Aim: Brain metastases (BM) in patients with lung cancer (LC) are linked to unfavorable outcomes. The eukaryotic translation elongation factor 1 alpha 2 (EEF1A2) is notably overexpressed across various cancer types and plays a role in promoting tumor initiation and progression. This research aimed to clarify the function of EEF1A2 in the context of lung cancer brain metastasis (LCBM) and to explore the mechanisms underlying its effects.
Methods: To identify genes with differential expression between LC and LCBM samples, transcriptomic microarray analyses were conducted, confirming that EEF1A2 expression is elevated in LCBM. EEF1A2 expression levels were validated in multiple LC cell lines. PC9 and SPCA1 cells were transfected with lentiviral vectors carrying siRNAs targeting EEF1A2 to assess its role both in vitro and in vivo. Tandem mass tag proteomics was employed to identify proteins regulated by EEF1A2. The expression of EEF1A2, BCL10, and phosphorylated NF-κB in tumor tissues from LC and LCBM patients was analyzed.
Results: Compared to the LC samples, the LCBM samples exhibited significantly higher levels of EEF1A2 expression. EEF1A2 knockdown in PC9 and SPCA1 cells resulted in substantial reductions in cell proliferation, migration, and invasion. Proteomic profiling revealed that BCL10 protein levels were markedly reduced in EEF1A2-knockdown cells. Additionally, there was a decrease in phosphorylated NF-κB, EGFR, and mesenchymal markers (N-cadherin, Twist, Snail, Slug, and Cdc42), along with an increase in E-cadherin expression. In a mouse model, EEF1A2 knockdown in PC9 cells significantly inhibited brain metastasis. Furthermore, patient samples presented elevated levels of EEF1A2, BCL10, and phosphorylated NF-κB in LCBM tissues than in LC tissues.
Conclusion: Our research revealed that EEF1A2 is upregulated in LCBM, and that its knockdown suppresses brain metastasis by decreasing BCL10 expression, inhibiting NF-κB signaling, and reducing epithelial-mesenchymal transition markers. These results suggest that targeting EEF1A2 may be a promising therapeutic approach for preventing and treating brain metastasis in lung cancer patients.
Aim: In cancer tissues, glycolysis metabolism is often heightened, making 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) useful for assessing glucose metabolism. However, the kidneys' high glucose metabolism makes it difficult to distinguish between normal renal tissue and renal cancer. This study aims to evaluate the maximum standardized uptake value (SUVmax) in renal cancer using PET/CT and determine its relationship to prognosis.
Methods: We also aim to examine the correlation between SUVmax and clinical parameters and its potential link to prognosis. We enrolled 105 patients who underwent FDG-PET/CT between March 2012 and October 2017. These patients, diagnosed with localized renal cell carcinoma (RCC), underwent surgery and were pathologically confirmed to have clear cell RCC (ccRCC). We investigated the impact of SUVmax and other parameters on recurrence.
Results: SUVmax and C-reactive protein (CRP) were associated with tumor progression, alongside stage, nuclear grade, microvascular invasion [v(+)], and tumor-infiltrating lymphocytes (TILs). Multivariate analysis with recurrence-free survival (RFS) as the endpoint indicated significant results (SUVmax ≥ 3.7: relative risk 10.21, P < 0.01; CRP ≥ 0.11 mg/dL: relative risk 7.73, P < 0.01, n = 89). In survival curve analysis with RFS, high SUVmax or elevated CRP predicted poor prognosis, with further worsening when v(+) was added.
Conclusion: SUVmax is a strong prognostic factor for poor outcomes. CRP is also a prognostic factor, though it should be interpreted cautiously, as CRP reflects overall systemic conditions and may not exclusively represent renal cancer activity. Further research into pre-treatment RCC prognosis prediction is anticipated.
Breast cancer is one of the most common female malignant tumors, which seriously endangers human health. Glucose metabolic reprogramming in rapidly proliferating cancers drives increased glycolysis to meet energy needs, promoting tumor growth, acidifying the tumor microenvironment, and impairing immune function, which diminishes therapeutic efficacy. Circular RNAs (circRNAs), as key regulators of cellular processes, are increasingly recognized for their involvement in the metabolic reprogramming of cancer. Concurrently, specific circRNAs could be released by tumor cells via exosomes to facilitate intercellular communication, significantly impacting glucose metabolism, cancer progression, and therapy resistance. However, the role of circRNAs in breast cancer and their mechanisms in regulating glucose metabolism remain unclear. Therefore, elucidating these metabolic regulatory pathways could provide valuable insights for developing targeted strategies to exploit metabolic vulnerabilities and improve the prognosis of breast cancer.
