finally
discarded. (5) To ensure that the genetic associations reflect the same
effect allele, we harmonized the exposure and outcome dataset and
eliminated SNPs with incompatible alleles or palindrome structures
whose sequence direction was not determined. (6) Finally, before
performing MR analysis, we conducted MR- Pleiotropy Residual Sum and
Outlier (MR-PRESSO) to recognize and eliminate SNPs with potential
pleiotropy ([66]28). After implementing the aforementioned procedures,
the remaining SNPs were ultimately determined as eligible IVs to assess
the causal impact of various tea consumption on respiratory diseases.
Univariable MR analysis
We conducted a primary MR analysis using the random-effects inverse
variance weighted (IVW) method to initially evaluate the causal
association between distinct tea intake and various respiratory
diseases. By utilizing a meta-analysis approach, this technique
combines the pooled causal effect of the exposure on the outcome,
derived from the Wald ratio causal estimates obtained from each of the
SNPs. Besides, The random-effects IVW method yields a more cautious
causal inference by considering the uncertainty caused by pleiotropy,
in contrast to the standard fixed-effects IVW approach ([67]29). The
random-effects IVW is referred to as ‘IVW’ unless stated otherwise in
this work. For the IVW estimates, Bonferroni correction was applied to
account for multiple tests, and an observed estimate with P< 0.0033
(0.05/15 outcomes) was recognized as statistically significant.
Sensitivity analysis
To examine potential violations of the second and third hypotheses of
MR, various methods, including the weighted median and MR-Egger
regression were employed ([68]30). The weighted median is the method
that yields more conservative estimates as it assumes less than 50% of
IVs are invalid ([69]30). The MR-Egger is the statistical method with
weak power and is typically used for direction validation.
To detect heterogeneity, Cochrane’s Q test was conducted, and the
Q-value with P< 0.05 was deemed as heterogeneity detected. Horizontal
pleiotropy was then assessed based on the evaluation of the MR-Egger
intercepts. Specifically, the intercept term with P< 0.05 indicated the
existence of horizontal pleiotropy, which would bias the MR estimates
([70]31). Furthermore, a sensitivity analysis, called “leave-one-out”,
was conducted by leaving out one SNP at a time to assess if a single
SNP had a disproportionate effect on the overall estimation. Finally,
we searched on the PhenoScanner3 website
([71]http://www.phenoscanner.medschl.cam.ac.uk) to investigate the
presence of SNPs linked to any confounding factors, including smoking
behaviors ([72]32–[73]34), sedentary behaviors and physical activity
([74]35, [75]36), educational attainment ([76]37), anti-oxidants
([77]38), dietary intake and food supplement intake ([78]39), and job
of participants ([79]40), which have been identified as common risk
factors of respiratory diseases. If any were found, we eliminated these
SNPs and replicated the IVW analysis to evaluate the robustness of the
results.
Multivariable MR analysis
For the identified causal associations, we further used MVMR analysis
to distinguish whether the observed association was driven by potential
confounders. Previous studies revealed that the mutational burden of
human bronchial epithelium is primarily affected by smoking ([80]32),
which also leads to lung cancer ([81]33, [82]34). In an MR study, it
was found that having a genetic inclination towards 3.6 more years of
education was linked to a 52% decrease in the risk of developing lung
cancer ([83]37). A meta-analysis also reported sedentary behavior
indexed by television watching was associated with an increased risk of
lung cancer ([84]36). Additionally, an MR analysis revealed the causal
impact of time spent watching television on both lung cancer and
squamous cell lung cancer ([85]35). As such, considering the tight
association between smoking, educational attainment, and sedentary
television watching with lung cancer, we adjusted these three factors
one by one in the MVMR models. The GWAS for the smoking phenotype came
from GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN)
consortium, comprising 607,291 European descents for smoking initiation
([86]41) The GWAS for educational attainment was conducted by The
Social Science Genetic Association Consortium (SSGAC), with 766,345
participants investigated for years of schooling ([87]42). GWAS for
time spent watching television was from the UKB cohort, including
437,887 participants. The data were all extracted from the IEU
consortium using the GWAS ID of “eu-b-4877”, “ieu-a-1239” and
“ukb-b-5192”, respectively for smoking initiation, educational
attainment, and television watching.
