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Chronic inflammation is a prerequisite for the development of cancers. Here, we present the framework of a novel theory termed as Cancer Evolution-Development (Cancer Evo-Dev) based on the current understanding of inflammation-related carcinogenesis, especially hepatocarcinogenesis induced by chronic infection with hepatitis B virus. The interaction between genetic predispositions and environmental exposures, such as viral infection, maintains chronic non-resolving inflammation. Pollution, metabolic syndrome, physical inactivity, ageing, and adverse psychosocial exposure also increase the risk of cancer via inducing chronic low-grade smoldering inflammation. Under the microenvironment of non-resolving inflammation, pro-inflammatory factors facilitate the generation of somatic mutations and viral mutations by inducing the imbalance between the mutagenic forces such as cytidine deaminases and mutation-correcting forces including uracil–DNA glycosylase. Most cells with somatic mutations and mutated viruses are eliminated in survival competition. Only a small percentage of mutated cells survive, adapt to the hostile environment, retro-differentiate, and function as cancer-initiating cells via altering signaling pathways. These cancer-initiating cells acquire stem-ness, reprogram metabolic patterns, and affect the microenvironment. The carcinogenic process follows the law of “mutation-selection-adaptation”. Chronic physical activity reduces the levels of inflammation via upregulating the activity and numbers of NK cells and lymphocytes and lengthening leukocyte telomere; downregulating proinflammatory cytokines including interleukin-6 and senescent lymphocytes especially in aged population. Anti-inflammation medication reduces the occurrence and recurrence of cancers. Targeting cancer stemness signaling pathways might lead to cancer eradication. Cancer Evo-Dev not only helps understand the mechanisms by which inflammation promotes the development of cancers, but also lays the foundation for effective prophylaxis and targeted therapy of various cancers.

Background Mechanical thrombectomy (MT) is an effective treatment for large-vessel occlusion in acute ischemic stroke, however, only some revascularized patients have a good prognosis. For stroke patients undergoing MT, predicting the risk of unfavorable outcomes and adjusting the treatment strategies accordingly can greatly improve prognosis. Therefore, we aimed to develop and validate a nomogram that can predict 3-month unfavorable outcomes for individual stroke patient treated with MT. Methods We analyzed 258 patients with acute ischemic stroke who underwent MT from January 2018 to February 2021. The primary outcome was a 3-month unfavorable outcome, assessed using the modified Rankin Scale (mRS), 3–6. A nomogram was generated based on a multivariable logistic model. We used the area under the receiver-operating characteristic curve to evaluate the discriminative performance and used the calibration curve and Spiegelhalter’s Z-test to assess the calibration performance of the risk prediction model. Results In our visual nomogram, gender (odds ratio [OR], 3.40; 95%CI, 1.54–7.54), collateral circulation (OR, 0.46; 95%CI, 0.28–0.76), postoperative mTICI (OR, 0.06; 95%CI, 0.01–0.50), stroke-associated pneumonia (OR, 5.76; 95%CI, 2.79–11.87), preoperative Na (OR, 0.82; 95%CI, 0.72–0.92) and creatinine (OR, 1.02; 95%CI, 1.01–1.03) remained independent predictors of 3-month unfavorable outcomes in stroke patients treated with MT. The area under the nomogram curve was 0.8791 with good calibration performance (P = 0.873 for the Spiegelhalter’s Z-test). Conclusions A novel nomogram consisting of gender, collateral circulation, postoperative mTICI, stroke-associated pneumonia, preoperative Na and creatinine can predict the 3-month unfavorable outcomes in stroke patients treated with MT.

Background Tissue plasminogen activator (t‐PA) is an effective therapy for acute ischemic stroke, but some patients still have poor clinical outcome. In this study, we investigated clinical characteristics of stroke patients and determined predictors for poor clinical outcome in response to t‐PA treatment. Methods Clinical data from 247 patients were retrospectively reviewed. Clinical parameters that were associated with survival of patients were analyzed. Areas under receiver operating characteristic curves (ROC) were used to determine the feasibility of using various combinations of the clinical parameters to predict poor clinical response. The clinical outcome was defined according to the changes in Modified Rankin Scale. Results Overall, 145 patients had improved/complete recovery, 73 had no change, and 29 had worsening conditions or died during the in‐clinic period. A univariate analysis showed that baseline characteristics including age, CRP, blood glucose level, systolic blood pressure, and admission NIHSS were significantly different (p < 0.05) among patients with different clinical outcome. A further multivariate analysis was then performed. Variables associated with poor clinical outcome (worsening/death) (p < 0.1) were included in the logistic regression model. Four parameters were retained in the model: Age, CRP, Blood glucose level, and Systolic blood pressure (ACBS). To allow a convenient usage of the ACBS classifier, the parameters were put into a scoring system, and the score at 7.7 was chosen as a cut‐off. The ROC curve of this ACBS classifier has an area under the curve (AUC) of 0.7788, higher than other individual parameters. The ACBS classifier provided enhanced sensitivity of 69.2% and specificity of 74.3%. Conclusion The ACBS classifier provided a satisfactory power in estimating the patients’ clinical outcome. After further validating, the classifier may provide important information to clinicians for making clinical decisions.

To determine the incidence, risk factors, and relative survival of acute myeloid leukemia (AML) secondary to myelodysplastic syndrome (MDS) in the Surveillance, Epidemiology, and End Results (SEER) database. Retrospective analysis of all patients with new MDS onset in the SEER‐18 database from 2001 to 2013. We identified 36 558 patients with primary MDS. The rate of secondary AML (sAML) was 3.7% among patients 40 years or younger and 2.5% among those older than 40 (P = .039). The median transformation interval was significantly shorter for the younger group (4.04 vs 13.1 mo; P < .001). For both age groups, median overall and cancer‐specific survival were significantly longer for patients who did not develop sAML. Although the younger patients survived longer than the older patients, sAML development had a more negative effect on the survival of younger patients. Female sex, age, and World Health Organization (WHO) type MDS with single lineage dysplasia (MDS‐SLD) were associated with a decreased risk of sAML for older but not younger patients. Among older patients with MDS, a married status, Black race, female sex, shorter time to sAML, and WHO type MDS‐SLD or MDS with ringed sideroblasts were favorable prognostic factors for survival. In the SEER database, the rate of sAML among patients with MDS is lower than that in previous reports, but these patients still have worse survival. Risk assessment should include clinical and demographic factors.