In the present study, we utilized WHO cancer and malaria data to conduct longitudinal (1955–2008) analyses to assess the relationship between these diseases. Our analyses indicate that endemic or epidemic malaria may decrease mortality for some solid cancers, including colon cancer both in men and women, lung and stomach cancer in men, and breast cancer in women. Country-specific cancer incidence and mortality are associated with many factors, such as ethnicity, habits, customs, the level of economic development, the level of health care, and the level of cancer diagnosis and treatment. The geographical distribution of the countries is also a potential confounding factor. Malaria is widely transmitted in Africa, Asia, and Latin America but has a low incidence in industrialized countries. Another potential confounding factor is the effect of time. To reduce the effects of these confounding factors, a GAMM was used to analyze the longitudinal data. A random intercept effect for each country in the GAMM regression analysis was used to accommodate the variations in cancer mortality across different countries. After adjusting for year, life expectancy at birth, national income level, and geographical locations, the inverse relationship between malaria incidence and all-cause cancer mortality persisted. Recent studies have demonstrated that some antimalarial drugs, such as artemisinin and chloroquine, have antitumor activities [12, 13]; thus, malaria treatment in endemic areas may affect cancer mortality, as well. However, although artemisinin was only used in clinics after the 1990s [14, 15], our analysis indicated that the negative relationship between malaria incidence and all-cause cancer mortality existed before the 1990s, during the period 1955–1981. Thus, the clinical use of artemisinin should not be a major confounder. Chloroquine may have been widely used throughout the entire period 1955–2008, but it was used only for short-term (3-day) treatment of malaria; thus, it is unlikely to have significantly affected cancer mortality. Furthermore, our murine Lewis lung cancer (LLC) model studies have demonstrated that 3-day treatment with a clinically relevant dose of chloroquine does not improve the survival of tumor-bearing mice (data not shown). Therefore, it is less likely that the observed inverse relationship between malaria incidence and cancer mortality was caused by the confounding factors discussed above.

Increased levels of economic development and increased life span typically decrease malaria incidence and increase cancer mortality. Thus, in countries with low levels of malaria and controlled endemic malaria, an inverse relationship between malaria incidence and cancer mortality would be observed merely through a simple analysis. To address this issue, we included a year variable spline smoothing term in the model to control for this time effect. We also conducted simulation analyses to estimate class I errors and the statistical power using data from 8 countries with decreasing malaria incidence. The estimated probability of this finding being due to chance was <0.035, and the power to detect our observed effect (−0.03) was 0.752. We also stratified the countries by trends in malaria incidence (up, down or no change). Negative correlations between malaria incidence and cancer mortality were observed in each stratum. Another potential time effect was the changing population structure over time because aging that relates to cancer mortality may also relate to malaria incidence. To address this issue, we stratified the years into two segments, 1955–1981 and 1982–2008, to narrow the aging effect. Negative correlations between malaria incidence and cancer mortality were still observed during each time period.

There are three limitations to the present study. First, cancer mortality and malaria incidence data were obtained from WHO publications and databases. Different countries may use different reporting criteria for the malaria incidence data. In addition, we were unable to obtain age-standardized malaria incidence data from existing databases. For cancer mortality, although the data were age-standardized for the countries studied, medical care and cancer reporting systems varied across countries and may have changed over time; these factors may affect the comparability of cancer mortality data between countries and years. Although our analysis using the GAMM approach maximally accommodated the limitations of existing data by primarily examining trends over time within each country instead of across countries, such limitations could not be completely eliminated by our data analysis. Second, we do not have other countries’ data, especially the data from malaria high endemic countries in Africa. Therefore the impact of endemic malaria on cancer mortality in our analysis may mainly reflect vivax malaria that prevails outside Africa, not falcipuram malaria that prevails in Africa, even though all four species of human malaria parasite (Plasmodium vivax, falcipuram, ovale and malariae) contain pathogen-associated molecular patterns (PAMPs [16]) that can trigger the antitumor immune response (see below). Third, the positive relationship between the incidence of Burkitt’s lymphoma in African children and falciparum malaria has been well established, because this malaria causes a prolonged expansion of B cell in the germinal centers and therefore provides more time for DNA damage and MYC oncogene translocation that ultimately leads to Burkitts lymphoma [17], but we were unable to obtain mortality or incidence data for this tumor from WHO publications or databases. Thus, our analysis model lacks information that could be used to establish a positive relationship between malaria incidence and cancer mortality; such information would be important for validating our model further.