Aim: The overexpression of Kinesin superfamily proteins (KIFs) has been increasingly recognized as a critical factor associated with unfavorable prognostic outcomes across a spectrum of cancers. This study aims to elucidate the multifaceted role of KIFs within the tumor immune microenvironment and explore their potential as targets for precision cancer therapy.
Methods: Utilizing comprehensive genomic datasets from the Cancer Genome Atlas and Genotype-Tissue Expression databases, we systematically analyzed KIF expression patterns and their potential oncogenic functions. To investigate the functional impact of KIF3A in hepatocellular carcinoma (HCC), we synthesized siKIF3A and transfected it into HepG2 cells, followed by a series of functional assays. Cell proliferation was meticulously evaluated using EdU incorporation, CCK8, and colony formation assays, while cell migration was assessed through scratch wound healing and Transwell migration assays. Quantitative analysis of gene and protein expression levels was performed using RT-PCR and Western blot techniques, respectively.
Results: Our findings reveal that KIFs exhibit remarkably high mutation frequencies across multiple cancer types. Furthermore, we identified significant genomic and epigenetic modifications of KIFs in various tumors, with specific oncogenic mutations in certain cancers potentially serving as regulatory mechanisms for KIFs expression. Notably, tumor-derived KIFs demonstrate a strong association with immune cell infiltration patterns, suggesting their potential as novel therapeutic targets in cancer immunotherapy. Importantly, the majority of KIF family genes show significant correlations with patient prognosis, underscoring their clinical relevance. Specifically, KIF3A emerges as a promising prognostic marker for HCC, demonstrating significantly higher expression levels in HCC tissues compared to adjacent non-cancerous tissues (P < 0.05). This overexpression strongly correlates with poor survival outcomes and established risk factors. Functional studies reveal that knockdown of KIF3A significantly inhibits the proliferation and migration capabilities of HCC cells (P < 0.05), highlighting its critical role in tumor progression. Our findings suggest that KIF3A not only serves as a valuable prognostic biomarker but also represents a potential therapeutic target for HCC patients, particularly through its involvement in tumor immune regulation mechanisms.
Conclusion: This comprehensive study provides novel insights into the role of KIFs, particularly KIF3A, in cancer biology and offers promising avenues for the development of targeted therapies in hepatocellular carcinoma. The integration of genomic analysis with functional validation underscores the potential of KIFs as both diagnostic markers and therapeutic targets in cancer management.
Aim: Bone-metastatic prostate cancer (PCa) is a debilitating disease with few therapeutic options once androgen independence and chemotherapeutic resistance develop. Advanced PCa has metabolic vulnerabilities involving glycolysis, which is mediated by class I glucose transporters (GLUTs1-4). We previously patented DRB18, a small molecule pan-class I GLUT inhibitor that successfully inhibited the growth of a human lung cancer xenograft in mice. The purpose of this study was to determine the sensitivity of advanced PCa to GLUT antagonism using DRB18.
Methods: Bioinformatics was performed on human and canine PCa datasets to determine the clinical expression of class I GLUTs. Glucose uptake and cell viability in response to DRB18 were measured in vitro. Tibias of athymic mice were inoculated with Ace-1 canine PCa cells and treated with DRB18. The combination of DRB18 with cytotoxic docetaxel was assessed in vitro.
Results: Expression of important class I GLUTs and glycolysis genes increased during PCa progression in men and dogs. DRB18 reduced cancer cell glucose uptake and cell viability in a dose-dependent manner. Half-maximal inhibitory concentrations (IC50) ranged from 20-30 µM. DRB18 did not prevent intratibial PCa growth in vivo and had toxic effects at higher concentrations. DRB18 and docetaxel combination therapy and gene expression data from publicly available human PCa samples indicated docetaxel treatment does not stimulate glucose-related metabolic pathways.
Conclusion: GLUT1 inhibition alone or with combination therapy may not be appropriate for bone-metastasis inhibition. The results contribute to evidence that suggests bone metastatic PCa is not glucose dependent.
