The mechanism and application of traditional Chinese medicine extracts in the treatment of lung cancer and other lung-related diseases

Lung cancer stands as one of the most prevalent malignancies worldwide, bearing the highest morbidity and mortality rates among all malignant tumors. The treatment of lung cancer primarily encompasses surgical procedures, radiotherapy, and chemotherapy, which are fraught with significant side effects, unfavorable prognoses, and a heightened risk of metastasis and relapse. Although targeted therapy and immunotherapy have gradually gained prominence in lung cancer treatment, diversifying the array of available methods, the overall recovery and survival rates for lung cancer patients remain suboptimal. Presently, with a holistic approach and a focus on syndrome differentiation and treatment, Traditional Chinese Medicine (TCM) has emerged as a pivotal player in the prognosis of cancer patients. TCM possesses characteristics such as targeting multiple aspects, addressing a wide range of concerns, and minimizing toxic side effects. Research demonstrates that Traditional Chinese Medicine can significantly contribute to the treatment or serve as an adjunct to chemotherapy for lung cancer and other lung-related diseases. This is achieved through mechanisms like inhibiting tumor cell proliferation, inducing tumor cell apoptosis, suppressing tumor angiogenesis, influencing the cellular microenvironment, regulating immune system function, impacting signal transduction pathways, and reversing multidrug resistance in tumor cells. In this article, we offer an overview of the advancements in research concerning Traditional Chinese Medicine extracts for the treatment or adjunctive chemotherapy of lung cancer and other lung-related conditions. Furthermore, we delve into the challenges that Traditional Chinese Medicine extracts face in lung cancer treatment, laying the foundation for the development of diagnostic, prognostic, and therapeutic targets

The emerging role of m6A modification of non-coding RNA in gastrointestinal cancers: a comprehensive review

Gastrointestinal (GI) cancer is a series of malignant tumors with a high incidence globally. Although approaches for tumor diagnosis and therapy have advanced substantially, the mechanisms underlying the occurrence and progression of GI cancer are still unclear. Increasing evidence supports an important role for N6-methyladenosine (m6A) modification in many biological processes, including cancer-related processes via splicing, export, degradation, and translation of mRNAs. Under distinct cancer contexts, m6A regulators have different expression patterns and can regulate or be regulated by mRNAs and non-coding RNAs, especially long non-coding RNAs. The roles of m6A in cancer development have attracted increasing attention in epigenetics research. In this review, we synthesize progress in our understanding of m6A and its roles in GI cancer, especially esophageal, gastric, and colorectal cancers. Furthermore, we clarify the mechanism by which m6A contributes to GI cancer, providing a basis for the development of diagnostic, prognostic, and therapeutic targets

Data and Knowledge Co-driving for Cancer Subtype Classification on Multi-Scale Histopathological Slides

Artificial intelligence-enabled histopathological data analysis has become a valuable assistant to the pathologist. However, existing models lack representation and inference abilities compared with those of pathologists, especially in cancer subtype diagnosis, which is unconvincing in clinical practice. For instance, pathologists typically observe the lesions of a slide from global to local, and then can give a diagnosis based on their knowledge and experience. In this paper, we propose a Data and Knowledge Co-driving (D&K) model to replicate the process of cancer subtype classification on a histopathological slide like a pathologist. Specifically, in the data-driven module, the bagging mechanism in ensemble learning is leveraged to integrate the histological features from various bags extracted by the embedding representation unit. Furthermore, a knowledge-driven module is established based on the Gestalt principle in psychology to build the three-dimensional (3D) expert knowledge space and map histological features into this space for metric. Then, the diagnosis can be made according to the Euclidean distance between them. Extensive experimental results on both public and in-house datasets demonstrate that the D&K model has a high performance and credible results compared with the state-of-the-art methods for diagnosing histopathological subtypes. Code: https://github.com/Dennis-YB/Data-and-Knowledge-Co-driving-for-Cancer-Subtypes-Classificatio

Multi-Modality Multi-Scale Cardiovascular Disease Subtypes Classification Using Raman Image and Medical History

Raman spectroscopy (RS) has been widely used for disease diagnosis, e.g., cardiovascular disease (CVD), owing to its efficiency and component-specific testing capabilities. A series of popular deep learning methods have recently been introduced to learn nuance features from RS for binary classifications and achieved outstanding performance than conventional machine learning methods. However, these existing deep learning methods still confront some challenges in classifying subtypes of CVD. For example, the nuance between subtypes is quite hard to capture and represent by intelligent models due to the chillingly similar shape of RS sequences. Moreover, medical history information is an essential resource for distinguishing subtypes, but they are underutilized. In light of this, we propose a multi-modality multi-scale model called M3S, which is a novel deep learning method with two core modules to address these issues. First, we convert RS data to various resolution images by the Gramian angular field (GAF) to enlarge nuance, and a two-branch structure is leveraged to get embeddings for distinction in the multi-scale feature extraction module. Second, a probability matrix and a weight matrix are used to enhance the classification capacity by combining the RS and medical history data in the multi-modality data fusion module. We perform extensive evaluations of M3S and found its outstanding performance on our in-house dataset, with accuracy, precision, recall, specificity, and F1 score of 0.9330, 0.9379, 0.9291, 0.9752, and 0.9334, respectively. These results demonstrate that the M3S has high performance and robustness compared with popular methods in diagnosing CVD subtypes

IRES-Mediated Protein Translation Overcomes Suppression by the p14ARF Tumor Suppressor Protein.

