Abstract Background: The five-year cumulative incidence rate in patients diagnosed with stage I small-cell lung cancer (SCLC) who were instructed to undergo surgery was from 40% to 60%.The death competition influence the accuracy of the classical survival analyses.The aim of the study is to investigate the mortality of stage I small-cell lung cancer (SCLC) patients in the presence of competing risks according to a proportional hazards model, and to establish a competing risk nomogram to predict probabilities of both cause-specific death and death resulting from other causes. Methods: The study subjects were patients diagnosed with stage I SCLC according to ICD-O-3.First,the cumulative incidence functions (CIFs) of cause-specific death, as well as of death resulting from other causes, were calculated. Then, a proportional hazards model for the sub-distribution of competing risks and a monogram were constructed to evaluate the probability of mortality in stage I SCLC patients. Results: 1811 patients were included in this study. The five-year probabilities of death due to specific causes and other causes were 61.5% and 13.6%, respectively. Tumor size, extent of tumor, surgery, and radiotherapy were identified as the predictors of death resulting from specific causes in stage I SCLC. The results showed that surgery could effectively reduce the cancer-specific death, and the one-year cumulative incidence dropped from 34.5% to 11.2%.Like surgery, chemotherapy and radiotherapy improved the one-year survival rate. Conclusions: We constructed a predictive model for stage I SCLC using the data from the SEER database. The proportional sub-distribution models of competing risks revealed the predictors of death resulting from both specific causes and other causes. The competing risk nomogram that we built to predict the prognosis showed good reliability and could provide beneficial and individualized predictive information for stage I SCLC patients.
Abstract Background: The five-year cumulative incidence rate in patients diagnosed with stage I small-cell lung cancer(SCLC) who were instructed to undergo surgery was from 40% to 60%.The death competition influence the accuracy of the classical survival analyses.The aim of the study is to investigate the mortality of stage I small-cell lung cancer (SCLC) patients in the presence of competing risks according to a proportional hazards model, and to establish a competing risk nomogram to predict probabilities of both cause-specific death and death resulting from other causes. Methods : The study subjects were patients diagnosed with stage I SCLC according to ICD-O-3. First, the cumulative incidence functions (CIFs) of cause-specific death, as well as of death resulting from other causes, were calculated. Then, a proportional hazards model for the sub-distribution of competing risks and a monogram were constructed to evaluate the probability of mortality in stage I SCLC patients. Results: 1811 patients were included in this study. The five-year probabilities of death due to specific causes and other causes were 61.5% and 13.6%, respectively. Tumor size, extent of tumor, surgery, and radiotherapy were identified as the predictors of death resulting from specific causes in stage I SCLC. The results showed that surgery could effectively reduce the cancer-specific death, and the one-year cumulative incidence dropped from 34.5% to 11.2%. Like surgery, chemotherapy and radiotherapy improved the one-year survival rate. Conclusions : We constructed a predictive model for stage I SCLC using the data from the SEER database. The proportional sub-distribution models of competing risks revealed the predictors of death resulting from both specific causes and other causes. The competing risk nomogram that we built to predict the prognosis showed good reliability and could provide beneficial and individualized predictive information for stage I SCLC patients.
Abstract The growth and sustainable development of humanity is heavily dependent upon molecular nitrogen (N 2 ) fixation. Herein we discover ambient catalyst-free disproportionation of N 2 by water plasma which occurs via the distinctive HONH-HNOH +• intermediate to yield economically valuable nitroxyl (HNO) and hydroxylamine (NH 2 OH) products. Calculations suggest that the reaction is prompted by the coordination of electronically excited N 2 with water dimer radical cation, (H 2 O) 2 +• , in its two-center-three-electron configuration. The reaction products are collected in a 76-needle array discharge reactor with product yields of 1.14 μg cm –2 h –1 for NH 2 OH and 0.37 μg cm –2 h –1 for HNO. Potential applications of these compounds are demonstrated to make ammonia (for NH 2 OH), as well as to chemically react and convert cysteine, and serve as a neuroprotective agent (for HNO). The conversion of N 2 into HNO and NH 2 OH by water plasma could offer great profitability and reduction of polluting emissions, thus giving an entirely look and perspectives to the problem of green N 2 fixation.