Electronic Nose (ENose) technology has emerged as a transformative tool in medical diagnostics, leveraging sensor arrays that mimic the human olfactory system to detect odors and volatile organic compounds (VOCs) in various biological samples. ENose systems utilize a range of sensor types, such as metal oxide semiconductors and conducting polymers, to generate unique “smell fingerprints” through pattern recognition algorithms. These systems have shown promise in diagnosing various medical conditions, including respiratory diseases, infectious diseases, metabolic disorders, and neurological conditions. Notably, ENose technology holds significant promise in cancer diagnostics, offering a non-invasive, cost-effective, and rapid approach to early detection and monitoring. It has demonstrated impressive accuracy (85%-95%) in detecting cancers and monitoring complications. However, challenges remain, including issues with standardization, sensor sensitivity, and data interpretation. Despite these hurdles, ENose technology’s market growth is fueled by the increasing prevalence of chronic diseases. Recent developments in Artificial Intelligence (AI), particularly machine learning techniques like deep learning, have enhanced the diagnostic accuracy and robustness of ENose devices. This paper explores the evolution, core principles, applications, challenges, and future potential of ENose technology, with particular emphasis on integrating recent advancements in AI for enhanced detection and interpretation. Future research and collaboration across sectors are essential to overcome existing challenges and integrate ENose into mainstream healthcare.
Aim: To investigate patients with soft tissue sarcoma (STS) and evaluate (1) factors associated with lung metastases at the time of diagnosis; (2) the impact these factors had on overall survival; and (3) the impact of the metastatic site on survival.
Methods: We utilized the SEER database to analyze data from 14,520 patients diagnosed with STS between 2010 and 2018. Inclusion criteria included histologically confirmed STS of the lower limb, upper limb, or pelvis. Demographic, oncologic, and survival data were extracted, including tumor size, histopathology, AJCC staging, and metastatic status. Univariable and multivariable analyses identified risk factors for lung metastasis and Kaplan-Meier survival analysis was used to assess disease-specific survival (DSS).
Results: Of the 13,372 patients with STS, 7.9% had lung metastases at diagnosis. Undifferentiated pleomorphic sarcoma, leiomyosarcoma, and rhabdomyosarcoma were the most common STS presenting with lung metastasis. Patients who present with STS of the lower extremity, higher grade, and either leiomyosarcoma, spindle cell, or synovial cell sarcoma were more likely to present with lung metastases. Patients with lung metastases had worse survival than patients without metastases. However, survival was better in patients with isolated lung metastases than in those with any combination of lung, bone, brain, and/or liver metastases.
Conclusion: Tumor location, grade, T score, and lymph node involvement were significant risk factors for lung metastasis. A deeper understanding of risk factors for lung metastases presents a new opportunity for investigation in management. As expected, patients who present with lung metastases have worse overall survival.
Gastric cancer remains a significant global health burden, and while immunotherapy offers promising therapeutic avenues, its efficacy varies greatly among patients. The key challenge is accurately identifying treatment responders, while alternative strategies are necessary for non-responders. Biomarkers such as PD-L1 expression, tumor mutational burden, mismatch repair status, and Epstein-Barr virus infection have shown predictive potential, yet the quest for more reliable markers continues to be challenging. Emerging technologies, including liquid biopsy, single-cell sequencing, and artificial intelligence, present novel approaches to enhancing individualized research and improving predictive capabilities. This review provides a comprehensive analysis of current biomarkers and introduces emerging candidates from recent studies, thereby contributing to the ongoing efforts to refine patient stratification and treatment strategies.
Aim: To identify serum biomarkers for non-small cell lung cancer (NSCLC) and assess their potential for early diagnosis.
Experimental: MB-WCX coupled with MALDI-TOF MS was utilized to profile serum samples from 64 NSCLC patients and 64 healthy subjects, followed by ClinProTools software and Liquid chromatography-electrospray ionization-tandem mass spectrometry(LC-ESI-MS/MS) for recognition and characterization of differentially expressed peaks. Enzyme-linked immunosorbent assay (ELISA) confirmed protein concentrations, while The Cancer Genome Atlas (TCGA) database was leveraged for validation of candidate biomarkers in a larger cohort.
Results: 39 distinct proteomic m/z peaks were identified for NSCLC subjects, with five of these peaks significantly distinguishing NSCLC from healthy controls (HC). A model developed using the GA (Genetic Algorithm) with ClinProt data demonstrated a sensitivity of 84.72% and a specificity of 88.68% in identifying NSCLC patients. Peaks 2 through 5 were observed to be downregulated in the NSCLC group. Notably, a peptide peak, Peak 1, with an m/z value of 1,866, identified as a fragment of TUBB, was upregulated in NSCLC. ELISA validated the increased serum TUBB levels in NSCLC patients (P < 0.001). Furthermore, analysis of TUBB expression in lung cancer tissues through TCGA data revealed elevated TUBB expression in lung cancer tissues.
