Skip to main content
NCBI home page
Clinical and Translational Gastroenterology logoLink to Clinical and Translational Gastroenterology
. 2024 Nov 14;16(1):e00790. doi: 10.14309/ctg.0000000000000790

Candidate Genetic Loci Modifying the Colorectal Cancer Risk Caused by Lifestyle Risk Factors

Shabane Barot 1,2,✉, Litika Vermani 3, Johannes Blom 1,4, Susanna Larsson 5,6, Annelie Liljegren 7, Annika Lindblom 3,8
PMCID: PMC11756881  PMID: 39665592

Abstract

INTRODUCTION:

65%–70% of colorectal cancer (CRC) cases are considered sporadic; they arise under the influence of environmental factors in individuals lacking a family history of CRC. Low-risk genetic variants are believed to contribute to CRC risk, in tandem with lifestyle factors.

METHODS:

Six hundred sixteen nonfamilial Swedish CRC cases with at least 1 of the following 5 risk factors: smoking, excessive alcohol consumption, physical inactivity, adherence to an unhealthy diet, and excess body weight were included in this study. A control group consisting of 1,642 healthy individuals was used. Cases and controls were genotyped from blood samples at the Centre for Inherited Disease Research at Johns Hopkins University within the Colorectal Transdisciplinary Study research collaboration, using the Illumina Infinium OncoArray-500 K BeadChip. Five separate genome-wide haplotype association analyses were performed, one for each risk factor. Logistic regression models were used to estimate associations between haplotypes (exposure) and CRC (outcome) in cases with lifestyle risk factors vs controls. Haplotypes with an odds ratio >1 were considered candidate risk markers, denoting an area of interest in the genome. A significance threshold of P < 5 × 10−8 was used.

RESULTS:

We found 17 haplotype regions significantly associated with CRC in cases vs controls. Several regions included genes linked to inflammation and tumor promotion.

DISCUSSION:

We concluded that having certain genetic variants was associated with an increased risk of CRC compared with healthy controls among cases with known lifestyle risk factors. The interplay of lifestyle and genetic risk factors calls for further elucidation.

KEYWORDS: colorectal cancer, genome-wide association study, haplotype analyses, lifestyle factors

graphic file with name ct9-16-e00790-g001.jpg

Open in a new tab

INTRODUCTION

The global incidence rates of colorectal cancer (CRC) are increasing among populations in transitioning countries and young adults in high-income settings (1). Societal and cultural changes affecting dietary patterns, physical activity levels, smoking habits, alcohol consumption, and body weight are considered drivers of the growing CRC burden (2). CRC, however, is a complex disease arising under the influence of environmental exposures in interplay with genetic factors (3). Approximately 2%–5% of all CRC cases are attributable to well-characterized highly penetrant and rare inherited germline mutations such as in Lynch syndrome or hereditary nonpolyposis colorectal cancer and familial adenomatous polyposis (4). 20%–30% of cases are familial, i.e., occur in individuals with a family history of CRC (5). The familial clustering of CRC has been attributed to multiple inherited low-penetrance low-risk genetic variants (6). Genome-wide association studies (GWAS) have identified 205 independent SNPs associated with CRC. These loci explain only a small fraction of the CRC heritability (7,8). Modifiable lifestyle factors including obesity and physical inactivity further increase the risk of CRC in individuals with germline cancer predisposition syndromes, such as Lynch syndrome, as well as in familial cases of CRC (9,10). However, 65%–70% of all CRC cases are sporadic, arising in individuals lacking a known germline pathogenic variant or a family history. In such cases, genetic low-risk variants are likely to confer an increased susceptibility to CRC in tandem with lifestyle risk factors.

Most GWAS use single nucleotide polymorphisms (SNPs) as the genetic variant of interest when searching for associations with a trait. Haplotype analysis is a complementary approach that may be more effective when searching for low-risk loci in a genetically relatively homogenous population. A haplotype consists of SNPs residing in proximity of one another on the same chromosome that tend to be inherited en bloc (11). A haplotype GWAS thus identifies a susceptibility region, within which a locus or several loci of interest may be located. We previously performed haplotype GWAS in a Swedish cohort of CRC cases with results suggesting novel CRC susceptibility loci (12–14). The aim of this study was to search for risk-modifying loci in subcohorts of those patients with nonfamilial disease and lifestyle factors known to increase the risk of CRC, including adherence to unhealthy diets, physical inactivity, smoking, excessive alcohol consumption, and excess body weight.

METHODS

Study design

We conducted 5 haplotype-based genome-wide association analyses using sporadic stage I–IV CRC patients with lifestyle factors known to confer an increased risk of CRC as cases and healthy spouses or blood donors as controls.

Participants

Cases.

