Cancer cachexia and cancer-associated thrombosis are potentially fatal outcomes of advanced cancer. Unfortunately, this knowledge has not yet led to any breakthrough in cancer therapy. Thrombin is the key enzyme of blood coagulation system. The identification of a direct link between thrombin and the tumor progression remains unknown. We illustrated thrombin expression in lung adenocarcinoma (LUAD) was closely related to clinicopathological features, prognosis, and chemotherapy outcome of patients via TCGA and clinical pathological analysis. Using genetic and pharmacological approaches, we showed a direct link between thrombin catalytic activity and lung cancer progression in vitro and in vivo. Furthermore, we revealed that thrombin cleaves epidermal growth factor receptor (EGFR) at a GRG motif perfectly conserved across disparate species, indicating functional importance, which results in activation of EGFR/AKT/mTOR signaling pathway. Last we found the mutual interaction between thrombin and chemotherapy resistance. Combination therapy of thrombin inhibitor and chemotherapy results in improved anti-tumor efficacy. Together, our data firstly revealed a mechanism of cancer progression and chemotherapy resistance that involves thrombin-mediated EGFR cleavage. We propose that thrombin could be a prognostic biomarker for lung cancer, blockade of thrombin is a valuable therapeutic strategy to overcome cancer's resistance to chemotherapy.

Advances in cancer treatment have increased childhood cancer patient's survival rates. However, many childhood cancer survivors (CCS) face long-term effects such as fatigue. This study assessed fatigue in CCS and healthy controls (HCs), its contributors, and associated outcomes.

This cross-sectional study included 90 CCS and 55 age and sex-matched HCs. Fatigue was measured using the Pediatric Quality of Life Multidimensional Fatigue Scale, and modifiable contributors included sleep quality (Pittsburgh Sleep Quality Index [PSQI]) and physical fitness (VO2 peak during a treadmill test). Quality of life (QoL) and emotional and cognitive functioning were evaluated using the Pediatric Quality of Life Inventory, Beck Depression Inventory II, Cognitive Failure Questionnaire, and the Impact of Event Scale-Revised. Stepwise linear regressions identified predictors of general, sleep/rest, cognitive, and total fatigue.

General, cognitive, and total fatigue were significantly higher in CCS compared to HCs (44.7% vs. 23%, p < .001; cognitive: 64.7% vs. 29.3%, p < .001; total fatigue: 56.5% vs. 25%, p < .001). Sex, PSQI, PSQI × sex, and PSQI × VO₂ were significant predictors for general fatigue, PSQI and PSQI × VO₂ for cognitive fatigue, and PSQI for sleep/rest and total fatigue. Cognitive and total fatigue correlated most strongly with cognitive and work-related functioning, whereas general and sleep/rest fatigue were more related to psychosocial functioning.

Fatigue is highly prevalent among CCS, with distinct factors influencing general, sleep/rest, cognitive, and total fatigue. Female survivors, those with poorer sleep quality and lower physical fitness, are at risk.

This study underscores the need for tailored interventions for each type of fatigue. Improving sleep quality, physical fitness, and psychological well-being may contribute to reducing fatigue and enhancing overall quality of life in CCS.

Anxiety and depression are common symptoms in oncology patients undergoing chemotherapy. Study purpose was to evaluate for differences in severity of common symptoms (ie, fatigue, energy, sleep disturbance, cognitive function, pain) and quality of life (QOL) outcomes among three subgroups of oncology outpatients with distinct joint anxiety and depression profiles.

Oncology outpatients (N = 1328) completed measures of state anxiety and depression, six times over two cycles of chemotherapy. Latent profile analysis was done to identify subgroups of patients with distinct joint state anxiety AND depression profiles. Patients completed measures of trait anxiety, morning and evening fatigue, morning and evening energy, sleep disturbance, cognitive function, and pain, as well as generic and disease-specific measures of QOL at enrollment. Differences among the classes in symptom severity scores and QOL scores were evaluated using parametric and non-parametric tests.

Three distinct joint anxiety AND depression profiles were identified and named: Low Anxiety and Low Depression (57.5%, Both Low), Moderate Anxiety and Moderate Depression (33.7%, Both Moderate), and High Anxiety and High Depression (8.8%, Both High). All of the symptom severity scores showed a "dose-response effect" (ie, as the joint anxiety AND depression profiles worsened, the severity of all of the symptoms increased). Likewise, for both the general and disease-specific QOL (except spiritual well-being) measures, all of the scores decreased as the joint anxiety AND depression profiles worsened. Compared to the Both Low classes, the other two classes reported lower scores for the spiritual well-being domain.

More than 40% of patients receiving chemotherapy experience moderate to high levels of both anxiety AND depression. These patients report an extremely high symptom burden and significant decrements in all domains of QOL.