Conclusions: Serum TUBB is identified as a potential diagnostic biomarker for NSCLC, which may benefit early diagnosis and enhance the survival rate of NSCLC patients.
Aim: Renal cell carcinoma (RCC) screening is helpful to improve the prognosis of patients. However, the existing RCC detection methods are not suitable for large-scale screening. Serum microRNAs (miRNAs) is expected to be a convenient, economical, and non-invasive screening tool for RCC. This study aimed to identify relevant serum miRNAs as diagnostic markers for RCC.
Methods: This research included 112 patients with RCC and 112 healthy control individuals, carried out in three distinct phases. The objective was to identify serum miRNAs suitable for RCC diagnosis using quantitative reverse transcription polymerase chain reaction (RT-qPCR). Additionally, bioinformatics analyses were performed to predict target genes and provide functional annotations.
Results: Compared with healthy controls, patients with RCC highly expressed miR-221-3p and lowly expressed miR-124-3p, let-7b-5p, miR-30a-5p, and miR-302d-3p. After multiple rounds of combination screening, the combination of miR-124-3p, miR-221-3p, and let-7b-5p showed good diagnostic predictability. The diagnostic panel exhibited a 0.838 area under curve (AUC), achieving 75.00% sensitivity and 77.68% specificity.
Conclusion: Our analysis demonstrates that combining miR-124-3p, let-7b-5p, and miR-221-3p forms a non-invasive, economical, and remarkably effective diagnostic indicator for patients with renal cell carcinoma.
Aim: Hepatocellular carcinoma (HCC) is a prevalent malignancy globally, and pyroptosis, an inflammatory form of programmed cell death, is closely associated with tumor progression. The aim of this study is to explore the functional role of pyroptosis in HCC progression.
Methods: RNA sequencing and clinical data of HCC patients from TCGA and GEO databases were analyzed. We examined the expression patterns of 49 pyroptosis-related genes (PRGs) in HCC. Univariate Cox regression and consensus clustering divided TCGA-HCC patients into two subtypes, C1 and C2. A prognostic model was developed using LASSO Cox regression based on significant PRGs, and predictive value was assessed through nomogram and decision curve analysis (DCA). GSEA and immune infiltration analysis were conducted to evaluate immune status. Additionally, Networkanalyst tools were used to predict regulating networks of PRGs, and gene expression was validated using qRT-PCR.
Results: Subtype C2 showed higher grades (III-IV), immune scores, genetic mutations, and increased immune factor expression. A prognostic model based on four prognostically relevant PRGs stratified HCC patients into high- and low-risk groups. The low-risk group had better survival. The risk score was an independent prognostic factor with strong predictive ability. Immune status differed between the two risk groups. Regulatory networks between PRGs, transcription factors (TFs), miRNAs, and chemicals were identified. qRT-PCR confirmed higher expression of PRGs in paracancerous tissues compared to carcinoma.
Conclusion: A prognostic model based on four PRGs offers significant implications for prognosis assessment and could inform HCC treatment strategies.
Approximately one-third of patients diagnosed with a neuroendocrine tumor (NET) develop distant metastases, with the liver being the most common site. Therefore, the management of patients with neuroendocrine liver metastasis (NELM) is particularly important, as metastatic disease is often one of the main factors influencing patient prognosis. When patients are amenable to surgery, liver resection is associated with improved long-term outcomes and relief from potential tumor-related symptoms. NELM resection should be considered even when a radical resection is not achievable. Moreover, a tumor burden threshold of 70% for hepatic cytoreductive surgery can be safely adopted with favorable long-term outcomes. For patients with NELM who are not candidates for surgical resection, liver-directed therapies provide a valuable treatment strategy, enabling optimal disease control while preserving liver parenchyma. Furthermore, liver transplantation has emerged as a potential therapy for patients with NELM. Although significant progress has been made in managing NELM, the heterogeneity of NETs poses substantial challenges to research due to the variability in tumor characteristics. Therefore, devising an optimal therapeutic strategy requires a multidisciplinary approach to develop individualized treatment plans and optimize patient outcomes.
