Exposure to Psychotropic Drugs and Colorectal Cancer Risk in Patients with Affective Disorder: A Nested Case-Control Study

• Abstract
• Introduction
• Materials and Methods
• Data source
• Study participants
• Exposure to psychotropic medications
• Statistical analysis
• Data Availability Statement
• Results
• Discussion
• Mood stabilizers
• Antidepressants
• Antipsychotics
• Limitations
• Conclusion
• Data availability
• Financial Disclosure
• Authors contributions
• References

Abstract

Background This study aimed to assess the association between the risk of colorectal cancer (CRC) and exposure to mood stabilizers, antidepressants, and antipsychotics in patients with affective disorders.

Methods This nested case-control study used data from the National Health Insurance Database of Taiwan collected between 2001 and 2011. All participants in this study had affective disorders. Then, 1209 patients with CRC and 1:10 matched controls were identified based on their demographic and clinical characteristics. A logistic regression model adjusted for demographic and clinical characteristics was used to determine the risk of developing CRC after exposure to psychotropic drugs.

Results Among patients with affective disorders, exposure to mood stabilizers (reported as odds ratio; 95% confidence interval; 0.75; 0.57–0.98), antidepressants (0.83; 0.70–0.97), second-generation antipsychotics (0.67; 0.52–0.86), and first-generation antipsychotics (0.65; 0.52–0.81) were associated with a reduced risk of CRC compared to patients who were not exposed. When considering specific drugs, carbamazepine (0.34; 0.12–0.95), valproic acid (0.66; 0.46–0.95), gabapentin (0.44; 0.20–0.99), fluoxetine (0.82; 0.68–0.99), paroxetine (0.63; 0.45–0.87), and venlafaxine (0.72; 0.55–0.95) were associated with a lower risk of CRC.

Conclusion Exposure to psychotropic drugs in patients with affective disorders is associated with a lower risk of CRC compared to those who were not exposed. Although the causal relationship between psychotropic drug exposure and reduced risk of CRC could not be inferred directly, these findings may help clinicians and patients in clinical decision-making.

Introduction

Colorectal cancer (CRC) is the third most commonly diagnosed malignancy (6.1% of the total cases) and is the second most deadly cancer (9.2% of the total cases) worldwide [1]. The common risk factors of CRC are obesity, cigarette smoking, alcohol consumption, physical inactivity, a diet high in red and processed meat, diabetes mellitus, and low socioeconomic status [1]. These lifestyle characteristics are also frequently observed in patients with major depressive disorder (MDD) and bipolar disorder (BD) with depressed episodes [2]. Besides, several factors, including psychological stress, circadian disruption, exacerbated inflammation, and oxidative stress, are interrelated between cancer and depression [2]. Previous literature also suggested that patients with BD and MDD would have increased 1.24-fold and 1.15-fold risks of overall cancers (BD: relative risk [RR]=1.24, 95% confidence intervals [CIs]=1.05 to 1.46; MDD: RR=1.15, 95%CIs=1.09 to 1.22) [3] [4]. However, BD/MDD are conditions that require long-term medication, and the effects of these medications also need to be taken into consideration.

Several population-based epidemiologic studies have reported that exposure to psychotropic drugs, including antidepressants [5] [6] [7], mood stabilizers [8], and antipsychotics [9] [10] were associated with a lower risk of CRC compared to controls. Importantly, findings from in vitro studies suggested an antitumor effect of psychotropic drugs. For example, sertraline, a selective serotonin reuptake inhibitor (SSRI), induced a dose-dependent inhibition of cell viability and proliferation in the two human colon cancer cell lines, and this effect was similar to or superior to that of doxorubicin, vincristine, or 5-fluorouracil [11]. In vivo, sertraline also significantly inhibited tumor growth on the mice xenografted tumor cells [8]. Another in vitro study reported that carbamazepine and valproic acid had histone deacetylase inhibitor properties and decreased the level of β-catenin and vascular endothelial growth factor (VEGF) on SW480 colon cancer cell lines, which were characteristics of antitumor effects [12]. Chen et al. conducted an epidemiological survey and found a lower risk of colon cancer in people exposed to antipsychotics compared to non-exposed individuals [6]. They also conducted another in vitro study and found that risperidone, an atypical antipsychotic, exhibited the effect of inducing apoptosis and elevating intracellular reactive oxygen species in human colon cancer cells [9]. In contrast, a Finish case-control study reported a weak positive association between non-SSRI antidepressants and the incidence of colon cancer [13]. However, this finding might be subject to bias, particularly considering that additional analyses, such as (1) limiting the population to those with longer than 4 years of follow-up, and (2) restricting the antidepressant group to individuals with over 90 defined daily dose (DDD) of antidepressant exposure, both revealed no significant association.

BD and MDD are major affective disorders, and the lifetime prevalence rates of BD and MDD are 2.63% [14] and 20.6% [15], respectively. Untreated BD and MDD can lead to recurrent mood episodes, further contributing to cognitive, social, and occupational impairments [16] [17]. Meta-analytic studies have implied a positive association of BD and MDD with an increased risk of cancer [3] [4]. Besides, patients with BD and MDD have a higher prevalence of alcohol consumption, smoking, and obesity compared to the general population [18] [19] [20] [21] [22]. In contrast, antidepressants, mood stabilizers, and antipsychotics are commonly used psychotropic drugs in BD and MDD treatment, and these drugs have been suggested to have anticancer effects. Compared to the general population, patients with BD/MDD are more likely to take long-term psychotropic agents regularly to control mood symptoms. To date, few studies have assessed the association between the risk of CRC and psychotropic drug exposure in patients with BD or MDD.

The gap in current knowledge was addressed by conducting a nationwide population-based case-control study to investigate the association between psychotropic drug exposure and the risk of CRC in patients with affective disorders. Specifically, we examined 14 antipsychotics, seven mood stabilizers, and 11 antidepressants, calculating the cumulative defined daily dose for subgroup analyses to determine which psychotropic agents may have anticancer effects and whether a dose-dependent relationship exists. This is likely the first nationwide study to investigate the relationship between individual types and doses of psychotropics and CRC. We hypothesize that exposure to psychotropic drugs is associated with a reduced risk of CRC in patients with affective disorders. This potential anticancer effect of psychotropic agents could offer clinicians a compelling rationale for encouraging regular medication adherence among these patients.

Materials and Methods

Data source

Taiwan National Health Insurance Research Database (NHIRD), which consists of healthcare data from more than 99% of the Taiwanese population (more than a million people) at the end of 2010, was audited and released by the National Health Research Institute for scientific and study purposes [23] [24] [25]. The database includes comprehensive information on insured individuals, such as demographic data, dates of clinical visits/ admission, disease diagnoses, and medical interventions (including medication and surgery). Individual medical records included in the NHIRD are anonymized to protect patient privacy. The NHIRD has been used in numerous epidemiological studies in Taiwan [26] [27] [28] [29]. The diagnostic codes used in the present study are based on the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM). The Institutional Review Board of Taipei Veterans General Hospital approved the study protocol [2018–07–016AC] and waived the requirement for informed consent because this investigation used de-identified data, and no human subject contact was required.

