Predicting potential therapeutic targets and small molecule drugs for early-stage lung adenocarcinoma.

Lung cancer is a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) constituting the majority, and its main subtype being lung adenocarcinoma (LUAD). Despite substantial advances in LUAD diagnosis and treatment, early diagnostic biomarkers inadequately fulfill clinical requirements. Thus, we conducted bioinformatics analysis to identify potential biomarkers and corresponding therapeutic drugs for early-stage LUAD patients. Here we identified a total of 10 differentially expressed genes (DEGs) with survival significance through the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Subsequently, we identified a promising small molecule drug, Aminopurvalanol A, based on the 10 key genes using the L1000FWD application, which was validated by molecular docking followed by in vivo and in vitro experiments. The results highlighted TOP2A, CDH3, ASPM, CENPF, SLC2A1, and PRC1 as potential detection biomarkers for early LUAD. We confirmed the efficacy and safety of Aminopurvalanol A, providing valuable insights for the clinical management of LUAD.

Rapid Progress in Intelligent Radiotherapy and Future Implementation.

Radiotherapy is one of the major approaches to cancer treatment. Artificial intelligence in radiotherapy (shortly, Intelligent radiotherapy) mainly involves big data, deep learning, extended reality, digital twin, radiomics, Internet plus and Internet of Things (IoT), which establish an automatic and intelligent network platform consisting of radiotherapy preparation, target volume delineation, treatment planning, radiation delivery, quality assurance (QA) and quality control (QC), prognosis judgment and post-treatment follow-up. Intelligent radiotherapy is an interdisciplinary frontier discipline in infancy. The review aims to summary the important implements of intelligent radiotherapy in various areas and put forward the future of unmanned radiotherapy center.

Clinical characteristics and a decision tree model to predict death outcome in severe COVID-19 patients.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. METHODS: A total of 2169 adult COVID-19 patients were enrolled from Wuhan, China, from February 10th to April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups, as well as between survivors and non-survivors. In addition, we developed a decision tree model to predict death outcome in severe patients. RESULTS: Of the 2169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed as severe illness, and 75 patients died. An older median age and a higher proportion of male patients were found in severe group or non-survivors compared to their counterparts. Significant differences in clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A decision tree, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in training and test datasets. The accuracy of this model were 0.98 in both datasets. CONCLUSION: We performed a comprehensive analysis of COVID-19 patients from the outbreak in Wuhan, China, and proposed a simple and clinically operable decision tree to help clinicians rapidly identify COVID-19 patients at high risk of death, to whom priority treatment and intensive care should be given.

Clinical features and death risk factors in COVID-19 patients with cancer: a retrospective study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spread around the world. This retrospective study aims to analyze the clinical features of COVID-19 patients with cancer and identify death outcome related risk factors. METHODS: From February 10th to April 15th, 2020, 103 COVID-19 patients with cancer were enrolled. Difference analyses were performed between severe and non-severe patients. A propensity score matching (PSM) analysis was performed, including 103 COVID-19 patients with cancer and 206 matched non-cancer COVID-19 patients. Next, we identified death related risk factors and developed a nomogram for predicting the probability. RESULTS: In 103 COVID-19 patients with cancer, the main cancer categories were breast cancer, lung cancer and bladder cancer. Compared to non-severe patients, severe patients had a higher median age, and a higher proportion of smokers, diabetes, heart disease and dyspnea. In addition, most of the laboratory results between two groups were significantly different. PSM analysis found that the proportion of dyspnea was much higher in COVID-19 patients with cancer. The severity incidence in two groups were similar, while a much higher mortality was found in COVID-19 patients with cancer compared to that in COVID-19 patients without cancer (11.7% vs. 4.4%, P = 0.028). Furthermore, we found that neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) were related to death outcome. And a nomogram based on the factors was developed. CONCLUSION: In COVID-19 patients with cancer, the clinical features and laboratory results between severe group and non-severe group were significantly different. NLR and CRP were the risk factors that could predict death outcome.

A randomized, controlled phase II trial of maxillofacial and oral massage in attenuating severe radiotherapy-induced oral mucositis and lipid metabolite changes in nasopharyngeal carcinoma.