Internal ribosome entry sites (IRES elements) have attracted interest in cancer gene therapy because they can be used in the design of gene transfer vectors that provide bicistronic co-expression of two transgene products under the control of a single promoter. Unlike cellular translation of most mRNAs, a process that requires a post-translational 5' modification of the mRNA known as the cap structure, IRES-mediated translation is independent of the cap structure. The cellular conditions that may intervene to modulate IRES-mediated, cap-independent versus cap-dependent translation, however, remain poorly understood, although they could be critical to the choice of gene transfer vectors. Here we have compared the effects of the p14ARF (Alternate Reading Frame) tumor suppressor, a translational suppressor frequently overexpressed in cancer, on cap-dependent translation versus cap-independent translation from the EMCV viral IRES often used in bicistronic gene transfer vectors. We find that ectopic overexpression of p14ARF suppresses endogenous and ectopic cap-dependent protein translation, consistent with other studies. However, p14ARF has little or no effect on transgene translation initiated within an IRES element. This suggests that transgenes placed downstream of an IRES element will retain efficient translation of their gene products in the presence of high levels of ectopic or endogenous p14ARF, a finding that could be particularly relevant to therapeutic gene therapy strategies for cancer

Prognostic Value of ctDNA Mutation in Melanoma: A Meta-Analysis

Purpose. Melanoma is the most aggressive form of skin cancer. Circulating tumor DNA (ctDNA) is a diagnostic and prognostic marker of melanoma. However, whether ctDNA mutations can independently predict survival remains controversial. This meta-analysis assessed the prognostic value of the presence or change in ctDNA mutations in melanoma patients. Methods. We identified studies from the PubMed, EMBASE, Web of Science, and Cochrane databases. We estimated the combined hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS) using either fixed-effect or random-effect models based on heterogeneity. Results. Sixteen studies including 1,781 patients were included. Both baseline and posttreatment detectable ctDNA were associated with poor OS (baseline detectable vs. undetectable, pooled HR = 1.97, 95% CI = 1.64–2.36, P<0.00001; baseline undetectable vs. detectable, pooled HR = 0.19, 95% CI = 0.11–0.36, P<0.00001; posttreatment detectable vs. undetectable, pooled HR = 2.36, 95% CI = 1.30–4.28, P=0.005). For PFS, baseline detectable ctDNA may be associated with adverse PFS (baseline detectable vs. undetectable, pooled HR = 1.41, 95% CI = 0.84–2.37, P=0.19; baseline undetectable vs. detectable, pooled HR = 0.43, 95% CI = 0.19–0.95, P=0.04) and baseline high ctDNA and increased ctDNA were significantly associated with adverse PFS (baseline high vs. low/undetectable, pooled HR = 3.29, 95% CI = 1.73–6.25, P=0.0003; increase vs. decrease, pooled HR = 4.48, 95% CI = 2.45–8.17, P<0.00001). The baseline BRAFV600 ctDNA mutation-positive group was significantly associated with adverse OS compared with the baseline ctDNA-negative group (pooled HR = 1.90, 95% CI = 1.58–2.29, P<0.00001). There were no significant differences in PFS between the baseline BRAFV600 ctDNA mutation-detectable group and the undetectable group (pooled HR = 1.02, 95% CI = 0.72–1.44, P=0.92). Conclusion. The presence or elevation of ctDNA mutation or BRAFV600 ctDNA mutation was significantly associated with worse prognosis in melanoma patients

Deficiency of BAP1 inhibits neuroblastoma tumorigenesis through destabilization of MYCN

Abstract The transcription factor MYCN is frequently amplified and overexpressed in a variety of cancers including high-risk neuroblastoma (NB) and promotes tumor cell proliferation, survival, and migration. Therefore, MYCN is being pursued as an attractive therapeutic target for selective inhibition of its upstream regulators because MYCN is considered a “undruggable” target. Thus, it is important to explore the upstream regulators for the transcription and post-translational modification of MYCN. Here, we report that BRCA1-associated protein-1 (BAP1) promotes deubiquitination and subsequent stabilization of MYCN by directly binding to MYCN protein. Furthermore, BAP1 knockdown inhibits NB tumor cells growth and migration in vitro and in vivo, which can be rescued partially by ectopic expression of MYCN. Importantly, depletion of BAP1 confers cellular resistance to bromodomain and extraterminal (BET) protein inhibitor JQ1 and Aurora A kinase inhibitor Alisertib. Furthermore, IHC results of NB tissue array confirmed the positive correlation between BAP1 and MYCN protein. Altogether, our work not only uncovers an oncogenic function of BAP1 by stabilizing MYCN, but also reveals a critical mechanism for the post-translational regulation of MYCN in NB. Our findings further indicate that BAP1 could be a potential therapeutic target for MYCN-amplified neuroblastoma