Causes of cancer: physical, chemical, biological carcinogens, and viruses

Das, Subhayan; Kundu, Moumita; Jena, Bikash Chandra

doi:10.1016/b978-0-12-818128-7.00025-3

Cited by 9 publications

(6 citation statements)

References 190 publications

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“…Jinghao Duan 1 Yongsheng Gao 2 Ying Yin 3 Fengchang Yang 4 Wenjie Tang 1 Xiaoyu Song 1 Tao Hu 1 Jinfeng Cui 1 Jinming Yu 1 Shuanghu Yuan 1,5…”

Section: A C K N O W L E D G E M E N T Smentioning

confidence: 99%

Section: Dear Editormentioning

confidence: 99%

“…Tumourigenesis and progression are heterogeneous at the phenotypic, physiologic and genomic levels, making predictive information obtained via radiomic or genomic profiling alone of limited value for clinical decision making. [4][5][6] The present study aimed to develop a combination of genomic, radiomic and fused radiogenomic biomarkers for predicting the response of LS-SCLC to dCRT in training and validation cohorts, and to provide optimised multi-omics prediction models based on their predictive power for LS-SCLC.Totally 154 patients with LS-SCLC who received dCRT in Shandong Cancer Hospital and Institute were included, and were randomly divided into a training group and test group at a ratio of 7:3. No significant differences in clinical or genomic characteristics were found between the two cohorts (Table S1).…”

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confidence: 99%

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Multi‐omics predictive model based on clinical, radiomic and genomic features for predicting the response of limited‐stage small cell lung cancer to definitive chemoradiotherapy

Li,

Duan,

Gao

et al. 2024

Clinical & Translational Med

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Dear Editor,The efficacy of definitive concurrent or sequential chemoradiotherapy (dCRT) varies significantly among limited-stage small-cell lung cancer (LS-SCLC) patients, with about 10%-13% of patients achieving 5-year survival, while 58% of patients die within 1 year. 1-3 Therefore, there is an urgent need to find biomarkers for early prediction of the efficacy of dCRT in LS-SCLC in support of risk stratification. Tumourigenesis and progression are heterogeneous at the phenotypic, physiologic and genomic levels, making predictive information obtained via radiomic or genomic profiling alone of limited value for clinical decision making. [4][5][6] The present study aimed to develop a combination of genomic, radiomic and fused radiogenomic biomarkers for predicting the response of LS-SCLC to dCRT in training and validation cohorts, and to provide optimised multi-omics prediction models based on their predictive power for LS-SCLC.Totally 154 patients with LS-SCLC who received dCRT in Shandong Cancer Hospital and Institute were included, and were randomly divided into a training group and test group at a ratio of 7:3. No significant differences in clinical or genomic characteristics were found between the two cohorts (Table S1). The median PFS (progression free survival, mPFS) among all patients was 12.7 months (range, 2.4−60.5 months).In the training cohort, LASSO regression 7 was performed to obtain the most significant radiomic features related to PFS according to a λ min of .046 (Figure 1A,B). The radiomic signature (Rad-score) was then constructed by linearly combining the 10 selected features and corresponding weighting coefficients, as listed in Table S2. The best threshold was .35, 8 which divided patients into a high-risk group (Rad-score ≥ .35) and a low-risk group (Rad-score < .35). Rad-score was identified as an independent biomarker for PFS on both univariate and mul-This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Jinghao Duan 1 Yongsheng Gao 2 Ying Yin 3 Fengchang Yang 4 Wenjie Tang 1 Xiaoyu Song 1 Tao Hu 1 Jinfeng Cui 1 Jinming Yu 1 Shuanghu Yuan 1,5…”

Section: A C K N O W L E D G E M E N T Smentioning

confidence: 99%

Section: Dear Editormentioning

confidence: 99%

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confidence: 99%

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Multi‐omics predictive model based on clinical, radiomic and genomic features for predicting the response of limited‐stage small cell lung cancer to definitive chemoradiotherapy

Li,

Duan,

Gao

et al. 2024

Clinical & Translational Med

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“…Also, carcinogenic can increases cancer [30]. Chemical carcinogenic which refers to some chemical substances such as arsenic (As), cadmium (Cd), nickel (Ni), and selenium (Se), can destroy DNA that directly or indirectly causes a mutation in the essential oncogene or immune cell gene that causes cancer [31]. The NiTi-based shape memory alloys are the most popular ones that are used in biomedical applications, however, one of the big problems of using NiTi as a biomaterial is the release of the carcinogenic Ni and Ti ions into the human body [32].…”

Section: Carcinogenicmentioning

confidence: 99%

A Review Study on Biocompatible Improvements of NiTi-based Shape Memory Alloys

Mohammed

Kök

Qader

et al. 2021

International Journal of Innovative Engineering Applications

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Review paperNiTi-based shape memory alloys (SMAs) have many applications, especially for implantation, however since they are not a passive material so it is important to investigate them from different biocompatible perspectives. In this study, we introduced the important physical characteristics of NiTi alloys, then we explained different biocompatible terminologies, including carcinogenic, genotoxic, cytotoxicity, mutagenic, allergic, and corrosivity. We collected some important previous works that investigated the biocompatibility of NiTi-based SMAs and the different techniques used for improving the alloy and diminishing the hazard due to Ni-leakages.

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“…In addition to genetic predisposition, environmental factors play a major role. Exposition to physical, chemical, or biological carcinogens favor the development of mutations, which can lead to the transformation of normal cells to tumor cells [ 5 ]. For many types of cancer, the precise mutational characterization of a tumor is essential, influencing diagnosis, prognosis, and therapy.…”

Section: Introductionmentioning

confidence: 99%

Reconstructing Clonal Evolution—A Systematic Evaluation of Current Bioinformatics Approaches

Sandmann

Richter

Jiang

et al. 2023

IJERPH

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The accurate reconstruction of clonal evolution, including the identification of newly developing, highly aggressive subclones, is essential for the application of precision medicine in cancer treatment. Reconstruction, aiming for correct variant clustering and clonal evolution tree reconstruction, is commonly performed by tedious manual work. While there is a plethora of tools to automatically generate reconstruction, their reliability, especially reasons for unreliability, are not systematically assessed. We developed clevRsim—an approach to simulate clonal evolution data, including single-nucleotide variants as well as (overlapping) copy number variants. From this, we generated 88 data sets and performed a systematic evaluation of the tools for the reconstruction of clonal evolution. The results indicate a major negative influence of a high number of clones on both clustering and tree reconstruction. Low coverage as well as an extreme number of time points usually leads to poor clustering results. An underlying branched independent evolution hampers correct tree reconstruction. A further major decline in performance could be observed for large deletions and duplications overlapping single-nucleotide variants. In summary, to explore the full potential of reconstructing clonal evolution, improved algorithms that can properly handle the identified limitations are greatly needed.

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Causes of cancer: physical, chemical, biological carcinogens, and viruses

Cited by 9 publications

References 190 publications

Multi‐omics predictive model based on clinical, radiomic and genomic features for predicting the response of limited‐stage small cell lung cancer to definitive chemoradiotherapy

Multi‐omics predictive model based on clinical, radiomic and genomic features for predicting the response of limited‐stage small cell lung cancer to definitive chemoradiotherapy

A Review Study on Biocompatible Improvements of NiTi-based Shape Memory Alloys

Reconstructing Clonal Evolution—A Systematic Evaluation of Current Bioinformatics Approaches

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