PURPOSE: This randomized controlled phase II study investigated the efficacy, safety and underlying mechanism of maxillofacial and oral massage (MOM) in nasopharyngeal carcinoma (NPC) patients receiving intensity-modulated radiotherapy. METHODS: A total of 158 NPC patients were randomly assigned 1:1 to routine oral care and medication (the control group) or that with additional MOM (the treatment group). The primary endpoint was the incidence of severe radiotherapy-induced oral mucositis (SRTOM). In addition, the time of initiation and duration of RTOM and SRTOM, adverse events, dynamic changes of lipid metabolites in peripheral blood were analyzed. RESULTS: Seventy-six patients in the treatment group and seventy-nine in the control group completed the trial. The incidence of SRTOM in the treatment group was lower than the control (26.3% vs. 46.8%, P = 0.008). The median initiation time to RTOM and SRTOM was significantly longer in the treatment group than the control (RTOM:12 vs 10 days, hazard ratio [HR] 0.52, P < 0.001; SRTOM: 28.5 vs 19 days, HR 0.5579, P = 0.002). While the median duration time of RTOM and SRTOM in the treatment group was shorter (RTOM: 20.7 vs 24.7 days, P = 0.001; SRTOM: 8.05 vs 13.08 days, P < 0.001). Only 1.3% of patients obtained grade 3 or higher adverse events during MOM. The anti-inflammatory lipids increased significantly after MOM, especially with 10.6 Gy or higher. CONCLUSION: MOM significantly attenuated the incidence of SRTOM in NPC patients. The adverse events of MOM were slight and tolerant. MOM enhanced anti-inflammatory lipid metabolites, which might be an underlying mechanism.

Survival Analysis and Prediction Model for Pulmonary Sarcomatoid Carcinoma Based on SEER Database.

OBJECTIVE: This study aimed to investigate the incidence of the pulmonary sarcomatoid carcinoma (PSC), to compare the clinical characteristics and overall survival (OS) of patients with PSC and those with other non-small-cell lung cancer (oNSCLC), so as to analyze the factors affecting the OS of patients with PSC and construct a nomogram prediction model. METHODS: Data of patients with PSC and those with oNSCLC diagnosed between 2004 and 2015 from the Surveillance, Epidemiology, and End Results database were collected. The age-adjusted incidence of PSC was calculated. The characteristics of patients with PSC and those with oNSCLC were compared, then the patients were matched 1:2 for further survival analysis. Patients with PSC were randomly divided into training set and testing set with a ratio of 7:3. The Cox proportional hazards model was used to identify the covariates associated with the OS. Significant covariates were used to construct the nomogram, and the C-index was calculated to measure the discrimination ability. The accuracy of the nomogram was compared with the tumor-node-metastasis (TNM) clinical stage, and the corresponding area under the curve was achieved. RESULTS: A total of 1049 patients with PSC were enrolled, the incidence of PSC was slowly decreased from 0.120/100,000 in 2004 to 0.092/100,000 in 2015. Before PSM, 793 PSC patients and 191356 oNSCLC patients were identified, the proportion of male, younger patients (<65 years), grade IV, TNM clinical stage IV was higher in the PSC. The patients with PSC had significantly poorer OS compared with those with oNSCLC. After PSM, PSC still had an extremely inferior prognosis. Age, sex, TNM clinical stage, chemotherapy, radiotherapy, and surgery were independent factors for OS. Next, a nomogram was established based on these factors, and the C-indexs were 0.775 and 0.790 for the training and testing set, respectively. Moreover, the nomogram model indicated a more comprehensive and accurate prediction than the TNM clinical stage. CONCLUSIONS: The incidence of PSC was slowly decreased. PSC had a significantly poor prognosis compared with oNSCLC. The nomogram constructed in this study accurately predicted the prognosis of PSC, performed better than the TNM clinical stage.

Indications for and contraindications of immune checkpoint inhibitors in cancer patients with COVID-19 vaccination.

The COVID-19 pandemic has lasted over 1 year and will not disappear in a short time. There is no specific remedy against the virus as yet. Vaccination is thus far one of the most important strategies for preventing COVID-19. Cancer patients with COVID-19 have a higher mortality because of immunosuppression. Immune checkpoint inhibitors (ICIs) are a novel anticancer strategy for blocking inhibitory pathways, which are related to the immune response. There is a question regarding whether COVID-19 vaccination and ICI treatment impact each other in cancer patients. This review explores both sides of the relationship between ICI treatment and COVID-19 vaccination and suggests good efficacy and safety of ICI treatment after COVID-19 vaccination as well as little impact on the virus protection and toxicity associated with COVID-19 vaccination during ICI treatment.