The CRC low-risk study is a Swedish multicenter cohort study including more than 3,300 consecutive CRC cases recruited in 2004–2009 (15). Written informed consent was obtained from all study participants. All participants were interviewed by the same interviewer on family history of CRC and other malignancies on study inclusion and pedigrees were constructed for the families of the index-person. CRC cases in first-degree and second-degree relatives, as well as cousins, were verified from medical records or death certificates. Those lacking relatives with CRC were considered sporadic or nonfamilial cases (12).

All participants included in 2004–2006 were invited to fill out a self-administered lifestyle questionnaire on study inclusion (n = 1,767). The response rate was 93% (n = 1,639). Of the 1,639 participants with lifestyle data, 616 nonfamilial cases with stage I-IV CRC had eligible genotype data, and at least one lifestyle factor associated with an increased risk and was thus included (Table 1).

Table 1.

Characteristics of 616 patients with stage I–IV colorectal cancer in the colorectal cancer low-risk cohort

Men Women Total
Participants, na (%) 353 263 616
Age, median (min, max) 70 (38–91) 68 (28–91) 69 (28–91)
Tumor location, n (%)
 Right colon 83 (48.3) 89 (51.7) 172
 Left colon 81 (53.6) 70 (46.4) 151
 Rectum 146 (67.0) 72 (33.0) 218
 Othera 43 (57.3) 32 (42.7) 75
Stage according to Dukesb, n (%)
 A 61 (53.0) 54 (47.0) 115
 B 131 (55.0) 107 (45.0) 238
 C 109 (58.3) 78 (41.7) 187
 D 30 (69.8) 13 (30.2) 43
High risk alcohol consumptionc, n (%) 98 (70.0) 42 (30.0) 140
Physical inactivityd, n (%) 126 (53.2) 111 (46.8) 237
Unhealthy dietse, n (%) 203 (57.0) 153 (43.0) 356
Overweight or obesityf, n (%) 229 (59.8) 154 (40.2) 383
Smokingg, n (%) 50 (48.1) 54 (51.9) 104
Open in a new tab
a

Other: including appendix, indistinguishable (appendix/cecum, colon descendens/colon sigmoideum, colon sigmoideum/rectum).

b

Classified according to Dukes staging system.

c

≥9 units/wk for women, ≥12 units/week for men.

d

<150 minutes leisure time physical activity/wk.

e

Nonadherence to the Mediterranean dietary pattern.

f

BMI ≥25.

g

Current and former smokers with ≤ 1 year of cessation.

The GWAS analyses included 356 CRC cases adhering to unhealthy diets, 237 physically inactive cases, 103 smokers, 140 cases with excessive alcohol consumption, and 383 cases with excess body weight Several cases had more than one risk factor: 193 had 2, 121 had 3, 46 cases had 4 risk factors, and 7 individuals had all 5 risk factors. Notably, among smokers, 70% adhered to unhealthy diets, and among the physically inactive, 72% also had excessive body weight. The group consisting of excessive alcohol consumers had the least overlap.

Controls.

Five hundred thirty-six cancer-free spouses of participants, without a family history of cancer and 1,106 blood donors from Karolinska University Hospital were used as controls. Patients with cancer and cancer survivors are not allowed to donate blood according to the criteria of the Swedish blood centers. We can however not exclude subclinical cases of cancer in the control group. Information on age, lifestyle factors, and family history of cancer was unavailable for controls. A total of 1,642 controls were recruited, 870 male patients and 772 female patients. The same 1,642 controls were used in all 5 analyses.

Risk factor assessment.

Information on dietary habits, physical activity, smoking, alcohol consumption, and anthropometric markers was collected from cases using a validated semiquantitative questionnaire, as described in detail elsewhere (16). Current and former smokers with ≤ 1 year of cessation were considered smokers. Participants with <150 min/wk of leisure time physical activity were considered physically inactive, according to the World Health Organization recommendations (17). Those with a body mass index (BMI) ≥25.0 m2 were regarded as overweight/obese according to the World Health Organization classification (18). A high-risk consumption of alcohol was defined as using ≥9 units of alcohol/week for women and ≥14 units for men. A unit was defined as an alcoholic beverage containing more than 12 g of pure alcohol (19).

An unhealthy diet was defined as nonadherent to the Mediterranean dietary pattern. A diet variable was computed using the modified Mediterranean dietary scale (mMED), a modification of the Mediterranean diet scale originally developed by Trichopoulou (20). The mMED was developed by Tektonidis and Larsson et al to suit the specific intake habits of the Swedish population (21,22). Using self-reported data, participants were assigned an mMED score based on the intakes of 7 food groups: red and processed meats, dairy, fish, legumes and nuts, fruits and vegetables, whole grains, and the use of olive or rapeseed oil. The score ranged from a minimum of 7 points (low adherence to a Mediterranean dietary pattern) and a maximum of 35 (high adherence to said pattern). Participants with a score ≤ the group median were considered to have an unhealthy diet.

