Predict Cancer Patient Research Articles

SurvConvMixer: robust and interpretable cancer survival prediction based on ConvMixer using pathway-level gene expression images

Cancer is one of the leading causes of deaths worldwide. Survival analysis and prediction of cancer patients is of great significance for their precision medicine. The robustness and interpretability of the survival prediction models are important, where robustness tells whether a model has learned the knowledge, and interpretability means if a model can show human what it has learned. In this paper, we propose a robust and interpretable model SurvConvMixer, which uses pathways customized gene expression images and ConvMixer for cancer short-term, mid-term and long-term overall survival prediction. With ConvMixer, the representation of each pathway can be learned respectively. We show the robustness of our model by testing the trained model on absolutely untrained external datasets. The interpretability of SurvConvMixer depends on gradient-weighted class activation mapping (Grad-Cam), by which we can obtain the pathway-level activation heat map. Then wilcoxon rank-sum tests are conducted to obtain the statistically significant pathways, thereby revealing which pathways the model focuses on more. SurvConvMixer achieves remarkable performance on the short-term, mid-term and long-term overall survival of lung adenocarcinoma, lung squamous cell carcinoma and skin cutaneous melanoma, and the external validation tests show that SurvConvMixer can generalize to external datasets so that it is robust. Finally, we investigate the activation maps generated by Grad-Cam, after wilcoxon rank-sum test and Kaplan–Meier estimation, we find that some survival-related pathways play important role in SurvConvMixer.

Cellular senescence and nanoparticle-based therapies: Current developments and perspectives

Abstract The timing and location of senescent cells in vivo is a leading candidate explanation for human aging. A rapidly developing scientific field with the potential to slow the aging process is the creation of pharmacologically active medicines that target senescent cells. Senotherapeutics have been developed to selectively or preferentially target and eliminate senescent cells. Senolytic compounds that delay aging in animal models are being explored in humans with great hope. Nanoparticle (NP) drug delivery strategies for targeting senescent cells are in their infancy, but advancements have been made, and preliminary anti-aging applications are promising. However, using nanomedicine effectively requires an understanding of how NPs behave in senescent cells. Senescence theranostics could offer a variety of information, including a prognostic predictor in cancer patients after treatment. The NPs have a much better outlook for translating it to the clinic for aging. Reversing aging pathologies may only require a percentage reduction in senescent cells to achieve therapeutic success, in contrast to cancer, where it is essential to eradicate the tumor. This review provides an overview of the factors that lead to senescence and different therapeutic approaches, focusing on the use of nanocarriers/particles in senotherapy.

Smartphone-based inertial measurements during Chester step test as a predictor of length of hospital stay in abdominopelvic cancer postoperative period: a prospective cohort study

BackgroundObjective assessment of pre-operative functional capacity in cancer patients using the smartphone gyroscope during the Chester step (CST) test may allow greater sensitivity of test results. This study has investigated whether the CST is a postoperative hospital permanence predictor in cancer patients undergoing abdominopelvic surgery through work, VO2MAX and gyroscopic movement analysis.MethodsProspective, quantitative, descriptive and inferential observational cohort study. Fifty-one patients were evaluated using CST in conjunction with a smartphone gyroscope. Multivariate linear regression analysis was used to examine the predictive value of the CST.ResultsThe duration of hospital permanence 30 days after surgery was longer when patients who performed stage 1 showed lower RMS amplitude and higher peak power. The work increased as the test progressed in stage 3. High VO2MAX seemed to be a predictor of hospital permanence in those who completed levels 3 and 4 of the test.ConclusionThe use of the gyroscope was more accurate in detecting mobility changes, which predicted a less favorable result for those who met at level 1 of the CST. VO2MAX was a predictor of prolonged hospitalization from level 3 of the test. The work was less accurate to determine the patient's true functional capacity.