Study participants

The present study employed a nested case-control design utilizing data from the NHIRD from 2001 to 2011. Both the cases and controls were drawn from a predetermined cohort. The defined cohort included people aged≥20 years with psychiatrist-diagnosed BD (ICD-9-CM codes: ICD-9-CM codes: 296.0x, 296.1x, 296.4x, 296.5x, 296.6x, 296.7x, 296.80, 296.81, 296.89) or MDD (ICD-9-CM codes: 296.2x and 296.3x) with no prior history of any malignant cancer. The enrollment time was defined as the date of the diagnosis of BD/MDD between 2000 and 2011, and the endpoint time was defined as the date of the CRC diagnosis within the same period. The follow-up period was the duration between enrollment time and the endpoint. Importantly, the first time diagnosis of BD/MDD could have taken place before 2000. The cases were those who were subsequently diagnosed with a malignant CRC (ICD-9-CM codes 153 and 154) from the enrollment until the end of 2011 or until death. The controls were 10:1 matched with the case cohort based on birth date (±365 days), diagnoses (bipolar disorder and major depressive disorder) and diagnosis time (±365 days), follow-up duration, medical and mental comorbidities (hypertension, dyslipidemia, diabetes mellitus, obesity, smoking, and alcohol and substance use disorders), income, and residence. The controls were also selected from those with BD or MDD who had no recorded diagnosis of any malignant cancer in the database between 2000 and 2011. Income level (levels 1–3 per month:≤19,100 NTD (New Taiwanese Dollars), 19,001~42,000 NTD, and>42,000 NTD) and urbanization level of residence (levels 1–5, most to least urbanized) were assessed as the proxy for healthcare availability in Taiwan [30]. Additionally, the Charlson Comorbidity Index (CCI) and all-cause clinical visits were provided for the study and the matched-control cohorts. The CCI, consisting of 22 physical conditions (cancer was excluded in the current study), was also assessed to determine the systemic health conditions of all enrolled subjects [31]. The study design is illustrated in [Fig. 1].

Exposure to psychotropic medications

Information regarding prescribed drugs (according to the World Health Organization Anatomical Therapeutic Chemical classification system), drug dosage, supply days, and the number of dispensed drug pills was extracted from the NHIRD. The DDD recommended by the World Health Organization is a unit for measuring the prescribed amount of a drug. The DDD is the assumed average daily maintenance dose of a drug consumed for its main indication. We calculated the sum of the dispensed DDD (cumulative DDD [cDDD]) of all antipsychotics (chlorpromazine, clotiapine, flupentixol, fluphenazine, haloperidol, loxapine, sulpiride, aripiprazole, risperidone, olanzapine, amisulpride, ziprasidone, clozapine, and quetiapine), mood stabilizers (lithium, carbamazepine, oxcarbazepine, valproate, lamotrigine, topiramate, and gabapentin), and antidepressants (fluoxetine, sertraline, paroxetine, fluvoxamine, citalopram, escitalopram, venlafaxine, duloxetine, milnacipran, bupropion, and mirtazapine) during the follow-up period. Based on the cDDD, medication use patterns were separated into four subgroups: cDDD of<30, cDDD of 30 to 179, cDDD of 180 to 364, and cDDD of>364 to detect the dose-dependent effect. We calculated the cDDD to assess the cumulative drug exposure, and this method has been used in previous studies using NHIRD [32] [33] [34].

Statistical analysis

For between-group comparisons, the independent t-test was used for continuous variables and Pearson’s X² test was used for nominal variables, where appropriate. Logistic regression analyses adjusted for demographic characteristics, medical and mental comorbidities, CCI scores, and all-cause clinical visits were performed to calculate the odds ratio (OR) and 95% confidence interval (CI) of the association between psychotropic medication use (cDDD categories:<30, 30–179, 180–364, and>364) and subsequent CRC risk. Regarding the over 100 association comparisons that would be performed, the p-value would be adjusted from 0.05 to 0.0005 in our study. A 2-tailed P value<0.05 was considered statistically significant. All data processing and statistical analyses were performed using the Statistical Analysis Software (SAS) Version 9.1 (SAS Institute, Cary, NC).

Data Availability Statement

The NHIRD was released and audited by the Department of Health and the Bureau of the NHI Program for Scientific Research (https://nhird.nhri.org.tw/). The NHIRD can be obtained through a formal application regulated by the Department of Health and Bureau of the NHI Program.

Results

Characteristics of the 1209 patients diagnosed as CRC (62.4±13.3 years) and 12090 matched controls (62.3±13.2 years) are presented in [Table 1]. The CRC cohort did not differ from the control cohort in terms of age, sex, follow-up duration, proportion of BD and MDD, urbanization level, and income ([Table 1]). Except for dyslipidemia (25.0% vs. 28.7%, p<0.001), the CRC cohort did not have different proportions of medical comorbidities compared to the control cohort ([Table 1]). However, compared to the control cohort, the CRC cohort had a higher CCI score (3.1±2.0 vs. 1.7±1.7, p<0.001) and more occurrences of all-cause clinical visits (22.8±17.5 vs. 20.5±19.0, p<0.001). The exposure numbers of medications between groups are presented in s[Table 1].

The association between CRC risk and mood stabilizer and antidepressant use are presented in [Fig. 2] and sTable 2. Compared to individuals not exposed to mood stabilizers (cDDD<30), patients exposed to low cumulative dose mood stabilizers (cDDD,≥30,<180) had a lower likelihood of a diagnosis of CRC (Odd ratio, OR, 0.75; 95% confidence interval, CI, 0.57–0.98). Compared to individuals not exposed to antidepressants (cDDD<30), patients exposed to high cumulative dose antidepressants (cDDD≥365) had a lower likelihood of being diagnosed with CRC (OR, 0.83; 95%CI, 0.70–0.97). When considering specific medications, exposed to moderate cumulative dose (cDDD,≥180,<365) carbamazepine (OR, 0.34; 95%CI, 0.12–0.95), low cumulative dose valproic acid (OR, 0.66; 95%CI, 0.46–0.95), low cumulative dose gabapentin (OR, 0.44; 95%CI, 0.20–0.99), low cumulative dose fluoxetine (OR, 0.82; 95%CI, 0.68–0.99), high cumulative dose paroxetine (OR, 0.63; 95%CI, 0.45–0.87), and low cumulative dose venlafaxine (OR, 0.72; 95%CI, 0.55–0.95) were associated with a lower likelihood of a diagnosis of CRC, compared to people who were not exposed.

[Fig. 3] and sTable 3 summarize the risk of CRC among patients exposed to second-generation antipsychotics (SGAs) and first-generation antipsychotics (FGAs). Compared to individuals not exposed to SGA, those exposed to low cumulative dose SGA (OR, 0.67; 95%CI, 0.52–0.86) had a lower likelihood of a diagnosis of CRC. Likewise, compared to individuals not exposed to FGA, those exposed to low cumulative dose FGA (OR, 0.65; 95%CI, 0.52–0.81) had a lower likelihood of a diagnosis of CRC.