Genetic exposure assessment.

All CRC cases and the 1,642 healthy controls were genotyped from blood samples at the Centre for Inherited Disease Research at Johns Hopkins University within the Colorectal Transdisciplinary Study research collaboration (23). This was done using the Illumina Infinium Onco-Array-500 K BeadChip. The first quality check of the Swedish samples was conducted within the Colorectal Transdisciplinary Study. Swedish data were subsequently retrieved, and a second quality check was performed before further analysis, as has been described in detail elsewhere (12).

Statistical methods

To examine the associations between haplotypes and CRC cases with lifestyle risk factors, 5 separate haplotype GWAS were performed. The cases consisted of CRC patients with sporadic, stage I–IV disease and one or several of the following lifestyle risk factors: smoking, excess body weight, adherence to unhealthy diets, physical inactivity, and excessive alcohol consumption. High-performance computers for big-data analysis at Uppsala Multidisciplinary Center for Advanced Computational Science were used to analyze data. The haplotype GWAS was conducted using a sliding window method, with a window size of 1–25 SNPs, as has been used in several previous haplotype genome-wide analyses (24). This method entails testing all possible haplotypes within the region of 24 SNPs before and after each included SNP, generating haplotypes of different lengths for the same locus. Logistic regression models were used to estimate associations between haplotypes (exposure) and CRC (outcome) in cases with lifestyle risk factors vs controls. All analyses were performed using the software PLINK version 1.07 (25). The parameters used when running the analyses included hap-logistic (imputes haplotype phasing based on multimarker predictors, using the E-M algorithm and omnibus association test), hap-window 1–25 (specifying the fixed number of SNPs in the sliding windows), minor allele frequency 0.01, and genotype 0.1. A P value of < 5 × 10−8 was considered statistically significant (26). Haplotypes with an odds ratio (OR) > 1 were considered to harbor candidate risk markers, denoting an area of interest in the genome. To find out more about how to evaluate the results, and what P values could be generated when comparing 2 small-sized and similar cohorts, an additional GWAS was undertaken where we simply compared 100 healthy control persons with another 100 healthy controls.

RESULTS

Each of the 5 haplotype analyses performed generated data over all 23 chromosomes. The first haplotype GWAS used 356 cases adhering to unhealthy diets vs 1,642 healthy controls. 2 regions with an OR > 1, located on chromosomes 2 and 9, respectively, were associated with CRC in this subcohort of cases (Table 2). The locus on chromosome 9 spanned the protein-coding area of the transient receptor potential cation channel subfamily M member 3 gene (TRPM3). Located within the locus on chromosome 2 was the protein-coding region of histone deacetylase 4 (HDAC4) (Figure 1).

Table 2.

Haplotype regions significantly associated with colorectal cancer in cases vs controls

Risk factor Chrom SNP1 SNP2 Haplotype Sample freq. OR P value 95% CI
Unhealthy diets* 2 chr2_240022151_C_T rs3791483 AGAGGAAAG 0.0107 7.72 3.97e-08 3.72–16.00
9 rs978790 Chr9_73746381_A_G GGCGACAGAGAA 0.0387 2.73 3.42e-08 1.91–3.89
Physical inactivity** 1 rs6677499 rs7518703 AACAGAAGGAGA 0.0109 7.38 3.27e-08 3.63–15.00
12 rs10842673 rs7978769 GGGAAAAAAAGG 0.0147 5.33 3.21e-08 3.02–10.14
Smoking*** 5 rs2964381 rs967052 AAGAAGGGG 0.0142 9.11 4.61e-09 4.35–19.10
14 rs1760941 rs1760927 AAGGGGCGAAG 0.0109 11.00 3.20e-08 4.70–25.73
15 rs1474084 rs7162897 AAGGGGGCGG 0.0135 6.78 4.64e-08 3.41–13.46
19 rs11084578 rs1078373 GGAGCG 0.0103 11.60 1.63e-08 4.96–27.16
23 rs209373 rs6622104 GAACCCAGAAAG 0.0426 4.82 3.07e-08 2.76–8.41
Alcohol consumption**** 2 rs1863101 rs2380677 GAG​AGA​CGC​CCA​AGA​AAC​GGA​GCA​A 0.0149 6.42 3.72e-08 3.31–12.45
3 rs450138 chr3_8016955_C_T AAGAGAAGAAAAGGAGGG 0.0105 7.75 1.85e-08 3.80–15.83
4 rs1440561 rs1497427 CAAAGACCGAGACAGA 0.0149 5.59 4.01e-08 3.02–10.34
5 rs6861682 rs4513726 AGAAGAAAGGGG 0.0441 3.68 2.57e-08 2.33–5.82
8 chr8_82928049_A_G rs10102998 AGAAGACGCCAGC 0.0230 4.53 3.88e-08 2.64–7.76
11 rs4910051 chr11_9593427_C_T AAGGAAGAAAGAGAA 0.0261 5.75 9.08e12 3.48–9.51
11 rs2034716 rs7927029 AGAGAGGGGGAAC 0.0183 5.98 3.3e-08 3.17–11.28
19 chr19_45414451_C_T chr19_45530552_A_G GAGGGGAAAAGAGCAG 0.0100 9.58 6.76e-09 4.46–30.57
Open in a new tab