Predictors of cancer patients' utilization of psychooncological support: Examining patient´s attitude and physician´s recommendation

PurposePatients with cancer suffer from a wide range of psychological distress. Nevertheless, in the literature low utilization rates of psychooncological services are reported. Various factors may influence the utilization of professional support during inpatient care. Up to now it is unclear to what extent patients’ attitude towards psychooncological support and physicians’ recommendation for psychooncological care may influence the utilization.MethodsIn a multicenter longitudinal observational study in Comprehensive Cancer Centers Germany, 1398 patients with mixed cancer diagnoses were assessed at baseline during their hospital stay with respect to psychooncological distress and the need for and use of psychooncological services.ResultsPsychooncological support was used by almost 28.4% of patients up to this time. A positive attitude towards psychooncological support was reported by 41.6%. A recommendation of psychooncological support by a physician was received by 16.2%. These patients reported a significant higher level of distress compared to patients who did not received a recommendation. Multivariable logistic regression detected that the utilization rate was 3.79 times higher among patients with positive attitude towards psychooncological support (OR, 3.79; 95% CI 2.51–5.73, p < 0.001). Utilization was 4.21 times more likely among patients who received a physician´s recommendation (OR, 4.21; 95% CI 2.98–5.95, p < 0.001).ConclusionThe results of the study provide evidence of the relevance of giving more attention to psychooncological distress and attitudes towards psychooncological care. To reduce reservations, patients need low-threshold information about the psychooncological services offered.

A potential way of detecting circulating tumor cells via laser scanning

Circulating tumor cells (CTCs) are generally considered as seeds for metastases, serving as a clinical biomarker for cancer diagnosis. Due to their low content in the blood, detection of CTCs normally requires an antibody-mediated biological enrichment process, which is rather complicated and time-consuming. In this work, we demonstrate a potential, fast, and cost-effective way of detecting CTCs. That is, CTCs are physically isolated by size using filtration and imaged optically via laser scanning. The filtered and stained blood sample, which is placed on a moving and spinning stage, is illuminated by a 405 nm diode laser. Scattered optical signals are collected by using an ellipsoidal reflective mirror for dark-field imaging. Then CTCs in dark-field images are screened and counted by the clustering algorithm. Our method possesses the advantages of antibody-free and low cost, holding great promise for early diagnosis and prognosis prediction of cancer patients.

Copy number alterations in cancer: detection and precision medicine

Copy number alterations (CNAs) are genomic alterations where some regions exhibit more or less copy number than the normal two copies. In this talk, I will describe two ideas: (1) how CNAs are estimated from data generated by next generation sequencing (NGS) and what steps are required to make the data interpretable, (2) how the CNA can be utilised for precision medicine in terms of prediction of tumour subtypes and prediction of cancer patients’ survival. If time permits, I will also discuss how to estimate genomic markers from CNA profile across cancer patients.

Prevalence and predictors of cancer patients' unexpressed needs in the admission interview of inpatient rehabilitation.

The admission interview in oncological inpatient rehabilitation might be a good opportunity to identify cancer patients' needs present after acute treatment. However, a relevant number of patients may not express their needs. In this study, we examined (a) the proportion of cancer patients with unexpressed needs, (b) topics of unexpressed needs and reasons for not expressing needs, (c) correlations of not expressing needs with several patient characteristics, and (d) predictors of not expressing needs. We enrolled 449 patients with breast, prostate, and colon cancer at beginning and end of inpatient rehabilitation. We obtained self-reports about unexpressed needs and health-related variables (quality of life, depression, anxiety, adjustment disorder, and health literacy). We estimated frequencies and conducted correlation and ordinal logistic regression analyses. A quarter of patients stated they had "rather not" or "not at all" expressed all relevant needs. Patients mostly omitted fear of cancer recurrence. Most frequent reasons for not expressing needs were being focused on physical consequences of cancer, concerns emerging only later, and not knowing about the possibility of talking about distress. Not expressing needs was associated with several health-related outcomes, for example, emotional functioning, adjustment disorder, fear of progression, and health literacy. Depression measured at the beginning of rehabilitation showed only small correlations and is therefore not sufficient to identify patients with unexpressed needs. A relevant proportion of cancer patients reported unexpressed needs in the admission interview. This was associated with decreased mental health. Therefore, it seems necessary to support patients in expressing needs.