Pathway enrichment analysis
To further uncover the possible mechanism of how standard tea intake
promoted LSCC progression, we performed pathway enrichment analysis.
Initially, we employed the Kyoto Encyclopedia of Genes and Genomes
(KEGG) enrichment analyses. Gene list annotation and analysis were
conducted in Metascape, a user-friendly online portal available at
[88]http://metascape.org/gp/index.html. The analysis and visualization
of data were performed using [89]https://www.bioinformatics.com.cn, a
freely available online platform for GO Enrichment.
Results
Study overview
The present research evaluated the causal impact of five distinct tea
intake phenotypes on the risk of 15 respiratory outcomes. After
rigorous IVs quality control, the number of SNPs utilized for each
exposure-outcome pair ranged from 14 to 40, and the range of
F-statistics was between 20.87 and 493.64, indicating no weak IVs were
included in our study ([90] Supplementary File 1 , [91]Supplementary
Table S1-15 ). For binary outcomes, the MR estimates were presented as
odds ratios (OR), whereas for continuous outcomes, the MR estimates
were shown as beta. A 95% confidence interval (CI) was provided.
UVMR results
The IVW method showed that genetic inclination towards standard tea
intake was significantly associated with an increased risk of lung
squamous cell cancer (LSCC) (OR = 1.004; 95% CI = 1.001–1.007; P =
0.003) ([92] Figure 2 ). The weighted median and MR-Egger yielded
similar results ([93] Figure 3 , [94]Table 2 ). According to Cochran’s
Q-test, there was no evidence of heterogeneity in the IVW model ([95]
Table 2 ). No evidence of horizontal pleiotropy was found according to
the MR-Egger intercept ([96] Table 2 ). Additionally, the leave-one-out
analysis verified that the combined IVW estimate was not dependent on
any individual SNP ([97] Supplementary Figure S1 ). Besides, by
searching on the PhenoSacnner3 website, we found an SNP (rs7233417)
related to educational attainment and time spent watching television.
After removing this SNP, replicative IVW estimation remained noteworthy
potential significant (OR = 1.004; 95% CI = 1.001–1.007; P = 0.009).
Figure 2.
[98]Figure 2
[99]Open in a new tab
Heat map of causal analysis results for 5 exposures and 15 outcomes,
based on p-value of IVW results.
Figure 3.
[100]Figure 3
[101]Open in a new tab
Scatter plot of causal association towards standard tea intake on lung
squamous cell cancer.
Table 2.
MR results of causality evaluation and sensitivity analysis between
standard tea intake and Squamous cell lung cancer.
Methods Nsnp Effector p-value
IVW/OR (95%CI) 13 1.004 (1.001, 1.007) 2.99×10^-3
Weighted median/OR (95%CI) 13 1.004 (1.000, 1.008) 0.032
MR Egger/OR (95%CI) 13 1.004 (0.997, 1.012) 0.253
Pleiotropy/Egger_intercept 13 -1.96×10^-3 0.933
Heterogeneity/Q value 13 15.177 0.232
IVW after confounder SNP removal/OR (95%CI) 12 1.004 (1.001, 1.007)
9.10×10^-3
[102]Open in a new tab
MR, Mendelian Randomization; IVW, Inverse variance weighted; OR
(95%CI), odds ratio with 95% confidence interval.
The IVW also found subtle evidence of several associations between
distinct tea consumption and various respiratory outcomes ([103]
Supplementary File 2 , [104]Supplementary Table S16 ). However, none of
them passed the examination of sensitivity analysis.
MVMR results
For the association between standard tea intake and LSCC, we further
conducted MVMR to evaluate the direct effect. The MV-IVW showed that
after adjusting for smoking initiation (OR = 1.005, 95% CI =
1.003–1.007, P< 0.001), educational attainment (OR = 1.003, 95% CI =
1.001–1.005, P = 0.002), and time spent on television watching (OR =
1.003, 95% CI = 1.001–1.005, P = 0.01), standard tea intake remained a
significant effect on LSCC. MV-median and MV-Egger yielded consistent
results ([105] Table 3 ). Besides, using the Egger intercept, we found
no evidence of pleiotropy in the MVMR models adjusted for smoking
initiation (intercept = 0.005, P = 0.15), educational attainment
(intercept = 0.002, P = 0.37), or time spent on television watching
(intercept = -0.002, P = 0.41).