Chrom, Chromosome; CI, 95% confidence interval; OR, odds ratio; sample freq, sample frequency; SNP1, single nucleotide polymorphism 1, the first SNP of the haplotype region; SNP2, single nucleotide polymorphism 2, the last SNP of the haplotype region.

*Unhealthy diets: non-adherence to the modified Mediterranean diet score. **Physical inactivity: <150 min/week of leisure time physical inactivity. ***Smoking: current and former smokers with ≤1 year cessation ****Alcohol consumption: ≥9 units of alcohol/wk for women and ≥14 units for men.

Figure 1.

Figure 1.

Open in a new tab

Haplotype regions including gene-coding regions with an odds ratio >1 significantly associated with colorectal cancer. ALX-4, aristaless-like homeobox 4; BHLHE41, basic helix-loop-helix family member e41; CD247, CD247 molecule; EXT2, exostosin glycosyltransferase 2; HDAC4, histone deacetylase 4; IPO7, Importin 7; NRK, Nik-related kinase; NRK, Nik-related kinase; PNP, purine nucleoside phosphorylase; POU2F1, POU class 2 homeobox 1; RADX, replication protein 1-related single-stranded DNA binding protein, X-linked; SSPN, sarcospan; TRPM3, transient receptor potential cation channel subfamily M member 3 gene; TSHZ3, teashirt zinc finger homeobox 3; ZNF143, zinc-finger protein 143.

The second GWAS used 237 physically inactive cases vs 1,642 healthy controls. Two loci on chromosomes 12 and 1, respectively, were associated with CRC with an OR > 1 (Table 2). The haplotype region on chromosome 12 included genes coding for transmembrane protein sarcospan (SSPN) and basic helix-loop-helix family member e41 (BHLHE41). The locus on chromosome 1 spanned the coding regions of proteins POU class 2 homeobox 1 (POU2F1) and CD247 molecule (CD247) (Figure 1).

The third GWAS used 103 smokers vs 1,642 healthy controls. Five haplotype regions with an OR > 1 were associated with CRC in this subcohort of cases, located on chromosomes 5, 14, 15, 19, and X (Table 2). No genes were located within the regions on chromosomes 5 and 15. On chromosome 19, one locus spanned the coding region of the teashirt zinc finger homeobox 3 (TSHZ3) gene. The haplotype on chromosome 14 included the coding region of purine nucleoside phosphorylase (PNP). On chromosome X, the haplotype spanned the coding regions of 4 genes: Nik-related kinase (NRK), interferon regulatory factor 4 (IRF4), thyroxine-binding globulin of serum (TBG), and replication protein 1-related single-stranded DNA binding protein, X-linked (RADX) (Figure 1).

Our fourth analysis included 140 CRC cases with a high-risk consumption of alcohol vs the same set of healthy controls. A total of 7 haplotypes with an OR > 1 were associated with CRC (Table 2). These were located on chromosomes 2, 3, 4, 5, 11 (2 separate loci), and 19.

The protein coding region of DNA-methylation driver gene AC008271.1 was located within the haplotype region on chromosome 2. On chromosome 11, 2 separate haplotype regions were associated with CRC. The first of these included 3 genes: Importin 7 (IPO7), zinc-finger protein 143 (ZNF143), and AC132192.1. Located within the second one were the genes exostosin glycosyltransferase 2 (EXT2) and aristaless-like homeobox 4 (ALX-4). On chromosome 19, finally, one haplotype region spanned the genes apolipoprotein C1 (APOC1), apolipoprotein C-IV (APOC4), apolipoprotein C-II (APOC2), cleft lip and palate transmembrane protein 1 (CLPTM1), and v-rel avian reticuloendotheliosis viral oncogene homolog B (RELB) (Figure 1). No genes were found in the haplotype regions on chromosomes 3, 4, and 5.

A fifth and final analysis of 383 CRC cases with excess body weight vs 1,642 controls was conducted. We did not detect any significant associations.

Comparing 100 healthy control persons to another 100 healthy controls did not generate any significant results. None of the observed P values were below 5 × 01−5.