Sarcoma Does Not Predict Malnutrition in Cancer Patients: A Retrospective Cohort Study

Abstract Objectives The association between inflammation, malnutrition, and cancer is not well understood. The aim of this study was to examine the association between inflammatory-type cancer and diagnosed malnutrition, albumin level, and age in patients with cancer. Methods Malnutrition and cancer diagnoses were obtained using data from hospital medical records in patients admitted for cancer between Oct. 2017 and Dec. 2018. Demographics, as well as the first and lowest albumin levels were also obtained. A simple t-test is processed between age and malnutrition status. Also a chi-square test of independence was performed to examine the relation between malnutrition and hypoalbuminemia status. Logistic regression was conducted between malnutrition status, sarcoma cancer, age, and hypoalbuminemia. Results The study included 4034 patients (2084 males, 1949 females). Approximately 4% of the patients were diagnosed with malnutrition. Logistic regression on malnutrition status, sarcoma, age, and hypoalbuminemia showed a significant association on global test (3, 2433, P-value = 0.013). Hypoalbuminemia (&amp;lt; 3.9 g/dL lower-limit) was significantly associated with malnutrition (X2 1, 2433 P-value = 0.0156). Sarcoma diagnosis was not significantly associated with malnutrition (X2 1, 2433 P-value = 0.267). Age is not significantly related to malnutrition status (X2 1, 2433 p-value = 0.449). A t-test was also performed malnutrition vs no malnutrition on age, resulting in a marginally significant association for malnutrition group (M = 65.33, SD = 15.50) vs no malnutrition group (M = 67.32, SD = 17.55) (t(1) = 3.7212, P = 0.0537). Conclusions Sarcoma is not significantly associated with an increased risk of malnutrition. Cancer patients with hypoalbuminemia have a higher risk for malnutrition compared to the patients with normal albumin level. Additionally, age may be a predictor for cancer patients’ risk of in-hospital malnutrition. Funding Sources None.

Dynamic 30-day readmission prediction for cancer patients via clinical embeddings.

2040 Background: Existing models typically predict unplanned 30-day readmission for cancer patients at discharge1. Performing prediction dynamically during hospital stay may allow earlier intervention for high risk patients. In addition, readmission risk may be associated with the outcome of a variety of labs and diagnoses. Models including all those elements may not be practical due to large number of variables relative to number of samples. Embeddings have the potential to represent medical concepts in low dimensional spaces2. In this study, we developed a machine learning model utilizing embedding representations of ICD and LOINC codes to dynamically predict readmission risk. Methods: This is a single institutional study examining inpatient 30-day unplanned readmissions from Jan 2013 to Dec 2016 (n = 16361 total, n = 5685 in hematology). The readmission rate was 18% (24% for hematology). We used gradient boosted trees models with 10-fold cross validation and included baseline factors that are typically available shortly after admission: gender, age, service, admission count within 6 months, insurance, emergency admission, admission year, allogeneic or autologous stem cell transplant (hematology only). For dynamic factors, we randomly selected a timepoint (TP, median = 2.4 days) during each visit. We utilized publicly available clinical embeddings2 to generate 300 dimensional representations for ICD9s and LOINCs in the patients’ Electronic Medical Records. We considered diagnoses (ICD9) between 6 months prior to admission and TP, and lab tests (LOINC) ordered between admission time and TP. We used records from Jan 2017 to Dec 2017 for prospective validation (n = 3785 total, n = 1424 in hematology), with 17% readmission rate (22% for hematology). Results: Prospective validation Area Under Receiver Operating Characteristic Curve (AUC) using baseline factors were 0.72 (average precision “AP” = 0.33) and 0.65 (AP = 0.32) for overall and hematology populations, respectively. By including dynamic factors, we obtained AUCs of 0.74 (AP = 0.4) and 0.7 (AP = 0.39) for overall and hematology populations, corresponding to 3% and 8% AUC (21% and 22% AP) improvements, respectively. Conclusions: We found that dynamic readmission prediction utilizing clinical embeddings improves the prediction performance comparing with using baseline factors only. The model shows potential to improve patient care and reduce costs by predicting and preventing readmissions when the patient is still in the hospital. 1 J Surg Oncol 2018; 117:1113-1118. 2 AMIA Jt Summits Transl Sci Proc. 2016;41-50.