We performed additional sensitivity analyses for the risk of CRC in patients exposed to mood stabilizers, antidepressants (sTable 4), SGA, and FGA (sTable 5) based on cumulative prescription days (<30, 30~179, 180~364,≥365 days), and found consistent findings.

Discussion

This nationwide, population-based, case-control study demonstrated the association between CRC risk and exposure to psychotropic drugs among patients with BD and MDD. Compared to people who were not exposed, we found a 0.75-fold lower likelihood of a diagnosis of CRC with exposure to low cumulative dose mood stabilizers, a 0.83 lower risk of CRC with exposure to high cumulative dose antidepressants, a 0.67-fold lower likelihood of a diagnosis of CRC with exposure to low cumulative dose SGA, and a 0.65-fold lower likelihood of a diagnosis of CRC with exposure to low cumulative dose FGA. When considering specific drugs, carbamazepine, valproic acid, gabapentin, fluoxetine, paroxetine, venlafaxine, and risperidone were associated with a reduced risk of CRC compared to people who were not exposed. Although we did not observe a dose-dependent anticancer effect of the psychotropic agent exposure, the mild positive findings of their potential anticancer effect might still encourage patients with affective disorder to enhance drug compliance.

Mood stabilizers

An epidemiological study including data from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and Japan Medical Data Center reported that a lower risk of CRC was associated with exposure to carbamazepine, valproic acid, and sodium channel-blocking anti-epilepsy drugs (phenytoin, carbamazepine, lamotrigine, topiramate, valproic acid, and ethotoin) as a group [8], consistent with our results. Voltage-gated sodium channels (VGSCs) are expressed in the metastatic cells of a number of cancers, where they enhance cellular migration and invasion [35]. Thus, sodium channel blockage might be an effective anticancer strategy. Another sodium channel blocker, phenytoin, and an anti-VEGF monoclonal antibody, bevacizumab, reportedly have anticancer effects in in vitro studies [36] [37]. In addition to their sodium channel blocking properties, carbamazepine and valproic acid have also been found to inhibit histone deacetylases [12]. Histone deacetylase inhibitors have been proven to be anticancer agents that arrest the cell cycle, inhibit differentiation, inhibit angiogenesis, and induce apoptosis pathways [38]. In vivo and in vitro studies have also investigated the potential of carbamazepine and valproic acid as anticancer agents candidates use alone or combined with other anticancer agents [12] [39] [40] [41] [42]. Although high doses of gabapentin have been reported to be associated with pancreatic acinar cell tumors in an animal study [43], a later study opposed this result, and even reported an anticancer effect of gabapentin by increasing superoxide dismutase activity and modulating immune response [44]. Although gabapentin is a well-known voltage-gated calcium channel inhibitor, it also presented a voltage-gated sodium channel blocking property [45]. It has been shown to suppress invasion and metastasis of prostate cancer cells in an animal model [46]. Lithium, another commonly used mood stabilizer with different mechanisms, has also been reported to be associated with a lower risk of overall cancer in epidemiology studies [3] [47]. Furthermore, lithium was found to inhibit glycogen synthase kinase-3, which regulates many cellular processes and over-expresses in different cancer types [3] [48]. However, in our findings, lithium did not outperform the other psychotropic drugs.

Antidepressants

Case-control studies have shown a decreased risk of CRC in patients exposed to SSRIs [6] [7]; however, few epidemiological studies have investigated the risk of CRC in patients exposed to serotonin-norepinephrine reuptake inhibitors (SNRIs). Only a case-control study reported that treatment with SNRI was not statistically associated with the risk of CRC [49]. In contrast, we found that venlafaxine was associated with a decreased risk of CRC. Kuwahara et al. compared the anticancer effects of antidepressants, including four SSRIs, namely sertraline, paroxetine, fluvoxamine, and escitalopram, and two SNRIs, namely milnacipran and duloxetine in human hepatocellular carcinoma cells, and reported that all of these drugs, except milnacipran, decrease cell viability in a dose-dependent manner [50]. Unfortunately, Kuwahara et al.’s study did not investigate venlafaxine [50]. Besides epidemiology studies [6] [7], our findings of a decreased risk of CRC in affective patients exposed to fluoxetine or paroxetine were consistent with previous experimental studies [51] [52] [53]. In addition to the management of depression, anxiety, and neuropathy induced by chemotherapy [54] [55], the antidepressants seemed to provide novel anticancer benefits for patients with cancer.

Antipsychotics

Epidemiological studies have demonstrated a decreased incidence of cancer in schizophrenia patients treated with antipsychotics, suggesting possible cancer-protective benefits [10] [56]. Several experimental studies have also shown different anticancer mechanisms of different antipsychotics. For example, aripiprazole inhibits cell proliferation and induces apoptosis in breast cancer cell line through the regulation of apoptosis-related genes, including BCL2L1, C-myc, BCL-10, and BAK [57]. Another antipsychotic, haloperidol, induces apoptosis, suppresses cell migration, and increases caspase-8 activation in a dose-dependent manner when used in combination with temozolomide and radiation therapy [58]. In our antipsychotic results, risperidone was significantly associated with a lower risk of CRC, which was consistent with the findings of Chen et al. [9]. Chen et al. compared the anticancer effect of risperidone to that of other antipsychotics, including clozapine, flupentixol, and quetiapine, in colon tumor cell lines and found that risperidone had the strongest cell toxic effect in a dose-dependent manner and elevated intracellular reactive oxygen species [9]. In addition, Dilly et al. reported that a lipid formulation of risperidone, VAL401, retarded the proliferation of prostate cancer cells in vitro and in vivo, and they further applied VAL401 as an anticancer drug in phase II trials in patients with non-small cell adenocarcinoma of the lung [59] [60].

Limitations

This study has several limitations and requires careful evaluation. First, the defined daily dose identified by the NHIRD depended on the filled prescriptions used as the assumed mean dose during the follow-up period. This did not reflect the patients’ drug compliance or the actual dosage of the medication. Furthermore, due to the limitations of follow-up time (2001–2011), participants’ drug exposure before 2001 could not be assessed, including the affective disorder group and the control group. Second, psychotropic drug prescriptions for BD and MDD usually involve multi-pharmacy. However, we only considered the drug exposure independently; situations involving multiple-psychotropic drug exposures were not evaluated. The synergic effect of combination therapy could not be assessed. Third, due to limitations in the NHIRD, we could not assess certain confounding factors, such as substance/alcohol/cigarette exposure, diet style, exercise habits, blood sugar control, and other environmental factors, which also influence the development of CRC. We could still note a significantly higher CCI score (without including the index cancers) in the CRC group, even though we took common medical comorbidities, including hypertension, dyslipidemia, diabetes, obesity, and smoking into account. This situation might further imply that those BD/MDD patients with CRC would have some unhealthy habits leading to higher prevalence of physical comorbidities (higher CCI score), even though we’ve adjusted for the CCI score in statistics. Furthermore, the healthy user effect might also bias our findings. Those BD/MDD patients who took medication and visited clinics regularly may be more likely to engage in other healthy behaviors such as exercise, a high fiber diet, routine colonoscopy, and so forth that are associated with decreased risk of CRC compared to those BD/DD patients who did not. At the very least, we have adjusted for basic demographics, medical comorbidities, and all-cause clinic visits in an attempt to reduce the impact of healthy user bias. Fourth, with the improvement of quality of cancer detection and public awareness of CRC, our findings might not reflect the current situation with a decade old data. Fifth, importantly, we did not find a significant dose-dependent anticancer effect of any one of the psychotropic drugs. One of the possible explanations was the relatively small sample sizes in each drug/cumulated dose group limited the statistical power. With the limitation of the dataset, drug adherence and multi-pharmacy might interfere with our ability to estimate actual drug consumption, which could, in turn, affect the results. Poor medication adherence may lead to an underestimation of the anticancer effects of psychotropics, especially among those assigned to the high-dose group. Sixth, over 100 comparisons had been made. If we revised the p-value from 0.05 to 0.0005, only 30~179 cDDD of any FGA exposure on the lower colon cancer risk achieved statistical significance (p=0.00015). Seventh, the sample sizes of exposure to several medications, such as amisulpiride (n=8 in the case group and n=101 in the control group), lamotrigine (n=10 in the case group and n=115 in the control group), and milnacipran (n=9 in the case group and n=98 in the control group), may limit the statistical power of our analyses. Further studies with large samples of exposure to those medications would be required. Finally, the causal relationship between psychotropic drugs and CRC development could not be fully inferred using a case-control study design.