Discussion

We performed a set of haplotype genome-wide association analyses using CRC patients with lifestyle risk factors as cases and healthy spouses and blood donors from the same geographical region as controls. We identified 17 candidate susceptibility loci associated with CRC in those adhering to an unhealthy diet, the physically inactive, smokers, and those with excessive alcohol consumption. The set of cases in each GWAS included individuals combining 2 or more of these risk factors. The loci identified may thus modify the risk in those with an overall unhealthy lifestyle. Among cases with excessive alcohol consumption and/or smoking, 13 of 17 loci were found to be associated with an increased risk of CRC in this study. The combination of smoking and alcohol consumption has been associated with an increased risk of several cancers, including colon cancer, and synergistic effects in those with a genetic susceptibility are plausible (27). To our knowledge, this is the first study to search for genetic risk modifiers in this manner. However, gene-environment interaction in CRC has been the focus of several previous studies, using different methodological approaches such as polygenic risk scores or genome-wide environment interaction analysis (28,29). Gene-environment interaction is an evolving scientific field, and this study will hopefully contribute to a better understanding of CRC etiology.

In cases adhering to an unhealthy diet, the haplotype regions associated with CRC included the genes TRPM3 and HDAC4. HDAC4 is involved in the epigenetic modification of gene expression by deacetylation of histones and alterations have been found in CRC, sarcoma, melanoma, and nonsmall-cell lung cancer (30).

Among the physically inactive cases, haplotype regions on chromosome 12 included the gene BHLHE41. The transcription factor BHLHE41 is involved in cell differentiation, hypoxia, and immune response, and is believed to suppress cell invasion in various cancers, including breast, colon, and pancreatic cancer. Silencing of BHLHE41 has been linked to activation of the mitogen-activated protein kinase/c-jun N-terminal kinases (MAPK/JNK) pathway (31,32). On chromosome 1 among the same cases, a locus with an OR of 7.38 included genes POU2F1 and CD247. The former has been linked to metabolic reprogramming in colon cancer cells, where upregulation of the protein POU2F1 was found to enhance proliferation and suppress oxidative stress and apoptosis (33). CD247 encodes the T-cell surface glycoprotein CD3 zeta chain, a component of the T-cell CD3 receptor complex. CD247 expression is thus linked to T-cell function and is downregulated in several chronic inflammatory diseases (34).

Among smokers, haplotypes spanning the genes TSHZ3 on chromosome 19, PNP on chromosome 14, and the 4 X-linked genes NRK, IRF4, TBG, and RADX were significantly associated with CRC. TSHZ3 expression is downregulated in CRC tissue, and low levels of TSHZ3 have been associated with a poorer prognosis in patients with CRC (35). The PNP gene is involved in the regulation of immune cell function (36). NRK is highly expressed in placental tissue where it functions as a regulator of cell proliferation (37). IRF4 is an immune system regulator involved in several lymphoid malignancies that may function both as an oncogene and a tumor suppressor in a context-dependent manner (38). The downstream target of IRF-4 is the MYC proto-oncogene, which is involved in the development of a range of cancers (39). RADX, finally, encodes a protein involved in replication fork stability (40).

Among high-risk consumers of alcohol, a haplotype region on chromosome 2 included the gene AC008271.1, a methylation driver that has been linked to an increased risk of hepatocellular cancer (41). Five genes were found in 2 separate loci on chromosome 11: IPO7, ZNF143, AC132192.1, EXT2, and ALX-4.

IPO7 modulates epidermal growth factor receptors (ERBBs), among these ERBB1 or the endothelial growth factor receptor and ERBB2. IPO7 overexpression has been found in pancreatic and prostate cancer cells and may be linked to ERBB dysregulation promoting cancer progression and dissemination (42). ZNF143 overexpression has been found to promote cell proliferation in gastric cancer and may promote cell migration and invasion in colon cancer (43). EXT2 has been implicated in the malignant transformation of glioblastoma (44). Methylation of ALX-4 has been linked to early stages of colorectal carcinogenesis (45).

Among the same cases, one haplotype region on chromosome 19 included 5 genes: apolipoprotein C1 (APOC1), apolipoprotein C-IV (APOC4), apolipoprotein C-II (APOC2), cleft lip and palate transmembrane protein 1 (CLPTM1), and v-rel avian reticuloendotheliosis viral oncogene homolog B (RELB). The apolipoproteins have been linked to cancer: APOC1 is overexpressed in CRC tissue and may act as a promotor of the MAPK pathway (46). APOC2 overexpression has been found in gastric cancer, promoting malignant transformation through the mammalian target of rapamycin pathway (47).

CLPTM1 overexpression has been linked to several cancers, including lung cancer and oral squamous cell carcinoma (48). The gene product of RELB is known to promote inflammation and has been linked to tumor-microenvironmental immunosuppression and immune evasion (49).

Sequencing the genes located within the candidate regions identified in this study would allow us to further examine their clinical relevance. If genetic variants of interest were to be identified, these could be included in a genetic risk score, improving the prognostic assessments for patients with CRC.