Identification of a prognostic immune signature for cervical cancer to predict survival and response to immune checkpoint inhibitors

ABSTRACTCervical cancer (CC) is a leading cause of cancer-related death in women. Limited studies have investigated whether immune-related genes (IRGs) or tumor immune microenvironment (TIME) could be indicators for CC prognoses. The aim of this study was to develop an improved prognostic signature for CC based on IRGs or TIME to predict survival and response to immune checkpoint inhibitors (ICIs). A prognostic signature was constructed using bioinformatics method and its predictive capability was validated. The mechanisms underlying the signature’s predictive capability were explored with CIBERSORT algorithm and mutation analysis. Immunophenoscore (IPS) is validated for ICIs response, and was therefore explored in relation to the signature. A prognostic signature based on 11 IRGs was developed. A multivariate analysis revealed that the 11-IRG signature was an independent prognostic factor for overall survival (OS) and progression-free interval in CC patients. In the 11-IRG signature high-risk group, CD8 T cells and resting mast cells, which are found to associate with better OS in our study, were lower; activated mast cells, associated with poorer OS, were higher, compared with the low-risk group. An IPS analysis suggested that the 11-IRG signature low-risk group, which possessed a higher IPS, represented a more immunogenic phenotype that was more inclined to respond to ICIs. In short, an 11-IRG prognostic signature for predicting CC patients’ survival and response to ICIs was firmly established. The predictive capability of this model in CC requires further testing with the goal of better prognostic stratification and treatment management.

ShenQi FuZheng Injection ameliorates fatigue-like behavior in mouse models of cancer-related fatigue

Cancer-related fatigue (CRF) not only has a negative impact on work, daily activities and social relationships, but also be a predictor of cancer patients' survival. And it has no FDA-approved therapy. ShenQi FuZheng Injection (SFI) is clinically used for adjuvant treatment of lung cancer and gastric cancer. And we found that SFI can improve the incidence of CRF in patients with gastric cancer. However, its efficacy against CRF remains to be elucidated. In the present study we established a tumor-bearing mouse fatigue model, conducted the forced swim test (FST) and grip strength measurements to evaluate the therapeutic effect of SFI. Additionally, we detected inflammation and immune indicators. The result showed that SFI administration could reduce depressive-like behaviors in tumor-bearing mice and inhibit tumor growth. In addition, SFI might improve fatigue symptoms by inhibiting pro-inflammatory cytokines produced by peripheral immune cells, restrain the dysfunction of exhausted T cells and improve the anti-tumor immunity through the targets of PDL1, TIM3 and FOXP3. These data suggested that SFI might be useful in alleviating CRF and provide further support to confirm SFI as a potential therapy for CRF in humans.

Clinical significance of retinoic acid-induced protein I expression in human gastric cancer

Objective To investigate the clinical significance and regulation of retinoic acid-induced protein I (RIG-I) in human gastric cancer. Methods Immunohistochemistry was used to study RIG-I expression in gastric cancer and normal tissues. We used Wilcoxon test to analyze the difference of RIG-I expression levels in cancer and normal tissues. Chi-square and Log-rank tests were used to analyze the RIG-I expression in cancer tissues in relation to patients’ clinical parameters and prognoses respectively. The gastric cancer cell lines SGC-7901 and AGS were used to construct the stable knockdown expression of RIG-I using RNA interference (RNAi) method. The cell counting kit-8 (CCK-8), wound healing, and Transwell assays were used to examine the cellular function after RIG-I knockdown of these cells. Results RIG-I expression level in human gastric cancer tissues was significantly lower than that in adjacent normal tissues (U=1 490, P=0.001). The ratio of higher RIG-I expression in cancer patients with pathological stage (Ⅲ-Ⅳ) was significantly lower than that in normal tissues (χ2=4.668, P=0.031). The overall survival of the patients with higher RIG-I expression was significantly longer than that with lower RIG-I expression (P=0.023). The COX model showed that the age (P=0.047), TNM stage (P=0.039) and the RIG-I expression (P=0.007) could be used as independent prognostic predictors for cancer patients. CCK-8 assay showed that at the time points of 48 h and 72 h after culture, the A450 value in LV-RIG-I-short hairpin RNA (shRNA) group was significantly higher than that in LV-NC group (for SGC7901: P=0.000 and P=0.000 respectively; for AGS: P=0.002 and P=0.016 respectively). The wound healing assay showed that the cell migration abilities in LV-RIG-I-shRNA group was significantly stronger than LV-NC group (for SGC7901: P=0.001; for AGS: P=0.004). The Transwell assay showed that the cell invasion abilities in LV-RIG-I-shRNA group was significantly stronger than that in LV-NC group (for SGC7901: P=0.032; for AGS: P=0.016). Conclusion RIG-I plays important roles in gastric cancer progression, its knockdown could promote proliferation, migration and invasion of cancer cells, and thus could be used as potential therapeutic target for gastric cancer. Key words: Retinoic acid-induced protein I; Gastric cancer; RNA interference; Immunohistochemistry