Abstract Introduction: Hepatocellular carcinoma (HCC) is an aggressive malignance of high mortality (6th most common worldwide) with few treatment options. Current drug treatments, including chemo-/target therapies (e.g., sorafenib), are usually ineffective among advanced HCC and with high toxicity. ARK5, or NUAK1, a novel AMP-activated protein kinase (AMPK) family member 5, is found overexpressed in many malignancies including HCC and usually associated with poor prognosis, as well as drug resistance. For example, doxorubicin, first-line chemotherapy for TACE, and transarterial chemoembolization for advanced HCC. In addition, ARK5 is found to be vitally involved in oncoprotein Myc-driven oncogenesis (metabolic homeostasis/cell survival), particularly in tumors under nutrition/oxygen-deprivations, including HCC. HX301 is a clinical stage first-in-class (FIC) ARK5 inhibitor (ARK5i) and other kinase activities (e.g., CDK4/6, CSF1R, etc.). In this study we evaluate HX301 antitumor activity in HCC over-expressing both ARK5 and c-Myc. Methods: A panel of subcutaneous HCC patient-derived xenografts (PDXs) were genomically profiled by whole transcriptome sequencing by RNAseq, and several selected models with different expression of ARK5 and c-myc expression were assessed pharmacologically using daily dosing of 100mg/kg HX301. Subcutaneous tumor responses to HX301 were calculated by tumor growth inhibition (TGI). Results: HCC PDX models LI1035, LI6610 both had high expression of ARK5 and Myc genes, whereas LI6650 had lower expression level of Myc than LI1035 and LI6610. LI6652 had lower expression level of ARK5 than LI1035 and LI6610. The log2 FPKM values of ARK5 and Myc in model of LI1035 was 3.9284 and 4.8177, 3.6629 and 5.3658 for LI6610, 3.1228 and -0.0556 for LI6650, -0.3538 and 6.5785 for LI6652. The TGI for LI1035 and LI6610 was 64% (P = 0.0293) and 62% (P = 0.0257) respectively whereas in comparison a smaller TGI of 44% (P = 0.034) was observed in LI6650. In the model of LI6652, TGI for HX301 was 58%, but a statistically significant difference was not observed when comparing the treatment group with the vehicle group (P = 0.138). Conclusions: Our preliminary results demonstrate that HX301 had strong antitumor activity in HCC PDX models expressing both ARK5 and c-Myc. HX301 has the potential to be a first-in-class ARK5i candidate for the treatment of advanced HCC with high expression of c-Myc, warranting further clinical investigation. Citation Format: Zhihua Jiao, Jingjing Wang, Hang Ke, Faming Zhang, Henry Li. HX301, a first-in-class ARK5i, demonstrates antitumor activity in preclinical HCC models with high ARK5/Myc expression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 497.
Background Small‐cell lung cancer (SCLC) is one of the most aggressive types of lung cancer. The prognosis for SCLC patients depends on many factors. The intent of this study was to construct a nomogram model to predict mortality for extensive‐stage SCLC. Methods Original data was collected from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute in the United States. A nomogram prognostic model was constructed to predict death probability for extensive‐stage SCLC. Results A total of 16 554 extensive‐stage SCLC patients from 2004 to 2014 in the SEER database were included in this study. Gender, race, age, TNM staging (including tumor extent, nodal status, and metastasis), and treatment (surgery, chemotherapy, and radiotherapy) were identified as independent predictors for lung cancer‐specific death for extensive‐stage SCLC patients. A nomogram model was constructed based on multivariate models for lung cancer related death and other cause related death. Performance of the two models was validated by calibration and discrimination, with C‐index values of 0.714 and 0.638, respectively. Conclusion A prognostic nomogram model was established to predict death probability for extensive‐stage SCLC. This validated prognostic model may be beneficial for treatment strategy choice and survival prediction.
Abstract Peritoneal dissemination (PD) is the major type of gastric cancer (GC) recurrence and leads to rapid death. Current approach cannot precisely determine which patients are at high risk of PD. In this study, we developed a technology to detect minimal residual cancer cells in peritoneal lavage fluid (PLF) samples by parallel profiling tumor-specific mutations. We applied the technology to a prospective cohort of 110 GC patients. The technology showed ultra-high sensitivity by successfully detecting all the 27 cases that developed PD. The minimal residual cancer cells in PLF was associated with an increased risk of PD (HR = 145.13; 95%CI = 20.20-18435.79; p < 0.001) and significantly shorter overall survival. In pathologically high-risk (T4) patients, the PLF mutation profiling model exhibited even greater specificity of 91% and positive predictive value of 88%, while retaining sensitivity of 100%. PLF cancer cell fraction remained the strongest independent predictor of PD and recurrence-free survival over pathologic diagnosis and cytological diagnosis in GC patients. This approach may help in the postsurgical management of GC patients by detecting PD far before the metastatic lesions grow to significant size detectable by conventional methods such as MRI and CT scanning.
Adv. Funct. Mater. 2019, 29, 1806058 The initially published version of this article contained a typographical error in the affiliation linked with the author Gang Cao (Zhengjiang Chinese Medical University). The correct affiliation details are as follows: Prof. G. Cao School of Pharmacy Zhejiang Chinese Medical University Hangzhou 310053, China E-mail: [email protected] The authors apologize for any inconvenience or misunderstanding that this error may have caused.
Pancreatic adenocarcinoma is one of the leading causes of cancer-related death worldwide. Since little clinical symptoms were shown in the early period of pancreatic adenocarcinoma, most patients were found to carry metastases when diagnosis. The lack of effective diagnosis biomarkers and therapeutic targets makes pancreatic adenocarcinoma difficult to screen and cure. The fundamental problem is we know very little about the regulatory mechanisms during carcinogenesis. Here, we employed weighted gene co-expression network analysis (WGCNA) to build gene interaction network using expression profile of pancreatic adenocarcinoma from The Cancer Genome Atlas (TCGA). STRING was used for the construction and visualization of biological networks. A total of 22 modules were detected in the network, among which yellow and pink modules showed the most significant associations with pancreatic adenocarcinoma. Dozens of new genes including PKMYT1, WDHD1, ASF1B, and RAD18 were identified. Further survival analysis yielded their valuable effects on the diagnosis and treatment of pancreatic adenocarcinoma. Our study pioneered network-based algorithm in the application of tumor etiology and discovered several promising regulators for pancreatic adenocarcinoma detection and therapy.
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