Conclusion

We found a reduced likelihood of a CRC diagnosis in patients with affective disorders who were exposed to mood stabilizers, antidepressants, or antipsychotics. Our findings were consistent with those of previous experimental studies on the anticancer effects of psychotropic drugs. In conclusion, we suggest that psychiatric medications are not associated with a higher risk of CRC. Although the causal relationship between psychotropic drug exposure and reduced risk of CRC could not be inferred directly, the findings might support that patients with MDD/BD should have better medication adherence, not only to manage emotional symptoms but also to potentially reduce the subsequent incidence of CRC. Clinicians will also have an additional basis when explaining medications, which can help persuade patients to maintain drug adherence. To improve the study design and better establish causality, future research could benefit from prospective cohort studies, where patients are followed over time to observe the development of CRC in relation to psychotropic drug use. This would help minimize recall bias and offer more robust temporal information.

Data availability

As participants did not consent to the option of having their data publicly shared, or even anonymized, data will be made available to only potential collaborators with ethical approval after they submit a research proposal to Bureau of the NHI (https://nhird.nhri.org.tw/).

Financial Disclosure

All authors have no financial relationships relevant to this article to disclose.

Authors contributions

Dr M.H.C. and Dr C.S.L. designed the study, and wrote the protocol. Dr T.W.H. drafted the manuscript. And Dr M.H.C. analyzed the data. Dr. C.S.L and Dr S.J.T. assisted with the preparation and proof-reading of the manuscript. Dr T.J.C. provided the advice on statistical analysis. All authors agreed on the submission and publication of this paper.

Conflict of Interest

The authors declare that they have no conflict of interest.

Acknowledgement

The authors thank Mr I-Fan Hu, MA (Courtauld Institute of Art, University of London; National Taiwan University) for his support. Mr Hu declares no conflicts of interest.

^# Mu-Hung Chen and Chih-Sung Liang are co-corresponding authors of this paper and have contributed equally in this capacity.

