Long-term exposure to ambient air pollution and risk of leukemia and lymphoma in a pooled European cohort

Are we ignoring sex differences in haematological malignancies? A call for improved reporting

There are clear sex-based differences in the incidence, risk factors and mortality of most haematologic malignancies (HM). Despite known differences in physiology, haematopoiesis, molecular profiles, drug pharmacokinetics, treatment-related toxicities and treatment experience, males and females receive standardized and identical treatment for most HMs. Previous published work has demonstrated disparities in female representation in cancer clinical trials and highlighted a paucity of information on differential treatment outcomes and toxicities by sex. We analysed references of 182 clinical trials which form the basis of recent treatment guidelines from the National Comprehensive Cancer Network and found a minority (17/9.3%) did not report the sex distribution of trial participants. However, a majority (165/90.6%) did not report sex-disaggregated outcomes. Of those that did, 36.5% showed outcome differences by sex. Academic leadership by women in the assessed trials as well as in guidelines committees was disproportionately lower than their representation in the profession. We call on all clinical trials leaders, consortia and guideline builders to include sex-disaggregated data in their analyses, reporting these in a transparent manner (as per regulations mandating such reporting), and for investigators to assess whether aetiological factors differ by sex. These actions will enhance personalized prevention, therapy and follow-up.

Fine particulate matter exposure and cancer risk: a systematic review and meta-analysis of prospective cohort studies

Studies examining the relationship between fine particulate matter (PM2.5) exposure and cancer risk is inconclusive, with an evident scarcity of comprehensive data on the overall cancer risk. Given the emergence of new evidence, updated meta-analyses is essential. A search was performed on multiple databases including PubMed, Embase, Scopus, Web of Science, and the Cochrane Library up to Jan 2025. Hazard ratios (HRs), relative risks (RRs), or incidence rate ratios (IRRs) with their 95 % confidence intervals (CIs) were extracted and pooled. Moreover, a comprehensive and detailed quality assessment of the included studies was conducted to validate the plausibility of the findings. Overall, 57 original studies were included, covering 36 cancer categories and including overall cancer and malignancies specific to particular anatomical sites. For each increase of 10 μg per cubic meter in PM2.5 concentration, there was an observed pooled HR of 1.07 for overall cancer (95 %CI:1.02-1.13). In the case of site-specific cancers, the pooled HRs were 1.11 (95 %CI:1.07-1.15), 1.06 (95 %CI:1.02-1.11), 1.17 (95 %CI:1.07-1.28), and 1.14 (95 %CI:1.03-1.26) for lung, breast, liver and esophageal cancers, respectively. Furthermore, PM2.5 exposure may potentially correlate with the risk of cancers at other anatomical locations including upper aerodigestive tract, oral cavity, kidney, skin, as well as digestive organs. In light of available evidence, it is inferred that PM2.5 exposure could potentially raise overall cancer risk with moderate certainty. As for site-specific malignancies, there is very low certainty evidence for lung cancer, low certainty evidence for breast cancer, and moderate certainty evidence for both liver and esophageal cancers.

MTLPM: a long-term fine-grained PM2.5 prediction method based on spatio-temporal graph neural network

The concentration of PM2.5 is one of the air quality indicators that the public pays the most attention to. Existing methods for PM2.5 prediction primarily analyze and forecast data from individual monitoring stations, without considering the mutual influence among multiple stations caused by natural environmental factors, e.g., air circulation. Moreover, the existing methods are mostly short-term predictions and perform poorly in long-term forecasting. In this paper, we propose MTLPM, i.e., a spatio-temporal graph neural network model based on an encoder-decoder architecture, which fully exploits the spatial dynamic patterns and long-term dependencies. Firstly, we adopt a message passing mechanism combined with spatial features and complex environmental factors (e.g., temperature, humidity, and wind direction) to update station data, capturing real-time spatial dynamic information. Secondly, we adopt the Multi-head ProbSparse Self-attention to extract temporal features, learning the long-term dependency relationships among the data. Finally, we adopt a generative one-step decoder structure to simultaneously forecast the data for multiple stations over a long period. We conducted experiments on both the project dataset and the publicly available dataset. Compared to existing state-of-the-art methods, MTLPM achieved an average reduction of approximately 1.6 in mean absolute error (MAE) and approximately 0.02 in symmetric mean absolute percentage error (SMAPE) in predicting results. The relevant source code is publicly available on GitHub1.

Outdoor air pollution and risk of incident adult haematologic cancer subtypes in a large US prospective cohort

BACKGROUND

Outdoor air pollution and particulate matter (PM) are classified as Group 1 human carcinogens for lung cancer. Pollutant associations with haematologic cancers are suggestive, but these cancers are aetiologically heterogeneous and sub-type examinations are lacking.