No statistically significant associations between haplotypes and CRC were identified in cases with excess body weight. The association between overweight, defined as BMI 25.0–29.9, and CRC is highly debated, as some studies have suggested a protective effect of overweight on CRC risk (50). Future studies may consider using only obese CRC cases, with a BMI > 29.9.

Our previous haplotype GWAS on CRC found several novel candidate risk loci among familial cases and 2 novel loci among sporadic cases (12). The latter were not identified in this study.

Strengths and weaknesses

The major limitation of this study was the small sample size. No significant results were obtained when using 2 healthy groups as cases and controls in our test GWAS, which strengthened us in considering our results, with P values < 5 × 10−8, to be robust and potentially reflective of relevant genetic variants contributing to increased risk of CRC at these loci.

The interpretation of our results is somewhat hampered by the lack of lifestyle data from the control group. For the spouses, one can assume some degree of shared risk factors. Blood donors, however, are likely to have a healthier lifestyle than cases.

CONCLUSIONS

We identified 17 loci associated with an increased risk of CRC in those exposed to lifestyle risk factors. Several of the haplotype regions identified included genes that have been linked to inflammation and tumor promotion. The results may facilitate the understanding of the combined effects of lifestyle factors and genetic susceptibility on CRC risk. Hopefully, they will contribute to developing personalized recommendations on lifestyle changes targeting those with a higher genetic susceptibility to CRC.

CONFLICTS OF INTEREST

Guarantor of the article: Annika Lindblom, MD, PhD.

Specific author contributions: S.B.: planning the study, interpreting data, drafting the manuscript. L.V.: planning the study, performing analyses, interpreting data, critical review of draft. J.B.: interpreting data, critical review of draft. S.L.: contributions to study design, critical review of draft. A.L.: planning the study, interpreting data, critical review of draft. A.L.: study design, planning, data acquisition, interpreting data, critical review of draft.

Financial support: This research was funded by the Swedish Cancer Society, grant number 18-0700; Swedish Research Council, grant number 2019-01441; the Cancer Research Funds of Radiumhemmet, grant number 191203; Stockholm County Council (ALF project), grant number 500395.

Potential competing interests: None to report.

Ethical permits: This study was approved by the Regional ethical review board in Stockholm (Dnr 02-489, 04-4377, 2009/2155-32, 2013/928-32, 2014/1326-32, 2017/57-31/4).

Study Highlights.

WHAT IS KNOWN

  • ✓ The interaction of low-risk genetic variants and lifestyle risk factors is believed to contribute to colorectal cancer (CRC) susceptibility.

WHAT IS NEW HERE

  • ✓ This study identified candidate risk regions harboring genes linked to inflammation and tumor promotion among cases of nonfamilial CRC exposed to lifestyle risk factors.

  • ✓ This study may contribute to developing personalized recommendations on lifestyle changes for those with a greater genetic susceptibility to CRC.

ACKNOWLEDGMENTS

The authors would like to express their heartfelt gratitude to all study participants, and members of the Swedish Low-Risk Colorectal Cancer Study Group. Colorectal Transdisciplinary Study (CORECT): The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the CORECT Consortium, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government or the CORECT Consortium.