Toward the precision breast cancer survival prediction utilizing combined whole genome-wide expression and somatic mutation analysis

BackgroundBreast cancer is the most common type of invasive cancer in woman. It accounts for approximately 18% of all cancer deaths worldwide. It is well known that somatic mutation plays an essential role in cancer development. Hence, we propose that a prognostic prediction model that integrates somatic mutations with gene expression can improve survival prediction for cancer patients and also be able to reveal the genetic mutations associated with survival.MethodDifferential expression analysis was used to identify breast cancer related genes. Genetic algorithm (GA) and univariate Cox regression analysis were applied to filter out survival related genes. DAVID was used for enrichment analysis on somatic mutated gene set. The performance of survival predictors were assessed by Cox regression model and concordance index(C-index).ResultsWe investigated the genome-wide gene expression profile and somatic mutations of 1091 breast invasive carcinoma cases from The Cancer Genome Atlas (TCGA). We identified 118 genes with high hazard ratios as breast cancer survival risk gene candidates (log rank p < 0.0001 and c-index = 0.636). Multiple breast cancer survival related genes were found in this gene set, including FOXR2, FOXD1, MTNR1B and SDC1. Further genetic algorithm (GA) revealed an optimal gene set consisted of 88 genes with higher c-index (log rank p < 0.0001 and c-index = 0.656). We validated this gene set on an independent breast cancer data set and achieved a similar performance (log rank p < 0.0001 and c-index = 0.614). Moreover, we revealed 25 functional annotations, 15 gene ontology terms and 14 pathways that were significantly enriched in the genes that showed distinct mutation patterns in the different survival risk groups. These functional gene sets were used as new features for the survival prediction model. In particular, our results suggested that the Fanconi anemia pathway had an important role in breast cancer prognosis.ConclusionsOur study indicated that the expression levels of the gene signatures remain the effective indicators for breast cancer survival prediction. Combining the gene expression information with other types of features derived from somatic mutations can further improve the performance of survival prediction. The pathways that were associated with survival risk suggested by our study can be further investigated for improving cancer patient survival.

CXCR3 is a prognostic marker and a potential target for patients with solid tumors: a meta-analysis.

ObjectiveTo deeply verify the clinical significance of CXCR3 in prediction of cancer patients’ prognosis.Data sourcesWe performed a meta-analysis including 12 studies searched from PubMed, Web of Science, Embase, and Cochrane databases. A total of 1,751 patients were used to analyze the association between CXCR3 and patients’ prognosis, based on either overall survival or time to tumor progression.Study selectionStudies evaluating CXCR3 expression for predicting prognosis in human solid tumors were included.ResultsIt showed that patients with higher expression of CXCR3 had significantly shorter OS (pooled hazard ratio =2.315, 95% CI: 1.162–4.611, P=0.017). In addition, higher CXCR3 expression was associated with distant metastasis (yes vs no: pooled relative ratio [RR] =1.828, 95% CI: 1.140–2.931, P=0.012) in solid tumors and indicated advanced tumor stage (III/IV vs I/II, RR =2.656, 95% CI: 1.809–3.900, P<0.001) and lymph node metastasis (yes vs no: RR =2.28, 95% CI: 1.61–3.25, P<0.001) in colorectal cancer.ConclusionOur study highlights the role of CXCR3 as a potential prognostic marker and a promising therapeutic target in solid tumors.

High mobility group protein A2 overexpression indicates poor prognosis for cancer patients: a meta-analysis.