Aim: Antitumor activity of TNF-related apoptosis-inducing ligand (TRAIL) can be inhibited by anti-apoptotic Bcl-2 family members. Here, we examined the potential of the BH-3 (bcl-2 homolog domain 3) mimetic obatoclax to sensitize non-small cell lung cancer (NSCLC) cells for TRAIL-induced apoptosis.
Methods: The sensitivity to obatoclax treatment in combination with TRAIL was measured with the tetrazolium-based 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) assay. TRAIL-induced apoptosis was evaluated with flow cytometric analysis. Mitochondrial and cytosolic components were fractionated and western blotting was performed to determine the level of Cytochrome c, GAPDH, and Cox IV. Knockdowns were performed with small interfering RNA (siRNA). TRAIL receptor protein levels were assessed with fluorescence intensity index.
Results: Synergistic interactions were observed both in TRAIL-resistant (SW1573, A549) and TRAIL-sensitive (H460) cells, characterized by enhanced caspase-dependent apoptosis. Although concurrent treatment with obatoclax and TRAIL was sufficient for apoptosis activation in SW1573 cells, A549 cells required 48-h pre-incubation with obatoclax, during which TRAIL-R2 (TRAIL receptor 2) cell surface expression increased and X-linked inhibitor of apoptosis protein (XIAP) levels decreased. Interestingly, the knockdown of XIAP was sufficient to sensitize A549 cells for concurrent treatment.
Conclusion: Obatoclax is an effective sensitizer for TRAIL-induced apoptosis in NSCLC, although displaying cell type-specific effects.
Aim: This study aimed to compare the prognostic outcomes of cytokeratin (CK) 19-positive and CK19-negative hepatocellular carcinoma (HCC), focusing on clinicopathological features and the impact of targeted therapy in CK19-positive patients.
Methods: A retrospective analysis was performed on 310 HCC patients who underwent curative resection between 2010 and 2020 at the First Affiliated Hospital of Nanjing Medical University. Among them, 102 were CK19-positive and 168 were CK19-negative. Multivariate Cox regression was used to identify independent predictors of overall survival (OS) and recurrence-free survival (RFS). Kaplan-Meier survival curves were generated from the Cox model.
Results: CK19-positive patients exhibited significantly poorer tumor differentiation (P < 0.001), increased microvascular invasion (P = 0.010), elevated α-fetoprotein (AFP) (P = 0.0001) and were more often asymptomatic at diagnosis (P = 0.014). The median survival time (MST) was 22.1 months in CK19-positive versus 60.3 months in CK19-negative patients. Among those with recurrence, MST was 56.4 months for CK19-positive and 101.6 months for CK19-negative patients. CK19 status significantly impacted OS (P < 0.001) and RFS (P = 0.006) in advanced-stage cases. Independent prognostic factors for RFS included cirrhosis, tumor size, number of tumors, macrovascular invasion, poor differentiation, and CK19 expression. Microvascular invasion and Child-Pugh classification were additional predictors of OS. Targeted therapies did not significantly improve outcomes in CK19-positive patients.
Conclusions: CK19-positive HCC is associated with more aggressive tumor behavior, higher recurrence, and poorer survival. Targeted therapy provided no significant survival benefit in this study.
Background: This study aimed to investigate the expression of lnc-MAPKAPK5-AS1 in colon cancer and its clinical implications for prognosis, liver metastasis, and tumor microenvironment (TME) regulation.
Methods: Through integrative analysis of public databases (TCGA, GEO, cBioPortal, David, BioGRID, etc.), we systematically evaluated the roles of lnc-MAPKAPK5-AS1 and MAPKAPK5 in colon cancer development, liver metastasis, immune microenvironment modulation, and associated biological pathways. Immune infiltration patterns were assessed using ESTIMATE, CIBERSORT, and xCell algorithms. MAPKAPK5 protein expression was further validated by immunohistochemistry.
Results:Lnc-MAPKAPK5-AS1 was upregulated in colon cancer tissues and correlated with poor clinical outcomes. Immunohistochemistry confirmed that MAPKAPK5 protein was strongly or moderately expressed in colon cancer tissues. Furthermore, compared with the colon cancer tissues in situ, patients with liver metastasis also showed elevated expression of lnc-MAPKAPK5-AS1. Functional enrichment analysis revealed that differentially expressed genes (DEGs) were primarily involved in amino acid and lipid metabolism pathways. In addition, immune infiltration analysis indicated that lnc-MAPKAPK5-AS1 expression was associated with multiple immune checkpoint inhibitors. The group with high gene expression showed increased infiltration abundance of regulatory T cells, gamma-delta T cells, and CD8+ naive T cells.