REFERENCES

  • 1.Morgan E, Arnold M, Gini A, et al. Global burden of colorectal cancer in 2020 and 2040: Incidence and mortality estimates from GLOBOCAN. Gut 2023;72(2):338–44. [DOI] [PubMed] [Google Scholar]
  • 2.Keum N, Giovannucci E. Global burden of colorectal cancer: Emerging trends, risk factors and prevention strategies. Nat Rev Gastroenterol Hepatol 2019;16(12):713–32. [DOI] [PubMed] [Google Scholar]
  • 3.Tenesa A, Dunlop MG. New insights into the aetiology of colorectal cancer from genome-wide association studies. Nat Rev Genet 2009;10(6):353–8. [DOI] [PubMed] [Google Scholar]
  • 4.Jasperson KW, Tuohy TM, Neklason DW, et al. Hereditary and familial colon cancer. Gastroenterology 2010;138(6):2044–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Lichtenstein P, Holm NV, Verkasalo PK, et al. Environmental and heritable factors in the causation of cancer: Analyses of cohorts of twins from Sweden, Denmark, and Finland. N Engl J Med 2000;343(2):78–85. [DOI] [PubMed] [Google Scholar]
  • 6.Graff RE, Moller S, Passarelli MN, et al. Familial risk and heritability of colorectal cancer in the nordic twin study of cancer. Clin Gastroenterol Hepatol 2017;15(8):1256–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Jiao S, Peters U, Berndt S, et al. Estimating the heritability of colorectal cancer. Hum Mol Genet 2014;23(14):3898–905. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Fernandez-Rozadilla C, Timofeeva M, Chen Z, et al. Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and East Asian ancestries. Nat Genet 2023;55(1):89–99. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Power RF, Doherty DE, Parker I, et al. Modifiable risk factors and risk of colorectal and endometrial cancers in Lynch syndrome: A systematic review and meta-analysis. JCO Precis Oncol 2024;8:e2300196. [DOI] [PubMed] [Google Scholar]
  • 10.Fardet A, Druesne-Pecollo N, Touvier M, et al. Do alcoholic beverages, obesity and other nutritional factors modify the risk of familial colorectal cancer? A systematic review. Crit Rev Oncol Hematol 2017;119:94–112. [DOI] [PubMed] [Google Scholar]
  • 11.Hettiarachchi G, Komar AA. GWAS to identify SNPs associated with common diseases and individual risk: Genome wide association studies (GWAS) to identify SNPs associated with common diseases and individual risk. In: Sauna ZE, Kimchi-Sarfaty C. (eds.). Single Nucleotide Polymorphisms: Human Variation and a Coming Revolution in Biology and Medicine. Springer International Publishing: Cham, Switzerland, 2022, pp 51–76. [Google Scholar]
  • 12.Liu W, Mahdessian H, Helgadottir H, et al. Colorectal cancer risk susceptibility loci in a Swedish population. Mol Carcinog 2022;61(3):288–300. [DOI] [PubMed] [Google Scholar]
  • 13.Thutkawkorapin J, Mahdessian H, Barber T, et al. Two novel colorectal cancer risk loci in the region on chromosome 9q22.32. Oncotarget 2018;9(13):11170–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Wallander K, Liu W, von Holst S, et al. Genetic analyses supporting colorectal, gastric, and prostate cancer syndromes. Genes Chromosomes Cancer 2019;58(11):775–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Forsberg A, Keranen A, VON Holst S, et al. Defining new colorectal cancer syndromes in a population-based cohort of the disease. Anticancer Res 2017;37(4):1831–5. [DOI] [PubMed] [Google Scholar]
  • 16.Barot S, Rantanen P, Nordenvall C, et al. Combined associations of a healthy lifestyle and body mass index with colorectal cancer recurrence and survival: A cohort study. Cancer Causes Control 2024;35(2):367–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Bull FC, Al-Ansari SS, Biddle S, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med 2020;54(24):1451–62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.WHO. Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation on obesity. Geneva, June 3–5, 1997. WHO: Geneva, Switzerland, 1998. [Google Scholar]
  • 19.World Cancer Research Fund American Institute for Cancer Research. Diet, nutrition, physical activity and cancer: A global perspective. Continuous update expert report 2018; 2018. [Google Scholar]
  • 20.Trichopoulou A, Costacou T, Bamia C, et al. Adherence to a Mediterranean diet and survival in a Greek population. N Engl J Med 2003;348(26):2599–608. [DOI] [PubMed] [Google Scholar]
  • 21.Tektonidis TG, Akesson A, Gigante B, et al. A mediterranean diet and risk of myocardial infarction, heart failure and stroke: A population-based cohort study. Atherosclerosis 2015;243(1):93–8. [DOI] [PubMed] [Google Scholar]
  • 22.Larsson SC, Hakansson N, Wolk A. Healthy dietary patterns and incidence of biliary tract and gallbladder cancer in a prospective study of women and men. Eur J Cancer 2017;70:42–7. [DOI] [PubMed] [Google Scholar]
  • 23.Schmit SL, Edlund CK, Schumacher FR, et al. Novel common genetic susceptibility loci for colorectal cancer. J Natl Cancer Inst 2019;111(2):146–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Liu W, Jiao X, Thutkawkorapin J, et al. Cancer risk susceptibility loci in a Swedish population. Oncotarget 2017;8(66):110300–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Purcell S, Neale B, Todd-Brown K, et al. Plink: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81(3):559–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Xu C, Tachmazidou I, Walter K, et al. Estimating genome-wide significance for whole-genome sequencing studies. Genet Epidemiol 2014;38(4):281–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Viner B, Barberio AM, Haig TR, et al. The individual and combined effects of alcohol consumption and cigarette smoking on site-specific cancer risk in a prospective cohort of 26,607 adults: Results from Alberta's Tomorrow Project. Cancer Causes Control 2019;30(12):1313–26. [DOI] [PubMed] [Google Scholar]