Malaria infection may serve to enhance immune surveillance mechanisms against some types of solid cancers. During the course of malaria, Plasmodium PAMPs [16] as danger signals are detected by the host immune cells’ sensors called pattern recognition receptors (PRRs) which include the toll-like receptors (TLRs) [18] at the membrane of endosomes or on the cell surface, RIG-I-like receptors (RLRs) [19] and NOD-like receptors (NLRs) [20] localized in cytoplasm. The Plasmodium PAMPs include the known glycosylphosphatidylinositol anchors (GPI anchors) [21], haemozoin [22] and immunostimulatory nucleic acid motifs [23] and other unknown molecules [24]. The PRRs activated by Plasmodium PAMPs trigger distinct transcriptional programs and stimulate multiple downstream pathways to induce systemic immune responses [25], including release of pro-inflammatory and Th1-type cytokines such as TNF-α, IL-1β, IL-2, IL-6, IL-12, type I and type II IFNs [25, 26], activation of NK cells, NKT cells, γ/δ T cells, macrophages and dendritic cells (DCs), afterwards activation of CD4+ and CD8+ T cells [26, 27] that counteract the tumor immune-suppressive microenvironment that contains TGF-β, IL-10, regulatory T cells (Tregs) and myeloid-derived suppressive cells (MDSCs) [28, 29], then turn the immune-suppressive milieu to immune-supportive milieu, finally may transform the tumor into an effective tumor vaccine [30, 31]. On the other hand, malaria damage-associate molecular patterns (DAMPs), such as the known intrinsic uric acid, microvesicles and haem [31–33] also induce similar immune activity. Indeed, our previous study demonstrated that blood-stage malaria exhibits anti-tumor effects by inducing a potent anti-tumor innate immune response, including the secretion of IFN-γ and TNF-α and the activation of NK cells. Our murine lung cancer model studies also demonstrated that malaria infection induced adaptive anti-tumor immunity by increasing tumor-specific T-cell proliferation and the cytolytic activity of CD8⁺ T cells and increased the infiltration of these cells into tumor tissues. In these studies, we found that in approximately 10% of lung cancer (LLC)-bearing mice infected with malaria parasite, the tumor regressed and did not regrow when the mice were re-inoculated with the same cancer cells, most likely because of the long-term memory of specific antitumor cellular immunity [11]. Study by Deng XF and colleagues demonstrated that attenuated liver-stage Plasmodium inoculation induced antitumor innate immune response, including secretion of TNF-α, IL-6/12 and IFN-γ and antitumor adaptive immunity with increasing cytolytic activity of CD8+ T cells [34]. Our unpublished data suggest that blood-stage Plasmodium infection significantly decreases the numbers of MDSCs in breast cancer (4 T1)-bearing mice or Tregs in lung cancer (LLC)-bearing mice. In addition, our previous study indicated that malaria infection significantly inhibited tumor angiogenesis in mice [11]. A review by Hobohm [35] suggested that the fever induced by malaria infection may cause an increase in tumor cell death. Based on our previous study and our unpublished data, parasitemia is required to effectively inhibit tumor growth. However, in mice, Plasmodium infection causes only a short-term infection without fever. Repeated Plasmodium infection is difficult to observe in murine models [36]. In humans lacking effective antimalarial treatment, Plasmodium infection can cause long-term parasitemia that is accompanied by a high fever in the acute phase, and this syndrome can recur many times throughout the life span [37]. Therefore, a naturally acquired Plasmodium infection by mosquito bite would produce liver-stage and blood-stage malaria that sequentially stimulate the immune systems to turn the tumor into the effective tumor vaccine, in combination with fever during the acute phase and the inhibition of tumor angiogenesis. In medical literature, febrile infection was linked to spontaneous regression of tumor [38], and malaria is a typical febrile infection. Other pathogen infections may have similar impact on cancer mortality or morbidity [39, 40], due to a similar mechanism of PAMPs-triggered antitumor immunity [41], but malaria parasite may be more significant than other pathogens, because malaria contains two stages (liver and blood) of infection, manifests typical high fever paroxysms during acute phase and may last longer period if not treated with effective antimalarial drugs. In addition, malaria is a well-documentary infectious disease in WHO databases and publications partially due to easily to be diagnosed by fever paroxysm and a simple blood smear test. Other pathogen infections lack well-documentary data in WHO databases or publications for analysis. In summary, some or all of these above mentioned factors and mechanisms may explain why endemic malaria might reduce cancer mortality at the population level.