Conclusions:Lnc-MAPKAPK5-AS1 may serve as a potential oncogenic biomarker in colon cancer. Our findings indicate that its upregulation could contribute to tumor progression and liver metastasis, possibly by remodeling the TME via immune cell modulation, including potential alterations in the proliferation, differentiation, and functional states of key lymphocyte subsets.
Autophagy, a cellular recycling process, plays a key role in maintaining genomic stability and regulating DNA damage repair. However, recent studies have challenged this consensus, suggesting that upregulation of autophagy may induce DNA damage and contribute to genomic instability. Notably, several investigations have demonstrated that autophagy-mediated DNA damage can occur through mechanisms involving the production of reactive oxygen species (ROS). Despite these findings, many questions remain unresolved regarding the controversial
Aim: To characterize the gastric cancer immune landscape and evaluate its correlation with prognosis and immunotherapy efficacy.
Methods: Gene expression data from eight GEO datasets were preprocessed. The datasets were partitioned into training and validation subsets. Immune- and prognostic-related genes were identified from the training set to construct an Immune-Related Gene Set Score (IRGS) prediction model. The model underwent external validation in two independent cohorts, with further optimization incorporating clinical factors. Differences in immune biomarkers between IRGS groups were analyzed and their correlation with therapeutic response was assessed in an immune cohort.
Results: Elevated IRGS significantly correlated with prolonged overall survival (OS) in both training (HR 0.46,
Conclusions: The IRGS model is a robust predictor of prognosis and immunotherapy response in GC, underscoring its potential clinical utility.
Non-small cell lung cancer (NSCLC) remains one of the most aggressive human malignancies worldwide. While tissue biopsy has long been considered the gold standard for diagnosing NSCLC, the past two decades have seen the emergence of various circulating biomarkers as key components of plasma-based liquid biopsy in NSCLC. These include circulating tumor cells (CTCs), circulating cell-free nucleic acids such as circulating tumor DNA and microRNAs, extracellular vesicles (exosomes), tumor-educated platelets, circulating proteins, and immune cells or immune system components. Despite their promise, CTCs are not yet routinely used in clinical practice for early-stage NSCLC. This commentary highlights the current understanding and detection of CTCs in early-stage NSCLC patients. To date, identifying reliable blood-based biomarkers - whether associated with CTCs or not - remains a major hurdle to diagnosing NSCLC across both early and advanced stages. Monitoring CTC levels could provide important clues on tumor heterogeneity and complexity, including pathologic staging, primary tumor characteristics, and treatment response, particularly in advanced disease. Currently, multiple techniques exist for isolating, characterizing, and enumerating CTCs. Among them, the CellSearch System is the most widely used and remains the only US FDA-approved method, despite certain limitations. In addition, this commentary explores the potential of combining other diagnostic modalities, such as 18-FDG PET/CT, with emerging nanotechnologies to monitor lung nodules - even at early stages - offering deeper insights into disease onset, progression, and therapeutic response.
Systemic therapy for metastatic renal cell carcinoma (mRCC) has advanced considerably over the past decade. Studies of systemic therapies in the adjuvant, neoadjuvant, and now perioperative settings as solitary, dual, and triplet combinations are underway. The timing of systemic therapy relative to extirpative surgery has yielded varying results. With the rise of novel therapeutics, new methods for risk stratification and patient selection, including initial biomarker evaluation, have begun. This perspective aims to summarize investigations into perioperative mRCC therapies and discuss the foundations of these concepts based on recent adjuvant and neoadjuvant trials, and to discuss future directions in this space.