• References

• 1 Sawicki T, Ruszkowska M, Danielewicz A. et al. A review of colorectal cancer in terms of epidemiology, risk factors, development, symptoms and diagnosis. Cancers (Basel) 2021; 13
  CrossrefPubMedSearch in Google Scholar
• 2 Fraile-Martinez O, Alvarez-Mon MA, Garcia-Montero C. et al. Understanding the basis of major depressive disorder in oncological patients: Biological links, clinical management, challenges, and lifestyle medicine. Front Oncol 2022; 12: 956923
  CrossrefPubMedSearch in Google Scholar
• 3 Anmella G, Fico G, Lotfaliany M. et al. Risk of cancer in bipolar disorder and the potential role of lithium: International collaborative systematic review and meta-analyses. Neurosci Biobehav Rev 2021; 126: 529-541
  CrossrefPubMedSearch in Google Scholar
• 4 Jia Y, Li F, Liu YF. et al. Depression and cancer risk: A systematic review and meta-analysis. Public Health 2017; 149: 138-148
  CrossrefPubMedSearch in Google Scholar
• 5 Chubak J, Boudreau DM, Rulyak SJ. et al. Colorectal cancer risk in relation to antidepressant medication use. Int J Cancer 2011; 128: 227-232
  CrossrefPubMedSearch in Google Scholar
• 6 Coogan PF, Strom BL, Rosenberg L. Antidepressant use and colorectal cancer risk. Pharmacoepidemiol Drug Saf 2009; 18: 1111-1114
  CrossrefPubMedSearch in Google Scholar
• 7 Xu W, Tamim H, Shapiro S. et al. Use of antidepressants and risk of colorectal cancer: A nested case-control study. Lancet Oncol 2006; 7: 301-308
  CrossrefPubMedSearch in Google Scholar
• 8 Takada M, Fujimoto M, Motomura H. et al. Inverse association between sodium channel-blocking antiepileptic drug use and cancer: Data mining of spontaneous reporting and claims databases. Int J Med Sci 2016; 13: 48-59
  CrossrefPubMedSearch in Google Scholar
• 9 Chen VC, Hsieh YH, Lin TC. et al. New use for old drugs: The protective effect of risperidone on colorectal cancer. Cancers (Basel) 2020; 12
  CrossrefPubMedSearch in Google Scholar
• 10 Dalton SO, Johansen C, Poulsen AH. et al. Cancer risk among users of neuroleptic medication: A population-based cohort study. Br J Cancer 2006; 95: 934-939
  CrossrefPubMedSearch in Google Scholar
• 11 Gil-Ad I, Zolokov A, Lomnitski L. et al. Evaluation of the potential anti-cancer activity of the antidepressant sertraline in human colon cancer cell lines and in colorectal cancer-xenografted mice. Int J Oncol 2008; 33: 277-286
  PubMedSearch in Google Scholar
• 12 Akbarzadeh L, Moini Zanjani T, Sabetkasaei M. Comparison of anticancer effects of carbamazepine and valproic acid. Iran Red Crescent Med J 2016; 18: e37230
  CrossrefPubMedSearch in Google Scholar
• 13 Haukka J, Sankila R, Klaukka T. et al. Incidence of cancer and antidepressant medication: Record linkage study. Int J Cancer 2010; 126: 285-296
  CrossrefPubMedSearch in Google Scholar
• 14 Clemente AS, Diniz BS, Nicolato R. et al. Bipolar disorder prevalence: A systematic review and meta-analysis of the literature. Braz J Psychiatry 2015; 37: 155-161
  CrossrefPubMedSearch in Google Scholar
• 15 Hasin DS, Sarvet AL, Meyers JL. et al. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiatry 2018; 75: 336-346
  CrossrefPubMedSearch in Google Scholar
• 16 Hammer-Helmich L, Haro JM, Jonsson B. et al. Functional impairment in patients with major depressive disorder: The 2-year PERFORM study. Neuropsychiatr Dis Treat 2018; 14: 239-249
  CrossrefPubMedSearch in Google Scholar
• 17 Peters AT, West AE, Eisner L. et al. The burden of repeated mood episodes in bipolar I disorder: Results from the National Epidemiological Survey on Alcohol and Related Conditions. J Nerv Ment Dis 2016; 204: 87-94
  CrossrefPubMedSearch in Google Scholar
• 18 Dickerson F, Stallings CR, Origoni AE. et al. Cigarette smoking among persons with schizophrenia or bipolar disorder in routine clinical settings, 1999-2011. Psychiatr Serv 2013; 64: 44-50
  CrossrefPubMedSearch in Google Scholar
• 19 Liu YK, Ling S, Lui LMW. et al. Prevalence of type 2 diabetes mellitus, impaired fasting glucose, general obesity, and abdominal obesity in patients with bipolar disorder: A systematic review and meta-analysis. J Affect Disord 2022; 300: 449-461
  CrossrefPubMedSearch in Google Scholar
• 20 Luppino FS, de Wit LM, Bouvy PF. et al. Overweight, obesity, and depression: A systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry 2010; 67: 220-229
  CrossrefPubMedSearch in Google Scholar
• 21 Mathew AR, Hogarth L, Leventhal AM. et al. Cigarette smoking and depression comorbidity: Systematic review and proposed theoretical model. Addiction 2017; 112: 401-412
  CrossrefPubMedSearch in Google Scholar
• 22 Puddephatt JA, Irizar P, Jones A. et al. Associations of common mental disorder with alcohol use in the adult general population: A systematic review and meta-analysis. Addiction 2022; 117: 1543-1572
  CrossrefPubMedSearch in Google Scholar
• 23 Huang JS, Yang FC, Chien WC. et al. Risk of substance use disorder and its associations with comorbidities and psychotropic agents in patients with autism. JAMA Pediatr 2021; 175: e205371
  CrossrefPubMedSearch in Google Scholar
• 24 Liang CS, Bai YM, Hsu JW. et al. The risk of sexually transmitted infections following first-episode schizophrenia among adolescents and young adults: A cohort study of 220 545 subjects. Schizophr Bull 2020; 46: 795-803
  CrossrefPubMedSearch in Google Scholar
• 25 Hsu TW, Liang CS, Tsai SJ. et al. Risk of major psychiatric disorders among children and adolescents surviving malignancies: A nationwide longitudinal study. J Clin Oncol 2023; 41: 2054-2066
  CrossrefPubMedSearch in Google Scholar
• 26 Chen MH, Hsu JW, Huang KL. et al. Risk and coaggregation of major psychiatric disorders among first-degree relatives of patients with bipolar disorder: A nationwide population-based study. Psychol Med 2019; 49: 2397-2404
  CrossrefPubMedSearch in Google Scholar
• 27 Chen MH, Lan WH, Hsu JW. et al. Risk of developing type 2 diabetes in adolescents and young adults with autism spectrum disorder: A nationwide longitudinal study. Diabetes care 2016; 39: 788-793
  CrossrefPubMedSearch in Google Scholar
• 28 Cheng CM, Chang WH, Chen MH. et al. Co-aggregation of major psychiatric disorders in individuals with first-degree relatives with schizophrenia: A nationwide population-based study. Molecular psychiatry 2018; 23: 1756-1763
  CrossrefPubMedSearch in Google Scholar
• 29 Huang MH, Cheng CM, Tsai SJ. et al. Familial coaggregation of major psychiatric disorders among first-degree relatives of patients with obsessive-compulsive disorder: A nationwide study. Psychol Med 2020; 1-8
  CrossrefPubMedSearch in Google Scholar
• 30 Liu CY, Hung YT, Chuang YL. et al. Incorporating development stratification of Taiwan townships into sampling design of large scale health interview survey. J Health Management (Chin) 2006; 4: 1-22