Lay abstract The COVID-19 pandemic has lasted over 1 year. Vaccination is a promising strategy for preventing COVID-19. Cancer patients are prone to infection with COVID-19, and these patients have high mortality. Immune checkpoint inhibitors (ICIs) are a novel anticancer strategy. Whether COVID-19 vaccination and ICI treatment impact each other in cancer patients remains unknown. This review explores both sides of the relationship between ICI treatment and COVID-19 vaccination and suggests good efficacy and safety of ICI treatment after COVID-19 vaccination as well as little impact on the virus protection and toxicity associated with COVID-19 vaccination during ICI treatment.

Construction of nomograms for nasopharyngeal carcinoma containing primary tumor size and SEER stage.

BACKGROUND: At present, little attention has been paid to the impact of primary tumor size on the survival of nasopharyngeal carcinoma (NPC). We aimed to construct predictive nomograms for NPC that contain primary tumor size and Surveillance, Epidemiology, and End Results (SEER) stage as factors with the use of a national population-based registry. METHODS: A total of 1,574 NPC patients diagnosed between 2004 and 2011 were identified from the SEER database. Univariate and multivariate Cox analysis was used to screen out survival related clinical features. Then, two nomograms of 5-year overall survival (OS) and 5-year cancer-specific survival (CSS) were constructed using the significant clinical features. The concordance index (c-index) and a calibration plot were used to assess the performance of the nomograms. Furthermore, survival analyses were performed by risk score stratification. RESULTS: According to univariate and multivariate analyses, age, race, histology, primary tumor size, SEER stage, radiotherapy and chemotherapy were related to 5-year OS and CSS in NPC. All of these clinical features were included when constructing nomograms. The c-index values were 0.74 and 0.73 for the 5-year OS nomogram in the training cohort and testing cohorts, respectively. For the 5-year CSS nomogram, the c-index values were both 0.73 in the training cohort and testing cohort, respectively. The calibration plots showed that the predicted 5-year OS and CSS outcomes were in good agreement with the observed outcomes. All patients were divided into three groups according to the risk score stratification. Kaplan-Meier survival analyses showed that patients in the low-risk cohort had a greater 5-year survival benefit than patients in the medium-risk and high-risk cohorts (P<0.05). CONCLUSIONS: The predictive nomograms, which contain primary tumor size and SEER stage, can predict the rates of 5-year OS and CSS of NPC patients and can be used as an auxiliary prediction tool for future clinical practice.

Clinicopathological characteristics and prognostic factors of pulmonary large cell neuroendocrine carcinoma: A large population-based analysis.

BACKGROUND: The study was conducted to compare the clinicopathological characteristics, survival outcomes, and metastatic patterns between pulmonary large cell neuroendocrine carcinoma (LCNEC) and other non-small cell lung cancer (ONSCLC), and to identify the prognostic factors of LCNEC. METHODS: Data of patients diagnosed with LCNEC and ONSCLC from 2004 to 2014 were obtained from the Surveillance, Epidemiology, and End Results dataset. Pearson's chi-square tests were used to compare differences in clinicopathological characteristics. The Kaplan-Meier method was used for survival analysis. A propensity score was used for matching and a Cox proportional hazards model was used for multivariate and subgroup analyses. RESULTS: A total of 2368 LCNEC cases and 231 672 ONSCLC cases were identified. LCNEC incidence increased slightly over time. Except for marital status, LCNEC patients had obviously different biological features to ONSCLC patients. Survival analysis showed that LCNEC had poorer outcomes than ONSCLC. Multivariate analysis revealed that female gender, black race, surgery, radiation, and chemotherapy were protective factors for LCNEC. Matched subgroup analysis further demonstrated that most subgroup factors favored ONSCLC, especially in early stage. Early-stage LCNEC patients had a higher risk of lung cancer-specific death than early-stage ONSCLC patients. Moreover, metastatic patterns were different between LCNEC and ONSCLC. LCNEC patients with isolated liver metastasis or combined invasion to other organs had poorer survival rates. CONCLUSIONS: LCNEC has totally different clinicopathological characteristics and metastatic patterns to ONSCLC. LCNEC also has poorer survival outcomes, primarily because of isolated liver metastasis or combined invasion to other organs. Most subgroup factors are adverse factors for LCNEC.