Oral submucous fibrosis (OSF) is characterized by abnormal collagen deposition. It is a precancerous disorder and transforms into a malignant tumor in 1.5–15% of all cases. Symptoms include submucous fibrosis, ulceration, xerostomia, a burning sensation, and restricted mouth opening. All of these greatly interfere with patient quality of life. The present review introduces OSF from a molecular perspective and summarizes what is known about its underlying mechanisms, diagnostic biomarkers, and therapeutic interventions. In addition to the aggressive treatment of OSF, its prevention is also important. Future research should, therefore, focus on improving the oral health literacy of the patients susceptible to OSF.

The lysyl oxidase (LOX) family of oxidases contains a group of extracellular copper-dependent enzymes that catalyze the cross-linking of collagen and elastin by oxidation, thus maintaining the rigidity and structural stability of the extracellular matrix (ECM). Aberrant expression or activation of LOX alters the cellular microenvironment, leading to many diseases, including atherosclerosis, tissue fibrosis, and cancer. Recently, a number of studies have shown that LOX is overexpressed in most cancers and that it is involved in the regulation of tumor progression and metastasis. In contrast, a few reports have also indicated the tumor-suppressing role of LOX. In this short review, we discuss recent research on the correlations between LOX and cancer. Further, the role of LOX in tumor microenvironment remodeling, tumorigenesis, and metastasis and the underlying mechanisms have also been elucidated.

Lung cancer is one of the most critical global health threats, as the second most common cancer and leading cause of cancer deaths globally. While smoking is the primary risk factor, an increasing number of cases occur in nonsmokers, with lung cancer in nonsmokers (LCNS) now recognized as the fifth leading cause of cancer mortality worldwide. Recent evidence identifies air pollution, particularly fine particulate matter (PM2.5), as a significant risk factor in LCNS. PM2.5 can increase oxidative stress and inflammation, induce genetic alterations and activation of oncogenes (including the epidermal growth factor receptor, EGFR), and contribute to lung cancer progression. This review summarizes the current understanding of how exposure to PM2.5 induces lung carcinogenesis and accelerates lung cancer development. It underscores the importance of prevention and early detection while calling for targeted therapies to combat the detrimental effects of air pollution. An integrated approach that combines research, public health policy, and clinical practice is essential to reduce the lung cancer burden and improve outcomes for those affected by PM2.5 exposurrre.

Particulate matter 2.5 (PM2.5) is a risk factor for lung cancer. In this study, we investigated the molecular mechanisms of PM2.5 exposure on lung cancer progression. We found that short‐term exposure to PM2.5 for 24 h activated the EGFR pathway in lung cancer cells (EGFR wild‐type and mutant), while long‐term exposure of lung cancer cells to PM2.5 for 90 days persistently promoted EGFR activation, cell proliferation, anchorage‐independent growth, and tumor growth in a xenograft mouse model in EGFR‐driven H1975 cancer cells. We showed that PM2.5 activated AhR to translocate into the nucleus and promoted EGFR activation. AhR further interacted with the promoter of TMPRSS2, thereby upregulating TMPRSS2 and IL18 expression to promote cancer progression. Depletion of TMPRSS2 in lung cancer cells suppressed anchorage‐independent growth and xenograft tumor growth in mice. The expression levels of TMPRSS2 were found to correlate with nuclear AhR expression and with cancer stage in lung cancer patient tissue. Long‐term exposure to PM2.5 could promote tumor progression in lung cancer through activation of EGFR and AhR to enhance the TMPRSS2‐IL18 pathway.

Patients with triple-negative breast cancer have few therapeutic strategy options. In this study, we investigated the effect of isoliquiritigenin (ISL) on the proliferation of triple-negative breast cancer cells. We found that treatment with ISL inhibited triple-negative breast cancer cell line (MDA-MB-231) cell growth and increased cytotoxicity. ISL reduced cell cycle progression through the reduction of cyclin D1 protein expression and increased the sub-G1 phase population. The ISL-induced apoptotic cell population was observed by flow cytometry analysis. The expression of Bcl-2 protein was reduced by ISL treatment, whereas the Bax protein level increased; subsequently, the downstream signaling molecules caspase-3 and poly ADP-ribose polymerase (PARP) were activated. Moreover, ISL reduced the expression of total and phosphorylated mammalian target of rapamycin (mTOR), ULK1, and cathepsin B, whereas the expression of autophagic-associated proteins p62, Beclin1, and LC3 was increased. The decreased cathepsin B cause the p62 accumulation to induce caspase-8 mediated apoptosis. In vivo studies further showed that preventive treatment with ISL could inhibit breast cancer growth and induce apoptotic and autophagic-mediated apoptosis cell death. Taken together, ISL exerts an effect on the inhibition of triple-negative MDA-MB-231 breast cancer cell growth through autophagy-mediated apoptosis. Therefore, future studies of ISL as a supplement or alternative therapeutic agent for clinical trials against breast cancer are warranted.

Detection of nodal micrometastasis (tumor size: 0.2-2.0 mm) is challenging for pathologists due to the small size of metastatic foci. Since lymph nodes with micrometastasis are counted as positive nodes, detecting micrometastasis is crucial for accurate pathologic staging of colorectal cancer. Previously, deep learning algorithms developed with manually annotated images performed well in identifying micrometastasis of breast cancer in sentinel lymph nodes. However, the process of manual annotation is labor intensive and time consuming. Multiple instance learning was later used to identify metastatic breast cancer without manual annotation, but its performance appears worse in detecting micrometastasis. Here, we developed a deep learning model using whole-slide images of regional lymph nodes of colorectal cancer with only a slide-level label (either a positive or negative slide). The training, validation, and testing sets included 1963, 219, and 1000 slides, respectively. A supercomputer TAIWANIA 2 was used to train a deep learning model to identify metastasis. At slide level, our algorithm performed well in identifying both macrometastasis (tumor size > 2.0 mm) and micrometastasis with an area under the receiver operating characteristics curve (AUC) of 0.9993 and 0.9956, respectively. Since most of our slides had more than one lymph node, we then tested the performance of our algorithm on 538 single-lymph node images randomly cropped from the testing set. At single-lymph node level, our algorithm maintained good performance in identifying macrometastasis and micrometastasis with an AUC of 0.9944 and 0.9476, respectively. Visualization using class activation mapping confirmed that our model identified nodal metastasis based on areas of tumor cells. Our results demonstrate for the first time that micrometastasis could be detected by deep learning on whole-slide images without manual annotation.