Prostate cancer (PCa) remains a global health challenge, with emerging evidence implicating environmental electromagnetic fields (EMFs) as dual regulators of tumor progression and therapeutic innovation. This perspective synthesizes mechanistic insights into EMFs bioeffects through three interconnected axes: (1) Calcium signaling as a central biosensor, translating EMFs stimuli into oncogenic processes via piezoelectric and ionic mechanisms; (2) Extracellular vesicles (EVs)-mediated metastatic reprogramming through cargo remodeling (e.g., pro-inflammatory proteins) and dysregulation of oncogenic miRNAs; and (3) Ferroptosis, a frequency-modulated cell death pathway driven by calcium-iron-reactive oxygen species (ROS) crosstalk. Paradoxically, EMFs exhibit context-dependent duality: low-intensity fields exacerbate malignancy by rewiring calcium/EVs-driven tumor-microenvironment interactions, while high-intensity fields achieve tumor ablation via calcium overload, ferroptosis, hyperthermia, or electroporation. Novel strategies, such as PEGylated nanocrystals combined with intratumoral micromagnets, synergize ferroptosis with immunogenic cell death, demonstrating therapeutic potential with minimal toxicity. Our analysis underscores the critical need for parameter optimization (frequency, intensity, duration) to balance oncogenic risks against therapeutic precision. We propose that integrating biophysical targeting with electromagnetic engineering holds promise to redefine PCa management through mechanism-driven precision. This perspective aims to frame these insights and highlight their implications for future research and therapeutic development.
Aim: This study focused on developing a prognostic index model associated with ferroptosis for predicting prostate cancer (PCa) relapse and progression. The aim was to enhance clinical decision making and improve immunotherapy strategies for PCa patients, ultimately leading to better patient outcomes.
Methods: The study employed the least absolute shrinkage and selection operator to develop the Ferroptosis-related gene (FRG) prognostic index model. This model's predictive power was validated across multiple PCa datasets, and its correlation with clinicopathological factors was investigated. Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analyses were conducted to identify associated signaling pathways. Furthermore, the CIBERSORT algorithm was used to assess PCa patient outcomes based on the combination of the FRGs risk index and immune cell infiltration patterns.
Results: The FRG index model emerged as an independent predictor of PCa recurrence. It correlated with advanced pathological stages, higher prostate-specific antigen levels, and higher tumor grades. Notably, the FRG index was significantly associated with immune cell infiltration, particularly activated mast cells, which are crucial in PCa recurrence and progression. Furthermore, the response of the FRG index in PCa cell lines implies that doxorubicin may hold clinical efficacy for recurrent PCa.
Conclusion: The FRG index established here could serve as a valuable prognostic tool and clinical decision-making aid in PCa. It offers insights into the molecular mechanisms underlying PCa progression and suggests new avenues for immunotherapeutic strategies, potentially leading to improved patient outcomes and a better understanding of PCa biology.
Osteosarcoma (OS) is a malignant bone tumor characterized by rapid progression and a high propensity to metastasis. Elucidating the mechanisms underlying cell proliferation and metastasis is crucial to improving prognosis. Recent advances in OS research span multiple dimensions, such as genetic mutations, epigenetic alterations, and aberrant signaling pathways. Additionally, the roles of the tumor microenvironment and cancer stem cells are increasingly recognized. Furthermore, traditional Chinese medicine (TCM) has gained significant attention due to its ability to regulate OS through multiple targets and pathways. Specifically, TCM formulations combat tumor progression via holistic mechanisms. These include reinforcing healthy Qi, eliminating pathogenic factors, promoting blood circulation, resolving stasis, and clearing heat toxicity. The monomeric components of TCM exert antitumor effects by suppressing tumor growth, inducing apoptosis, modulating the immune microenvironment, and reversing drug resistance. Acupuncture has shown efficacy in alleviating
Background: Gastric cancer’s heterogeneous nature and subtle symptoms necessitate the identification of reliable diagnostic and prognostic biomarkers.
Methods: CHST14 expression in gastric cancer was analyzed using TCGA and GTEx data. Differentially expressed genes were identified and visualized through ROC, survival, volcano plots, and nomograms. Functional analyses included GO and KEGG enrichment, drug sensitivity predictions from the GDSC database, and immune cell infiltration estimation using TIMER algorithms.
Results: CHST14 was overexpressed in gastric cancer tissues and correlated with poor overall survival and disease-specific survival, but showed no correlation with progression-free survival. Drug sensitivity analysis revealed CHST14’s positive association with chemotherapeutic agents such as SN-38, paclitaxel, and 5-fluorouracil, and negative correlation with others including BEZ235 and doxorubicin. Immune analysis showed CHST14 expression positively associated with infiltration of B cells, CD4+ T cells and CD8+ T cells, macrophages, neutrophils, and dendritic cells. Single-cell RNA sequencing data highlighted CHST14's role in cell-cell interactions, particularly between malignant cells and fibroblasts, and its involvement in tumor-stroma crosstalk. Enrichment analyses linked CHST14 to oncogenic pathways such as epithelial-mesenchymal transition, TNF-α signaling, and MAPK regulation.