  PubMedSearch in Google Scholar
• 31 Charlson ME, Pompei P, Ales KL. et al. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis 1987; 40: 373-383
  CrossrefPubMedSearch in Google Scholar
• 32 Hsu TW, Bai YM, Tsai SJ. et al. Risk of retinal disease in patients with bipolar disorder: A nationwide cohort study. Psychiatry Clin Neurosci 2022; 76: 106-113
  CrossrefPubMedSearch in Google Scholar
• 33 Hsu TW, Chen MH, Chu CS. et al. Attention deficit hyperactivity disorder and risk of migraine: A nationwide longitudinal study. Headache 2022; 62: 634-641
  CrossrefPubMedSearch in Google Scholar
• 34 Kuo HY, Liang CS, Tsai SJ. et al. Dose-dependent proton pump inhibitor exposure and risk of type 2 diabetes: A nationwide nested case-control study. Int J Environ Res Public Health 2022; 19
  CrossrefPubMedSearch in Google Scholar
• 35 Brackenbury WJ. Voltage-gated sodium channels and metastatic disease. Channels (Austin) 2012; 6: 352-361
  CrossrefPubMedSearch in Google Scholar
• 36 Wheler JJ, Janku F, Falchook GS. et al. Phase I study of anti-VEGF monoclonal antibody bevacizumab and histone deacetylase inhibitor valproic acid in patients with advanced cancers. Cancer Chemother Pharmacol 2014; 73: 495-501
  CrossrefPubMedSearch in Google Scholar
• 37 Yang M, Kozminski DJ, Wold LA. et al. Therapeutic potential for phenytoin: Targeting Na(v)1.5 sodium channels to reduce migration and invasion in metastatic breast cancer. Breast Cancer Res Treat 2012; 134: 603-615
  CrossrefPubMedSearch in Google Scholar
• 38 Kim HJ, Bae SC. Histone deacetylase inhibitors: Molecular mechanisms of action and clinical trials as anti-cancer drugs. Am J Transl Res 2011; 3: 166-179
  PubMedSearch in Google Scholar
• 39 Ahmadi R, Sepehri B, Irani M. et al. In-silico optimization of frizzled-8 receptor inhibition activity of carbamazepine: Design new anti-cancer agent. Comb Chem High Throughput Screen 2023; 26: 696-705
  CrossrefPubMedSearch in Google Scholar
• 40 Beutler AS, Li S, Nicol R. et al. Carbamazepine is an inhibitor of histone deacetylases. Life Sci 2005; 76: 3107-3115
  CrossrefPubMedSearch in Google Scholar
• 41 Bilen MA, Fu S, Falchook GS. et al. Phase I trial of valproic acid and lenalidomide in patients with advanced cancer. Cancer Chemother Pharmacol 2015; 75: 869-874
  CrossrefPubMedSearch in Google Scholar
• 42 Chavez-Blanco A, Perez-Plasencia C, Perez-Cardenas E. et al. Antineoplastic effects of the DNA methylation inhibitor hydralazine and the histone deacetylase inhibitor valproic acid in cancer cell lines. Cancer Cell Int 2006; 6: 2
  CrossrefPubMedSearch in Google Scholar
• 43 Sigler RE, Gough AW, de la Iglesia FA. Pancreatic acinar cell neoplasia in male Wistar rats following 2 years of gabapentin exposure. Toxicology 1995; 98: 73-82
  CrossrefPubMedSearch in Google Scholar
• 44 da Cunha LP, Rey MEC, Jorge DCR. et al. High dose gabapentin does not alter tumor growth in mice but reduces arginase activity and increases superoxide dismutase, IL-6 and MCP-1 levels in Ehrlich ascites. BMC Res Notes 2019; 12: 59
  CrossrefPubMedSearch in Google Scholar
• 45 Zhang JL, Yang JP, Zhang JR. et al. Gabapentin reduces allodynia and hyperalgesia in painful diabetic neuropathy rats by decreasing expression level of Nav1.7 and p-ERK1/2 in DRG neurons. Brain Res 2013; 1493: 13-18
  CrossrefPubMedSearch in Google Scholar
• 46 Bugan I, Karagoz Z, Altun S. et al. Gabapentin, an analgesic used against cancer-associated neuropathic pain: Effects on prostate cancer progression in an in vivo rat model. Basic Clin Pharmacol Toxicol 2016; 118: 200-207
  CrossrefPubMedSearch in Google Scholar
• 47 Huang RY, Hsieh KP, Huang WW. et al. Use of lithium and cancer risk in patients with bipolar disorder: Population-based cohort study. Br J Psychiatry 2016; 209: 393-399
  CrossrefPubMedSearch in Google Scholar
• 48 Suganthi M, Sangeetha G, Gayathri G. et al. Biphasic dose-dependent effect of lithium chloride on survival of human hormone-dependent breast cancer cells (MCF-7). Biol Trace Elem Res 2012; 150: 477-486
  CrossrefPubMedSearch in Google Scholar
• 49 Boursi B, Lurie I, Mamtani R. et al. Anti-depressant therapy and cancer risk: A nested case-control study. Eur Neuropsychopharmacol 2015; 25: 1147-1157
  CrossrefPubMedSearch in Google Scholar
• 50 Kuwahara J, Yamada T, Egashira N. et al. Comparison of the anti-tumor effects of selective serotonin reuptake inhibitors as well as serotonin and norepinephrine reuptake inhibitors in human hepatocellular carcinoma cells. Biol Pharm Bull 2015; 38: 1410-1414
  CrossrefPubMedSearch in Google Scholar
• 51 Jang WJ, Jung SK, Vo TTL. et al. Anticancer activity of paroxetine in human colon cancer cells: Involvement of MET and ERBB3. J Cell Mol Med 2019; 23: 1106-1115
  CrossrefPubMedSearch in Google Scholar
• 52 Kannen V, Hintzsche H, Zanette DL. et al. Antiproliferative effects of fluoxetine on colon cancer cells and in a colonic carcinogen mouse model. PLoS One 2012; 7: e50043
  CrossrefPubMedSearch in Google Scholar
• 53 Kannen V, Marini T, Turatti A. et al. Fluoxetine induces preventive and complex effects against colon cancer development in epithelial and stromal areas in rats. Toxicol Lett 2011; 204: 134-140
  CrossrefPubMedSearch in Google Scholar
• 54 Durand JP, Alexandre J, Guillevin L. et al. Clinical activity of venlafaxine and topiramate against oxaliplatin-induced disabling permanent neuropathy. Anticancer Drugs 2005; 16: 587-591
  CrossrefPubMedSearch in Google Scholar
• 55 Rabin EE, Kim M, Mozny A. et al. A systematic review of pharmacologic treatment efficacy for depression in older patients with cancer. Brain Behav Immun Health 2022; 21: 100449
  CrossrefPubMedSearch in Google Scholar
• 56 Fond G, Macgregor A, Attal J. et al. Antipsychotic drugs: Pro-cancer or anti-cancer? A systematic review. Med Hypotheses 2012; 79: 38-42
  CrossrefPubMedSearch in Google Scholar
• 57 Badran A, Tul-Wahab A, Zafar H. et al. Antipsychotics drug aripiprazole as a lead against breast cancer cell line (MCF-7) in vitro. PLoS One 2020; 15: e0235676
  CrossrefPubMedSearch in Google Scholar
• 58 Papadopoulos F, Isihou R, Alexiou GA. et al. Haloperidol induced cell cycle arrest and apoptosis in glioblastoma cells. Biomedicines 2020; 8
  CrossrefPubMedSearch in Google Scholar
• 59 Dilly SJ, Clark AJ, Marsh A. et al. A chemical genomics approach to drug reprofiling in oncology: Antipsychotic drug risperidone as a potential adenocarcinoma treatment. Cancer Lett 2017; 393: 16-21
  CrossrefPubMedSearch in Google Scholar
• 60 Dilly SJ, Morris GS, Taylor PC. et al. Clinical pharmacokinetics of a lipid-based formulation of risperidone, VAL401: Analysis of a single dose in an open-label trial of late-stage cancer patients. Eur J Drug Metab Pharmacokinet 2019; 44: 557-565
  CrossrefPubMedSearch in Google Scholar