Overexpression of the high mobility group protein A2 (HMGA2), an architectural transcription factor, has been linked to poor prognosis in many malignancies, although this remains controversial. Herein, we conducted a meta-analysis to investigate whether HMGA2 has prognostic value, and evaluated the association between HMGA2 and clinicopathologic factors in malignancies. A total of 29 studies involving 4114 patients were included in this meta-analysis. The pooled results demonstrated that elevated HMGA2 predicted a poor overall survival (OS) (hazard ratio [HR] = 1.82; 95% confidence interval [CI] = 1.62–2.05; P < 0.001) and disease-free survival/progression-free survival/recurrence-free survival (HR = 1.94; 95% CI = 1.27–2.98; P = 0.002). Subgroup analysis conducted by study region, sample size, detection method, and analysis method indicated that HMGA2 overexpression correlated with poor OS. Furthermore, HMGA2 overexpression was found to be linked to poor OS in various cancers except ovarian cancer (pooled HR = 1.14; 95% CI = 0.62–2.09; P = 0.673). High HMGA2 expression level also correlated with advanced TNM stage (OR = 2.44; 95% CI =1.87–3.2; P < 0.001), lymphovascular invasion (OR = 2.46, 95% CI = 1.67–3.64; P < 0.001), distant metastasis (OR = 2.66; 95% CI =1.51–4.69; P < 0.001), and lymph node metastasis (OR = 1.83; 95% CI =1.27–2.64; P = 0.001). In conclusion, HMGA2 overexpression indicates a worse prognosis and may serve as a prognostic predictor in cancer patients.

The role of tumor grade, demographics and psychological factors in Cancer survivorship

Introduction: The aim of this study was to predict cancer survival according to the role of demographic determinants, psychological factors and tumor grade. Methods: In this cross-sectional retrospective study, 350 patients with blood, breast, prostate, lung, stomach and colon cancers (173 patients with cancer survival for more than 2 years and 177 patients with cancer survival for 2 years or less) were selected by convenience sampling method among patients who referred to the Semnan Kosar Hospital and Specialized Cancer Treatment Centers in Semnan. Data was collected by the Posttraumatic Growth Inventory (PTGI), Inner Strength Questionnaire (ISQ), Bell Object Relations and Reality Testing Inventory (BORRTI), Self-Efficacy Scale for Self-Management (SESSM) and disease-demographic characteristics questionnaires. Data were analyzed by binary logistic regression analysis using the SPSS 20 software. Results: Age (P<0.001) and low-grade tumors (P<0.01) have reverse correlation and the post-traumatic growth has positive significant correlation (P<0.01) with cancer survival for more than 2 years survival group. Age (Exp=1.10), low-grade tumors (Exp=4.37) and post-traumatic growth (Exp=1.06) can predict cancer survival for more than 2 years in the cancer patients (P<0.01). Conclusion: Considering the role of age, tumor grade and post-traumatic growth in the prediction of cancer patients’ survival, it can be suggested that preventive programs are important to detect and diagnose cancer and administer tailored interventions in the critical time period.

Mucin-Type O-Glycosylation in Gastric Carcinogenesis.

Mucin-type O-glycosylation plays a crucial role in several physiological and pathological processes of the gastric tissue. Modifications in enzymes responsible for key glycosylation steps and the consequent abnormal biosynthesis and expression of their glycan products constitute well-established molecular hallmarks of disease state. This review addresses the major role played by mucins and associated O-glycan structures in Helicobacter pylori adhesion to the gastric mucosa and the subsequent establishment of a chronic infection, with concomitant drastic alterations of the gastric epithelium glycophenotype. Furthermore, alterations of mucin expression pattern and glycan signatures occurring in preneoplastic lesions and in gastric carcinoma are also described, as well as their impact throughout the gastric carcinogenesis cascade and in cancer progression. Altogether, mucin-type O-glycosylation alterations may represent promising biomarkers with potential screening and prognostic applications, as well as predictors of cancer patients’ response to therapy.

Physical Activity Enjoyment and Self-Efficacy As Predictors of Cancer Patients' Physical Activity Level.