Clinicians need to perform comprehensive assessments of depression and anxiety and other common symptoms and QOL outcomes during chemotherapy. In addition, referrals for targeted interventions are needed to manage multiple symptoms and improve patients' QOL.

Children with acute lymphoblastic leukemia experience various adverse symptoms during remission induction. Elucidating the interrelationships among symptoms can facilitate precise and efficacious symptom management.

This study aimed to elucidate symptom clusters and sentinel symptoms and to examine core and bridge symptoms within the symptom network in children with acute lymphoblastic leukemia during remission induction.

A cross-sectional survey of 226 children aged 8 to 16 years with a new diagnosis of acute lymphoblastic leukemia during remission induction chemotherapy was conducted using the Chinese version of the Memorial Symptom Assessment Scale 10-18. Symptom clusters and sentinel symptoms were identified using exploratory factor analysis and Apriori algorithm. Core and bridge symptoms were identified using network analysis.

Five symptom clusters and sentinel symptoms were identified: gastrointestinal (constipation as the sentinel symptom), emotional (feeling sad as the sentinel symptom), and somatic (cough as the sentinel symptom); however, the sentinel symptoms of neurological and self-image impairment symptom clusters were not specified. In symptom network, feeling sad and nausea were core symptoms, whereas dizziness and lack of energy were the bridge symptoms.

The somatic symptom cluster should be prioritized for intervention during remission induction. Network analysis and sentinel symptom analysis must be extended to the symptom research in pediatric cancer to provide a scientific basis for symptom management.

Nurses should aim to identify and intervene with sentinel and networked symptoms to ensure that children are effectively supported during the remission induction, reducing symptom burden and improving quality of life.

Patients with leukemia often suffer from the combined effects of cancer-related fatigue (CRF) and subthreshold depression, which mutually exacerbate each other in a vicious cycle. In this editorial, we comment on the article by Liu et al, published in the World Journal of Psychiatry. We further elucidate the profound impact of subthreshold depressive symptoms on the experience of CRF and complications in patients with leukemia. This editorial highlights the importance of early identification and treatment of subclinical depression, and advocates for a multidisciplinary and integrated treatment approach that includes social support, psychological interventions, and individualized treatment plans. Future research needs to explore the biological mechanisms underlying the interaction between the two to develop more effective prevention and treatment strategies.

This study aimed to examine (a) whether psychological stress is associated with experiencing multiple psycho-neurological symptoms (depression, cognitive impairment, fatigue, sleep disturbance, and pain) as a cluster and (b) whether stress hormones (adrenocorticotropic hormone [ACTH] and cortisol) are associated with psychological stress and symptom cluster experience.

This cross-sectional study analyzed data from 133 patients with hematologic cancer awaiting chemotherapy. Enzyme-linked immunosorbent assays analyzed the morning stress hormone levels (ACTH and cortisol). Latent profile analyses identified the group experiencing a psycho-neurological symptom cluster. Factors influencing the experience of the psycho-neurological symptom cluster were included as covariates and analyzed using multinomial logistic regression.

Thirty-three percent (n = 44) experienced all five psycho-neurological symptoms as a cluster and experienced each symptom in a higher severity than those who did not experience the symptom cluster (ps < 0.05). Thereby, this group legitimately experienced the psycho-neurological symptom cluster. The major determinant of this group was the perceived psychological stress (OR = 8.05, 95% CI = 3.08; 20.99). Further, each symptom demonstrated a positive association with stress levels (correlation r ranged from 0.22 to 0. 56, all ps < 0.05). Participants with higher stress were more likely to experience the symptom cluster. Stress hormones levels (ACTH and cortisol) were neither associated with the symptom cluster experience nor with psychological stress levels.

Psychological stress, rather than biological stress response, is involved in experiencing the psycho-neurological symptom cluster. Managing stress levels would help alleviate this symptom cluster.

Part I reviews persistent challenges obstructing progress in understanding complex fatigue's biology. Difficulties quantifying subjective symptoms, mapping multi-factorial mechanisms, accounting for individual variation, enabling invasive sensing, overcoming research/funding insularity, and more are discussed. Part II explores how emerging artificial intelligence and machine and deep learning techniques can help address limitations through pattern recognition of complex physiological signatures as more objective biomarkers, predictive modeling to capture individual differences, consolidation of disjointed findings via data mining, and simulation to explore interventions. Conversational agents like Claude and ChatGPT also have potential to accelerate human fatigue research, but they currently lack capacities for robust autonomous contributions. Envisioned is an innovation timeline where synergistic application of enhanced neuroimaging, biosensors, closed-loop systems, and other advances combined with AI analytics could catalyze transformative progress in elucidating fatigue neural circuitry and treating associated conditions over the coming decades.