Table 3.
MVMR results between standard tea intake and Squamous cell lung cancer.
Traits Exposure mv-IVW mv-Weighted median mv-Egger
OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value
1 Standard tea intake 1.005(1.003,1.007) <0.001 1.005(1.001,1.009)
0.002 1.003(0.999,1.007) 0.085
Smoking 1.707(1.393,2.094) <0.001 1.629(1.258,2.110) <0.001
1.692(1.380,2.075) <0.001
2 Standard tea intake 1.003(1.001,1.005) 0.002 1.003(1.001,1.005) 0.023
1.002(0.998,1.006) 0.152
Education 0.488(0.402,0.591) <0.001 0.528(0.404,0.689) <0.001
0.486(0.400,0.590) <0.001
3 Standard tea intake 1.003(1.001,1.005) 0.011 1.003(0.999,1.007) 0.127
1.004(1.000,1.008) 0.015
Watching TV 2.635(1.767,3.931) <0.001 1.917(1.150,3.198) 0.013
2.705(1.803,4.058) <0.001
[106]Open in a new tab
MVMR, Multivariable Mendelian Randomization; IVW, Inverse variance
weighted; OR (95%CI), odds ratio with 95% confidence interval.
Pathway enrichment analysis
The associated genes used for KEGG analysis can be found in
[107]Supplementary Table S17 . Both KEGG and GO analysis showed that
the effect of standard tea intake on LSCC might be through the
regulation of B cell proliferation ([108] Supplementary Figure S2 ).
Discussion
We examined the causal relationship between tea consumption and
respiratory illnesses using a two-sample MR analysis. In our study,
genetic liability for standard tea intake was associated with an
increased risk of LSCC. Further MVMR analysis demonstrated the observed
causality was not driven by the common risk factors of lung cancer, and
GO enrichment analysis indicated that B cell proliferation and
activation may be the underlying mechanisms linking standard tea intake
with LSCC. As far as we know, this MR study is the initial assessment
of the impact of distinct tea consumption on various respiratory
diseases.
Our results were consistent with some previous studies. In a 7-year
follow-up cohort study conducted in Japan, it was found that the
consumption of green tea had no correlation with the development of
lung cancer. The incidence of lung cancer, after adjusting for multiple
variables, did not show any significant difference between individuals
who consumed 1 cup, 2 cups, 3 cups, or more cups of green tea per day
compared to those who consumed less than 1 cup per day. Furthermore,
the risk of lung cancer did not appear to decrease for past, current,
and passive smokers who consumed green tea ([109]16). In our study, we
also discovered that there is no connection between consuming green tea
and respiratory diseases, including lung cancer and its subtypes. A
majority of laboratory evidence has demonstrated the anti-tumor effect
of green tea polyphenols on tumor cell lines and animal models, while
epidemiologic studies in humans failed to get consistent findings. This
discrepancy might be attributed to a much higher dose in cell lines or
animal models compared with dietary human consumption, confounders, and
some different mechanisms between vitro and vivo ([110]13). A
prospective cohort study of 500,000 Chinese population aged 30-79
years, which followed them for 10.1 years, showed that daily consumers
adding tea leaves over 4.0 g/day had elevated risk of all cancers,
including lung cancer (HR, 1.31; CI, 1.17–1.46) once all potential
confounders have been taken into account ([111]18). According to the
Prostate, Lung, Colorectal, and Ovarian Cancer clinical prospective
screening trial (PLCO) conducted in the United States, consuming a cup
or more of tea daily was linked to a 5% reduction in overall cancer
mortality when compared to consuming less than a cup. However, the
study did not find any significant decrease in the risk of
site-specific cancers, including lung cancer ([112]15).