  • 28.Xin J, Du M, Gu D, et al. Risk assessment for colorectal cancer via polygenic risk score and lifestyle exposure: A large-scale association study of east Asian and European populations. Genome Med 2023;15(1):4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Jordahl KM, Shcherbina A, Kim AE, et al. Beyond GWAS of colorectal cancer: Evidence of interaction with alcohol consumption and putative causal variant for the 10q24.2 region. Cancer Epidemiol Biomarkers Prev 2022;31(5):1077–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Cuttini E, Goi C, Pellarin E, et al. HDAC4 in cancer: A multitasking platform to drive not only epigenetic modifications. Front Mol Biosci 2023;10:1116660. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Zhang D, Zheng Q, Wang C, et al. BHLHE41 suppresses MCF-7 cell invasion via MAPK/JNK pathway. J Cell Mol Med 2020;24(7):4001–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Chen S, Dong QJ, Wan ZA, et al. BHLHE41 overexpression alleviates the malignant behavior of colon cancer cells induced by hypoxia via modulating HIF-1α/EMT pathway. Gastroenterol Res Pract 2022;2022:6972331. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Lin J, Xia L, Oyang L, et al. The POU2F1-ALDOA axis promotes the proliferation and chemoresistance of colon cancer cells by enhancing glycolysis and the pentose phosphate pathway activity. Oncogene 2022;41(7):1024–39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Li Y, Chen S, Li X, et al. CD247, a potential T cell-derived disease severity and prognostic biomarker in patients with idiopathic pulmonary fibrosis. Front Immunol 2021;12:762594. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Zhou Y, Wang S, Yin X, et al. TSHZ3 functions as a tumor suppressor by DNA methylation in colorectal cancer. Clin Res Hepatol Gastroenterol 2021;45(6):101725. [DOI] [PubMed] [Google Scholar]
  • 36.Abt ER, Rashid K, Le TM, et al. Purine nucleoside phosphorylase enables dual metabolic checkpoints that prevent T cell immunodeficiency and TLR7-associated autoimmunity. J Clin Invest 2022;132(16):e160852. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Denda K, Ida K, Tanno M, et al. Proteomic analysis of Nrk gene-disrupted placental tissue cells explains physiological significance of NRK. BMC Res Notes 2019;12(1):785. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Amanda S, Tan TK, Iida S, et al. Lineage- and stage-specific oncogenicity of IRF4. Exp Hematol 2022;114:9–17. [DOI] [PubMed] [Google Scholar]
  • 39.Zhong Y, Zhu Y, Li Q, et al. Association between mediterranean diet adherence and colorectal cancer: A dose-response meta-analysis. Am J Clin Nutr 2020;111(6):1214–25. [DOI] [PubMed] [Google Scholar]
  • 40.Krishnamoorthy A, Jackson J, Mohamed T, et al. RADX prevents genome instability by confining replication fork reversal to stalled forks. Mol Cell 2021;81(14):3007–17.e5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Fu J, Qin W, Tong Q, et al. A novel DNA methylation-driver gene signature for long-term survival prediction of hepatitis-positive hepatocellular carcinoma patients. Cancer Med 2022;11(23):4721–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Li M, Xu D, Zhan Y, et al. IPO7 promotes pancreatic cancer progression via regulating ERBB pathway. Clinics (Sao Paulo) 2022;77:100044. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Paek AR, Lee CH, You HJ. A role of zinc-finger protein 143 for cancer cell migration and invasion through ZEB1 and E-cadherin in colon cancer cells. Mol Carcinog 2014;53(Suppl 1):E161–8. [DOI] [PubMed] [Google Scholar]
  • 44.Giambra M, Di Cristofori A, Conconi D, et al. Insights into the peritumoural brain zone of glioblastoma: CDK4 and EXT2 may be potential drivers of malignancy. Int J Mol Sci 2023;24(3):2835. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Ebert MP, Model F, Mooney S, et al. Aristaless-like homeobox-4 gene methylation is a potential marker for colorectal adenocarcinomas. Gastroenterology 2006;131(5):1418–30. [DOI] [PubMed] [Google Scholar]
  • 46.Tang W, Liu H, Li X, et al. Upregulation of APOC1 promotes colorectal cancer progression and serves as a potential therapeutic target based on bioinformatics analysis. J Oncol 2023;2023:2611105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Wang C, Yang Z, Xu E, et al. Apolipoprotein C-II induces EMT to promote gastric cancer peritoneal metastasis via PI3K/AKT/mTOR pathway. Clin Transl Med 2021;11(8):e522. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Inoue K, Hatano K, Hanamatsu Y, et al. Pathobiological role of cleft palate transmembrane protein 1 family proteins in oral squamous cell carcinoma. J Cancer Res Clin Oncol 2019;145(4):851–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Galbo PM, Jr, Zang X, Zheng D. Molecular features of cancer-associated fibroblast subtypes and their implication on cancer pathogenesis, prognosis, and immunotherapy resistance. Clin Cancer Res 2021;27(9):2636–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Walter V, Jansen L, Hoffmeister M, et al. Prognostic relevance of prediagnostic weight loss and overweight at diagnosis in patients with colorectal cancer. Am J Clin Nutr 2016;104(4):1110–20. [DOI] [PubMed] [Google Scholar]

Articles from Clinical and Translational Gastroenterology are provided here courtesy of American College of Gastroenterology

RESOURCES