Conclusion: CHST14 is a potential diagnostic and prognostic biomarker for gastric cancer, influencing drug metabolism and immune microenvironment interactions. These findings provide insights into CHST14’s mechanistic role in tumor progression and its potential as a therapeutic target. Further studies are required for clinical validation and mechanistic exploration.
Aim: Glioma, the most common primary brain tumor, is known for its poor prognosis, limited treatment success, and high level of aggressiveness. Although cuproptosis-related genes have been linked to outcomes in other cancers, their role in glioma is still not well understood.
Methods: By leveraging the Cancer Genome Atlas (TCGA) and additional databases, we conducted Cox regression and Kaplan-Meier analysis to determine the predictive importance of cuproptosis-related genes in individuals with glioma. By utilizing data from Gene Expression Omnibus (GEO) and other relevant databases, we examined how the expression of cuproptosis-associated genes correlates with immune cell infiltration, immunological checkpoint status, pathological stage, and histological grade. Additionally, we analyzed the connection between gene expression linked to cuproptosis and the prognosis in glioma patients.
Results: Our newly developed cuproptosis-based glioma predictive model demonstrated promising prediction performance. Additionally, we identified glutaminase (GLS) in glioblastoma as a potential novel diagnostic indicator for glioma patients.
Conclusion: GLS holds the potential to provide novel perspectives on cancer management and serve as a valuable diagnostic predictor for glioma patients.
Aim: Hepatocellular carcinoma (HCC) remains a major challenge due to poor prognosis. This study investigates
Methods: We integrated data from 486 HCC patients [The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets] and 16 pairs of clinical tissue samples. The expression, mutation profiles, and prognostic significance of 6 m3C regulators were analyzed. Functional assays, including cell proliferation and migration, were performed, alongside immune infiltration analysis using single-sample gene set enrichment analysis (ssGSEA). Finally, an m3C scoring system was constructed to evaluate prognostic potential.
Results: Most m3C regulators (except METTL8) were upregulated in HCC tissues. Knockdown of METTL2, METTL6, ALKBH1, or ALKBH3, as well as overexpression of METTL8, inhibited HCC cell proliferation and migration. Two distinct m3C modification modes were identified, each associated with unique clinical features. The m3C score was positively correlated with longer overall survival in high-score patients and was associated with tumor mutation burden (TMB) and expression of PD-1 and CTLA4, suggesting its potential to predict immunotherapy response.
Conclusion: This study highlights the genetic variation and prognostic relevance of m3C methylation regulators in HCC and introduces a novel scoring system for prognosis prediction, providing a potential tool to guide HCC treatment strategies.
Aim: Lung cancer remains a major global health challenge, and this study presents a censor-aware semi-supervised learning framework (SSL) that integrates clinical and imaging data to improve prognostic modeling and address biases in handling censored data.
Methods: We analyzed clinical, positron emission tomography (PET), and computed tomography (CT) data from 199 lung cancer patients from public and local databases, focusing on overall survival time as the primary outcome. Handcrafted (HRF) and Deep Radiomics features were extracted after preprocessing using Visualized & Standardized Environment for Radiomics Analysis (ViSERA) software and were combined with clinical features. Features were reduced using Pearson’s correlation coefficient regression (RR) and the F-test for regression (FR), followed by supervised learning (SL) and SSL. In SSL, censored data were pseudo-labeled using the Weibull accelerated failure time (AFT) model to enrich the training data. Seven regressors and three hazard ratio survival analyses (HRSAs) were optimized using five-fold cross-validation, grid search, and holdout test bootstrapping.
Results: For PET-HRFs, the SSL approach reduced the mean absolute error by 14.81%, achieving 1.04 years with FR + AdaBoost Regression (ABR) compared to 1.20 years with SL. For clinical features, SSL with RR + ABR reached a mean absolute error of 1.04 years, outperforming SL (1.09 years) with a 4.9% improvement. In HRSA, CT_HRF combined with principal component analysis (PCA) + Component-Wise Gradient Boosting Survival Analysis yielded an external C-index of 0.65 ± 0.02, effectively distinguishing high- and low-risk groups.
Conclusions: The SSL strategy applied to HRFs from PET imaging significantly enhanced survival prediction compared to SL and uncovered complementary biological information that may remain hidden when only limited labeled data are used.