Our results were also inconsistent with some of the previous studies. A
systematic review reevaluated 19 meta-analyses examining the links
between tea consumption and 11 various forms of cancer. The findings
indicated that consuming tea was connected to a reduced risk of lung
cancer, although the level of evidence was weak ([113]11). A study
conducted on 63257 individuals from the Singaporean Chinese population
revealed that consuming black tea could lead to a decrease in the risk
of developing lung cancer. Additionally, women who consumed moderate
quantities of green tea had a lower risk of lung cancer ([114]12).
Notably, this study solely gathered baseline information regarding tea
consumption, which might undergo modifications over time and
potentially result in bias. Besides, this study focused on an Asian
population, while the data source we obtained was derived from the
European population. Additionally, this study did not distinguish the
distinct effects of different kinds of tea consumption, including green
tea, black tea, decaffeinated tea, and so on. Another research
involving 1,177,156 individuals from 17 cohort studies conducted in the
United States, China, Japan, Korea, and Singapore revealed that among
Asian cohorts, the consumption of at least 2 cups of green tea per day
was linked to a greater risk of lung cancer for both current and
non-smokers. Additionally, the intake of oolong tea was found to be
associated with an elevated risk of lung cancer among non-smokers,
whereas the consumption of black tea did not show any such increased
risk ([115]17). Nevertheless, it should be noted that these discoveries
cannot be considered causal due to the possibility of remaining
confounding factors caused by smoking, including secondhand smoke
exposure, as well as alterations in the consumption of coffee and tea
after enrolling in the study ([116]17). Given that MR analyses are less
susceptible to confounding factors or reverse causation, we proceeded
with the investigation of the association between tea intake and
respiratory illnesses. Considering the potential effects might vary
among different tea compositions, our study included various kinds of
tea consumption for MR analysis.
There could be certain mechanisms that might elucidate the causality
between standard tea intake and LSCC examined from MR. The utilization
of insecticide residue in tea farming ([117]43) could potentially
account for the elevated risk of cancer. DNA breaks occurred at a
dietary concentration because of pyrogallol-related chemicals and
tannins from tea ([118]44). One potential mechanism worthy of proposed
was the tea composition of standard tea intake in the issue of LSCC
risk. As illustrated by the UKB dataset, the consumption of standard
tea encompasses various varieties of tea produced using black tea
leaves. Theaflavin and thearubigins are formed as oxidized derivatives
of black tea catechins during fermentation. Notably, these black tea
polyphenols are unique to black tea and are not present in other types
of tea ([119]45), This distinction helps explain the mechanism behind
the observed association, indicating that only black tea may contribute
to the increased risk of this particular type of cancer. Additionally,
our KEGG and GO enrichment analysis further showed that regulation of B
cell proliferation might mediate the development of LSCC.
B cells are a significant group of immune cells in the adaptive immune
system. In squamous cell carcinoma ([120]46), B cells have also been
discovered to play a pro-tumoral function through the accumulation of
immune complexes including IgG, which would promote the activation of
myeloid cells through FcγR and contribute to inflammation. In non-small
cell lung cancer (NSCLC), the traditional pathway is partially
activated through an IgM-dependent mechanism ([121]47), leading to a
negative prognosis ([122]48). B cells are stimulated in mice with
developing tumors and generate antibodies that accumulate in the early
cancerous lesions, sustaining long-term inflammation by activating Fcγ
receptors (FcγR) on innate cells that migrate into the preneoplastic
and neoplastic tumor environment ([123]49). Additionally, the
complement has been observed to contribute to pro-tumoral effects by
promoting antibody-induced chronic inflammation in CMT and TC1 models
of lung cancer ([124]47, [125]50). Multiple studies indicate that B
cells play a crucial role in lung cancer, with B cell proliferation
observed in 35% of lung cancer cases ([126]51). Furthermore, B cells
are present throughout all stages of lung cancer development, with
variations observed across clinical stages and histological subtypes
([127]52, [128]53). In tumor growth and spread, B cells can hinder T
cell reactions, especially by generating immunosuppressive cytokines
([129]54). B cell–enriched tertiary lymphoid structures (TLSs) in a
model of inflammation-induced hepatocellular carcinoma (HCC) were
discovered to function as a sanctuary for tumor progenitor cells and
promote the proliferation of cancerous cells through the secretion of
lymphotoxin β ([130]55). Some studies have also discovered that B cells
have a positive effect on numerous types of cancer. However, the exact
mechanism by which B lymphocytes impact cancer is not yet fully
understood ([131]56), It is believed that the tumor microenvironment
plays a role in determining the balance between the anti-tumor and
pro-tumor activities of B cells ([132]57).