Correspondence

Publication History

Received: 06 August 2024
Received: 19 October 2024

Accepted: 12 November 2024

Article published online:
17 December 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag
Rüdigerstraße 14, 70469 Stuttgart, Germany

• References

• 1 Sawicki T, Ruszkowska M, Danielewicz A. et al. A review of colorectal cancer in terms of epidemiology, risk factors, development, symptoms and diagnosis. Cancers (Basel) 2021; 13
  CrossrefPubMedSearch in Google Scholar
• 2 Fraile-Martinez O, Alvarez-Mon MA, Garcia-Montero C. et al. Understanding the basis of major depressive disorder in oncological patients: Biological links, clinical management, challenges, and lifestyle medicine. Front Oncol 2022; 12: 956923
  CrossrefPubMedSearch in Google Scholar
• 3 Anmella G, Fico G, Lotfaliany M. et al. Risk of cancer in bipolar disorder and the potential role of lithium: International collaborative systematic review and meta-analyses. Neurosci Biobehav Rev 2021; 126: 529-541
  CrossrefPubMedSearch in Google Scholar
• 4 Jia Y, Li F, Liu YF. et al. Depression and cancer risk: A systematic review and meta-analysis. Public Health 2017; 149: 138-148
  CrossrefPubMedSearch in Google Scholar
• 5 Chubak J, Boudreau DM, Rulyak SJ. et al. Colorectal cancer risk in relation to antidepressant medication use. Int J Cancer 2011; 128: 227-232
  CrossrefPubMedSearch in Google Scholar
• 6 Coogan PF, Strom BL, Rosenberg L. Antidepressant use and colorectal cancer risk. Pharmacoepidemiol Drug Saf 2009; 18: 1111-1114
  CrossrefPubMedSearch in Google Scholar
• 7 Xu W, Tamim H, Shapiro S. et al. Use of antidepressants and risk of colorectal cancer: A nested case-control study. Lancet Oncol 2006; 7: 301-308
  CrossrefPubMedSearch in Google Scholar
• 8 Takada M, Fujimoto M, Motomura H. et al. Inverse association between sodium channel-blocking antiepileptic drug use and cancer: Data mining of spontaneous reporting and claims databases. Int J Med Sci 2016; 13: 48-59
  CrossrefPubMedSearch in Google Scholar
• 9 Chen VC, Hsieh YH, Lin TC. et al. New use for old drugs: The protective effect of risperidone on colorectal cancer. Cancers (Basel) 2020; 12
  CrossrefPubMedSearch in Google Scholar
• 10 Dalton SO, Johansen C, Poulsen AH. et al. Cancer risk among users of neuroleptic medication: A population-based cohort study. Br J Cancer 2006; 95: 934-939
  CrossrefPubMedSearch in Google Scholar
• 11 Gil-Ad I, Zolokov A, Lomnitski L. et al. Evaluation of the potential anti-cancer activity of the antidepressant sertraline in human colon cancer cell lines and in colorectal cancer-xenografted mice. Int J Oncol 2008; 33: 277-286
  PubMedSearch in Google Scholar
• 12 Akbarzadeh L, Moini Zanjani T, Sabetkasaei M. Comparison of anticancer effects of carbamazepine and valproic acid. Iran Red Crescent Med J 2016; 18: e37230
  CrossrefPubMedSearch in Google Scholar
• 13 Haukka J, Sankila R, Klaukka T. et al. Incidence of cancer and antidepressant medication: Record linkage study. Int J Cancer 2010; 126: 285-296
  CrossrefPubMedSearch in Google Scholar
• 14 Clemente AS, Diniz BS, Nicolato R. et al. Bipolar disorder prevalence: A systematic review and meta-analysis of the literature. Braz J Psychiatry 2015; 37: 155-161
  CrossrefPubMedSearch in Google Scholar
• 15 Hasin DS, Sarvet AL, Meyers JL. et al. Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA Psychiatry 2018; 75: 336-346
  CrossrefPubMedSearch in Google Scholar
• 16 Hammer-Helmich L, Haro JM, Jonsson B. et al. Functional impairment in patients with major depressive disorder: The 2-year PERFORM study. Neuropsychiatr Dis Treat 2018; 14: 239-249
  CrossrefPubMedSearch in Google Scholar
• 17 Peters AT, West AE, Eisner L. et al. The burden of repeated mood episodes in bipolar I disorder: Results from the National Epidemiological Survey on Alcohol and Related Conditions. J Nerv Ment Dis 2016; 204: 87-94
  CrossrefPubMedSearch in Google Scholar
• 18 Dickerson F, Stallings CR, Origoni AE. et al. Cigarette smoking among persons with schizophrenia or bipolar disorder in routine clinical settings, 1999-2011. Psychiatr Serv 2013; 64: 44-50
  CrossrefPubMedSearch in Google Scholar
• 19 Liu YK, Ling S, Lui LMW. et al. Prevalence of type 2 diabetes mellitus, impaired fasting glucose, general obesity, and abdominal obesity in patients with bipolar disorder: A systematic review and meta-analysis. J Affect Disord 2022; 300: 449-461
  CrossrefPubMedSearch in Google Scholar
• 20 Luppino FS, de Wit LM, Bouvy PF. et al. Overweight, obesity, and depression: A systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry 2010; 67: 220-229
  CrossrefPubMedSearch in Google Scholar
• 21 Mathew AR, Hogarth L, Leventhal AM. et al. Cigarette smoking and depression comorbidity: Systematic review and proposed theoretical model. Addiction 2017; 112: 401-412
  CrossrefPubMedSearch in Google Scholar
• 22 Puddephatt JA, Irizar P, Jones A. et al. Associations of common mental disorder with alcohol use in the adult general population: A systematic review and meta-analysis. Addiction 2022; 117: 1543-1572
  CrossrefPubMedSearch in Google Scholar
• 23 Huang JS, Yang FC, Chien WC. et al. Risk of substance use disorder and its associations with comorbidities and psychotropic agents in patients with autism. JAMA Pediatr 2021; 175: e205371
  CrossrefPubMedSearch in Google Scholar
• 24 Liang CS, Bai YM, Hsu JW. et al. The risk of sexually transmitted infections following first-episode schizophrenia among adolescents and young adults: A cohort study of 220 545 subjects. Schizophr Bull 2020; 46: 795-803
  CrossrefPubMedSearch in Google Scholar
• 25 Hsu TW, Liang CS, Tsai SJ. et al. Risk of major psychiatric disorders among children and adolescents surviving malignancies: A nationwide longitudinal study. J Clin Oncol 2023; 41: 2054-2066
  CrossrefPubMedSearch in Google Scholar
• 26 Chen MH, Hsu JW, Huang KL. et al. Risk and coaggregation of major psychiatric disorders among first-degree relatives of patients with bipolar disorder: A nationwide population-based study. Psychol Med 2019; 49: 2397-2404
  CrossrefPubMedSearch in Google Scholar
• 27 Chen MH, Lan WH, Hsu JW. et al. Risk of developing type 2 diabetes in adolescents and young adults with autism spectrum disorder: A nationwide longitudinal study. Diabetes care 2016; 39: 788-793
  CrossrefPubMedSearch in Google Scholar
• 28 Cheng CM, Chang WH, Chen MH. et al. Co-aggregation of major psychiatric disorders in individuals with first-degree relatives with schizophrenia: A nationwide population-based study. Molecular psychiatry 2018; 23: 1756-1763
  CrossrefPubMedSearch in Google Scholar
• 29 Huang MH, Cheng CM, Tsai SJ. et al. Familial coaggregation of major psychiatric disorders among first-degree relatives of patients with obsessive-compulsive disorder: A nationwide study. Psychol Med 2020; 1-8
  CrossrefPubMedSearch in Google Scholar