Background: Physical activity (PA) can support cancer patients during medical treatment by reducing side-effects and increasing quality of life. However, PA levels mostly decline after diagnosis. Which factors can explain if patients are able to remain or even increase their PA level? Self-efficacy is an important cognitive factor that has been linked to cancer patients' PA across many studies. In contrast, affective factors such as PA enjoyment have rarely been examined. We compare the influence of self-efficacy and PA enjoyment on cancer patients' PA levels after completion of an exercise or stress-management intervention.Methods: Outpatient cancer patients [N = 72; 54% female; M = 56 years, SD = 12.34; most with breast or colon cancer (34%, 15%)] were enrolled in the MOTIVACTION study, a 4-week intervention (1 h counseling followed by weekly phone calls), with pre-test (T1), post-test (T2), and a 10-week follow-up (T3). Participants were randomized to either an exercise intervention (emphasizing self-regulatory strategies for behavior change) or to a stress management intervention (coping and relaxation techniques). Sixty-seven patients remained in the study and completed the SQUASH assessment of PA, a measure of maintenance self-efficacy (7 items, Cronbach's α = 0.88) and PA enjoyment (2 items, Cronbach's α = 0.89). Regression analyses were calculated with PA level (at T2 and T3) as dependent variable and relative weight analyses were conducted. The study was registered at clinicalTrials.gov (unique identifier:NCT01576107; URL: https://clinicaltrials.gov/ct2/show/NCT01576107?term=motivaction&rank=1).Results: Baseline self-efficacy and change in PA enjoyment significantly predicted cancer patients' PA level at T2 adjusting for baseline PA and type of intervention. Relative weight (RW) analysis revealed that PA enjoyment (baseline and change together) explained 34.3% of the dependent variable, self-efficacy (baseline and change) explained 38.4%. At follow-up, self-efficacy was still a significant predictor of PA (RW = 74.6%), whereas PA enjoyment was no longer a relevant factor (RW = 5.2%).Conclusion: The affective factor PA enjoyment was equally important as self-efficacy for predicting cancer patient' PA level directly after completion of the intervention. Reasons for the reduced relevance at follow-up and a broader range of affective factors should be analyzed in future studies on cancer patients' PA level.

Value of oncogenes for the prediction of brain metastases at initial diagnosis: a review of published data.

Identifying cancer patients who are at high risk of developing brain metastases at initial diagnosis and applying effective intervention or monitoring strategies is of vital importance. Recent advances in the biology of brain metastases revealed that some oncogenes from primary tumors may be potential markers for identifying cancer patients likely to metastasize to the brain. We here summarize data on the mechanisms of brain metastases supporting the involvement of oncogene changes in the brain metastatic evolution. We also review the available evidence on clinical studies of oncogenes in the prediction of cancer patients with high incidence of brain metastases.

Simple Patterned Nanofiber Scaffolds and Its Enhanced Performance in Immunoassay

Cancer has become the leading cause of death worldwide; early diagnosis and treatment of cancers is critical for the survival of the patients. The concentration of cancer markers in easy-to-access biological fluids can provide great assistance in screening for occult primary cancers, distinguishing malignant from benign findings, determining prognosis and prediction for cancer patients. The multiplex detection technology of a panel of cancer markers can greatly increase the accuracy of disease diagnosis. Herein, we briefly fabricate a high-throughput micro-immunoassay based on the electrospun polystyrene (PS) substrates to improve detection sensitivity. The immunoassay was evaluated by analyzing three different cancer biomarkers (AFP, CEA, VEGF). For AFP, CEA, VEGF immunofluorescence assay, the LOD of assay conducted on electrospun PS substrates before or after plasma and the conventional PS substrates were 0.42, 0.10, 1.12 ng/mL, 0.57, 0.09, 1.24 ng/mL, and 159.75, 26.19, 385.59 pg/mL, respectively (P < 0.05). Due to the high porosity and large surface area-to-volume ratio which is the foremost merit of nanostructures, and the plasma treatment which make the hydrophobic PS nanofibers hydropholic, the nanofibers substrates showed sufficient retention of immunoassay functionality and high potential for capture molecules immobilization. Consequently, the immunofluorescence assay conducted on electrospun PS substrates could significantly enhance the sensitivity and limits of detection.