The analysis of RNA-seq on individual cells obtained from tumor tissue
samples of patients with non-small cell lung cancer (NSCLC) validates
the presence of both primary categories of B cells, specifically the
naïve-like and plasma-like B lymphocytes. The levels of naïve-like B
cells are reduced in advanced NSCLC, and this decrease is linked to a
negative prognosis. When naïve-like B cells from NSCLC patients were
co-cultured with two lung cancer cell lines, it was observed that the
growth of lung cancer cells was suppressed by the secretion of the
factors that negatively regulate cell growth. Additionally, researchers
proved that the plasma-like B cells impede the progression of cancer
cells during the initial phase of NSCLC, while fostering cell
proliferation during the later stages of NSCLC ([133]58). Researchers
analyzed a group of 108 individuals with lung squamous cell lung cancer
(LSCC) through various techniques such as DNA copy number analysis,
somatic mutation examination, RNA-sequencing, and expression
proteomics. This comprehensive approach led to the identification of
three distinct proteomic subtypes in which the Inflamed and Redox
subtypes accounted for 87% of the tumors. The Inflamed subtype, in
particular, exhibited a higher presence of B-cells ([134]59). In
addition to tertiary lymphoid structures, a considerable number of
immune-dense regions lacking germinal center-like structures can be
seen in NSCLC. Analysis of transcriptomic data and digital pathology
images from nine hundred and thirty-five lung cancer patients revealed
that a high intratumoral immune hotspot score, indicating the
percentage of immune hotspots interacting with tumor islands, was
associated with unfavorable overall survival in LSCC but not in lung
adenocarcinoma. LSCC exhibiting elevated intratumoral immune hotspot
scores demonstrated a consistent increase in B-cell signatures
([135]60). These findings may indicate a positive relationship between
B cells and LSCC.
The current study exhibits several strengths. MR is less prone to
residual confounding and reverse causation. Genetic variants are
assigned at conception, making them independent of the disease
development process. Besides, MR often leverages data from large-scale
GWAS, maximizing the utility of existing genetic information. And the
results obtained from the current study have more statistical power. In
addition, the GWAS data used in this study were all derived from
participants of European ancestry, reducing the likelihood of
population stratification biasing the MR estimates. Furthermore, using
the MVMR technique, our study further distinguished the direct effect
of black tea intake (indicated by standard tea intake) on the risk of
squamous cell lung cancer. Our study has some constraints. As the data
was from Europeans, the result derived from our study could not be
directly extended to other populations. Besides, information on tea
intake was obtained from self-reported questionnaires, which may
contain recall bias and may not accurately reflect actual tea
consumption. Finally, we mainly analyzed linear correlations and could
not rule out the existence of nonlinear correlations in MR analysis.
Conclusion
Our MR estimates provide causal evidence that black tea intake
(indicated by standard tea intake) might increase the risk of squamous
cell lung cancer, independent of education attainment, smoking, and
television watching. The promotive effect on LSCC of black tea intake
and the absent protective effect found on other pulmonary diseases of
each kind of tea intake may need to rethink the role of distinct tea
consumption on human health, especially for black tea.
Data availability statement
The original contributions presented in the study are included in the
article/[136] Supplementary Material , further inquiries can be
directed to the corresponding author.
Author contributions
YZ: Supervision, Writing – review & editing. ZW: Conceptualization,
Data curation, Formal analysis, Investigation, Methodology, Project
administration, Writing – original draft. MJ: Data curation,
Investigation, Writing – original draft. CS: Data curation,
Investigation, Writing – original draft. CL: Methodology, Software,
Writing – original draft.
Acknowledgments