METHODS

The American Cancer Society Cancer Prevention Study-II Nutrition Cohort was used to examine associations of outdoor air pollutants with adult haematologic cancers. Census block group level annual predictions of particulate matter (PM2.5, PM10, PM10-2.5), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and carbon monoxide (CO) were assigned with residential addresses. Hazard ratios (HR) and 95% confidence intervals (CI) between time-varying pollutants and haematologic subtypes were estimated.

RESULTS

Among 108,002 participants, 2659 incident haematologic cancers were identified from 1992-2017. Higher PM10-2.5 concentrations were associated with mantle cell lymphoma (HR per 4.1 μg/m3 = 1.43, 95% CI 1.08-1.90). NO2 was associated with Hodgkin lymphoma (HR per 7.2 ppb = 1.39; 95% CI 1.01-1.92) and marginal zone lymphoma (HR per 7.2 ppb = 1.30; 95% CI 1.01-1.67). CO was associated with marginal zone (HR per 0.21 ppm = 1.30; 95% CI 1.04-1.62) and T-cell (HR per 0.21 ppm = 1.27; 95% CI 1.00-1.61) lymphomas.

CONCLUSIONS

The role of air pollutants on haematologic cancers may have been underestimated previously because of sub-type heterogeneity.

Effects and mechanisms of N6-methyladenosine RNA methylation in environmental pollutant-induced carcinogenesis

Environmental pollution, including air pollution, plastic contamination, and heavy metal exposure, is a pressing global issue. This crisis contributes significantly to pollution-related diseases and is a critical risk factor for chronic health conditions, including cancer. Mounting evidence underscores the pivotal role of N6-methyladenosine (m6A) as a crucial regulatory mechanism in pathological processes and cancer progression. Governed by m6A writers, erasers, and readers, m6A orchestrates alterations in target gene expression, consequently playing a vital role in a spectrum of RNA processes, covering mRNA processing, translation, degradation, splicing, nuclear export, and folding. Thus, there is a growing need to pinpoint specific m6A-regulated targets in environmental pollutant-induced carcinogenesis, an emerging area of research in cancer prevention. This review consolidates the understanding of m6A modification in environmental pollutant-induced tumorigenesis, explicitly examining its implications in lung, skin, and bladder cancer. We also investigate the biological mechanisms that underlie carcinogenesis originating from pollution. Specific m6A methylation pathways, such as the HIF1A/METTL3/IGF2BP3/BIRC5 network, METTL3/YTHDF1-mediated m6A modification of IL 24, METTL3/YTHDF2 dynamically catalyzed m6A modification of AKT1, METTL3-mediated m6A-modified oxidative stress, METTL16-mediated m6A modification, site-specific ATG13 methylation-mediated autophagy, and the role of m6A in up-regulating ribosome biogenesis, all come into play in this intricate process. Furthermore, we discuss the direction regarding the interplay between pollutants and RNA metabolism, particularly in immune response, providing new information on RNA modifications for future exploration.

Genetically determined telomere length as a risk factor for hematological malignancies: evidence from Mendelian randomization analysis

BACKGROUND

Over the past years, the exact correlation between telomere length and hematological malignancies was still not fully understood.

METHODS

We performed a two-sample Mendelian randomization study to investigate the causal relationship between telomere length and hematological malignancies. We selected genetic instruments associated with telomere length. The genetic associations for lymphoid and hematopoietic malignant neoplasms were obtained from the most recent publicly accessible FinnGen study R9 data. Inverse variant weighted (IVW) analysis was adopted as the primary method, and we also performed the weighted-median method and the MR-Egger, and MRPRESSO methods as sensitive analysis.

RESULTS

Significant associations have been observed between telomere length and primary lymphoid (IVW: OR = 1.52, P = 2.11 × 10-6), Hodgkin lymphoma (IVW: OR = 1.64, P = 0.014), non-Hodgkin lymphoma (IVW: OR = 1.70, P = 0.002), B-cell lymphoma (IVW: OR = 1.57, P = 0.015), non-follicular lymphoma (IVW: OR = 1.58, P = 1.7 × 10-3), mantle cell lymphoma (IVW: OR = 3.13, P = 0.003), lymphoid leukemia (IVW: OR = 2.56, P = 5.92E-09), acute lymphocytic leukemia (IVW: OR = 2.65, P = 0.021) and chronic lymphocytic leukemia (IVW: OR = 2.80, P = 8.21 × 10-6), along with multiple myeloma (IVW: OR = 1.85, P = 0.016).

CONCLUSION

This MR study found a significant association between telomere length and a wide range of hematopoietic malignancies. But no substantial impact of lymphoma and hematopoietic malignancies on telomere length has been detected.