• 30 Liu CY, Hung YT, Chuang YL. et al. Incorporating development stratification of Taiwan townships into sampling design of large scale health interview survey. J Health Management (Chin) 2006; 4: 1-22
  PubMedSearch in Google Scholar
• 31 Charlson ME, Pompei P, Ales KL. et al. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis 1987; 40: 373-383
  CrossrefPubMedSearch in Google Scholar
• 32 Hsu TW, Bai YM, Tsai SJ. et al. Risk of retinal disease in patients with bipolar disorder: A nationwide cohort study. Psychiatry Clin Neurosci 2022; 76: 106-113
  CrossrefPubMedSearch in Google Scholar
• 33 Hsu TW, Chen MH, Chu CS. et al. Attention deficit hyperactivity disorder and risk of migraine: A nationwide longitudinal study. Headache 2022; 62: 634-641
  CrossrefPubMedSearch in Google Scholar
• 34 Kuo HY, Liang CS, Tsai SJ. et al. Dose-dependent proton pump inhibitor exposure and risk of type 2 diabetes: A nationwide nested case-control study. Int J Environ Res Public Health 2022; 19
  CrossrefPubMedSearch in Google Scholar
• 35 Brackenbury WJ. Voltage-gated sodium channels and metastatic disease. Channels (Austin) 2012; 6: 352-361
  CrossrefPubMedSearch in Google Scholar
• 36 Wheler JJ, Janku F, Falchook GS. et al. Phase I study of anti-VEGF monoclonal antibody bevacizumab and histone deacetylase inhibitor valproic acid in patients with advanced cancers. Cancer Chemother Pharmacol 2014; 73: 495-501
  CrossrefPubMedSearch in Google Scholar
• 37 Yang M, Kozminski DJ, Wold LA. et al. Therapeutic potential for phenytoin: Targeting Na(v)1.5 sodium channels to reduce migration and invasion in metastatic breast cancer. Breast Cancer Res Treat 2012; 134: 603-615
  CrossrefPubMedSearch in Google Scholar
• 38 Kim HJ, Bae SC. Histone deacetylase inhibitors: Molecular mechanisms of action and clinical trials as anti-cancer drugs. Am J Transl Res 2011; 3: 166-179
  PubMedSearch in Google Scholar
• 39 Ahmadi R, Sepehri B, Irani M. et al. In-silico optimization of frizzled-8 receptor inhibition activity of carbamazepine: Design new anti-cancer agent. Comb Chem High Throughput Screen 2023; 26: 696-705
  CrossrefPubMedSearch in Google Scholar
• 40 Beutler AS, Li S, Nicol R. et al. Carbamazepine is an inhibitor of histone deacetylases. Life Sci 2005; 76: 3107-3115
  CrossrefPubMedSearch in Google Scholar
• 41 Bilen MA, Fu S, Falchook GS. et al. Phase I trial of valproic acid and lenalidomide in patients with advanced cancer. Cancer Chemother Pharmacol 2015; 75: 869-874
  CrossrefPubMedSearch in Google Scholar
• 42 Chavez-Blanco A, Perez-Plasencia C, Perez-Cardenas E. et al. Antineoplastic effects of the DNA methylation inhibitor hydralazine and the histone deacetylase inhibitor valproic acid in cancer cell lines. Cancer Cell Int 2006; 6: 2
  CrossrefPubMedSearch in Google Scholar
• 43 Sigler RE, Gough AW, de la Iglesia FA. Pancreatic acinar cell neoplasia in male Wistar rats following 2 years of gabapentin exposure. Toxicology 1995; 98: 73-82
  CrossrefPubMedSearch in Google Scholar
• 44 da Cunha LP, Rey MEC, Jorge DCR. et al. High dose gabapentin does not alter tumor growth in mice but reduces arginase activity and increases superoxide dismutase, IL-6 and MCP-1 levels in Ehrlich ascites. BMC Res Notes 2019; 12: 59
  CrossrefPubMedSearch in Google Scholar
• 45 Zhang JL, Yang JP, Zhang JR. et al. Gabapentin reduces allodynia and hyperalgesia in painful diabetic neuropathy rats by decreasing expression level of Nav1.7 and p-ERK1/2 in DRG neurons. Brain Res 2013; 1493: 13-18
  CrossrefPubMedSearch in Google Scholar
• 46 Bugan I, Karagoz Z, Altun S. et al. Gabapentin, an analgesic used against cancer-associated neuropathic pain: Effects on prostate cancer progression in an in vivo rat model. Basic Clin Pharmacol Toxicol 2016; 118: 200-207
  CrossrefPubMedSearch in Google Scholar
• 47 Huang RY, Hsieh KP, Huang WW. et al. Use of lithium and cancer risk in patients with bipolar disorder: Population-based cohort study. Br J Psychiatry 2016; 209: 393-399
  CrossrefPubMedSearch in Google Scholar
• 48 Suganthi M, Sangeetha G, Gayathri G. et al. Biphasic dose-dependent effect of lithium chloride on survival of human hormone-dependent breast cancer cells (MCF-7). Biol Trace Elem Res 2012; 150: 477-486
  CrossrefPubMedSearch in Google Scholar
• 49 Boursi B, Lurie I, Mamtani R. et al. Anti-depressant therapy and cancer risk: A nested case-control study. Eur Neuropsychopharmacol 2015; 25: 1147-1157
  CrossrefPubMedSearch in Google Scholar
• 50 Kuwahara J, Yamada T, Egashira N. et al. Comparison of the anti-tumor effects of selective serotonin reuptake inhibitors as well as serotonin and norepinephrine reuptake inhibitors in human hepatocellular carcinoma cells. Biol Pharm Bull 2015; 38: 1410-1414
  CrossrefPubMedSearch in Google Scholar
• 51 Jang WJ, Jung SK, Vo TTL. et al. Anticancer activity of paroxetine in human colon cancer cells: Involvement of MET and ERBB3. J Cell Mol Med 2019; 23: 1106-1115
  CrossrefPubMedSearch in Google Scholar
• 52 Kannen V, Hintzsche H, Zanette DL. et al. Antiproliferative effects of fluoxetine on colon cancer cells and in a colonic carcinogen mouse model. PLoS One 2012; 7: e50043
  CrossrefPubMedSearch in Google Scholar
• 53 Kannen V, Marini T, Turatti A. et al. Fluoxetine induces preventive and complex effects against colon cancer development in epithelial and stromal areas in rats. Toxicol Lett 2011; 204: 134-140
  CrossrefPubMedSearch in Google Scholar
• 54 Durand JP, Alexandre J, Guillevin L. et al. Clinical activity of venlafaxine and topiramate against oxaliplatin-induced disabling permanent neuropathy. Anticancer Drugs 2005; 16: 587-591
  CrossrefPubMedSearch in Google Scholar
• 55 Rabin EE, Kim M, Mozny A. et al. A systematic review of pharmacologic treatment efficacy for depression in older patients with cancer. Brain Behav Immun Health 2022; 21: 100449
  CrossrefPubMedSearch in Google Scholar
• 56 Fond G, Macgregor A, Attal J. et al. Antipsychotic drugs: Pro-cancer or anti-cancer? A systematic review. Med Hypotheses 2012; 79: 38-42
  CrossrefPubMedSearch in Google Scholar
• 57 Badran A, Tul-Wahab A, Zafar H. et al. Antipsychotics drug aripiprazole as a lead against breast cancer cell line (MCF-7) in vitro. PLoS One 2020; 15: e0235676
  CrossrefPubMedSearch in Google Scholar
• 58 Papadopoulos F, Isihou R, Alexiou GA. et al. Haloperidol induced cell cycle arrest and apoptosis in glioblastoma cells. Biomedicines 2020; 8
  CrossrefPubMedSearch in Google Scholar
• 59 Dilly SJ, Clark AJ, Marsh A. et al. A chemical genomics approach to drug reprofiling in oncology: Antipsychotic drug risperidone as a potential adenocarcinoma treatment. Cancer Lett 2017; 393: 16-21
  CrossrefPubMedSearch in Google Scholar
• 60 Dilly SJ, Morris GS, Taylor PC. et al. Clinical pharmacokinetics of a lipid-based formulation of risperidone, VAL401: Analysis of a single dose in an open-label trial of late-stage cancer patients. Eur J Drug Metab Pharmacokinet 2019; 44: 557-565
  CrossrefPubMedSearch in Google Scholar

• Supplementary Material