Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide, with limited effective targeted therapies. Metabolic reprogramming is a hallmark of cancer, and post-translational modifications (PTMs), such as phosphorylation, ubiquitination, and malonylation, play critical roles in regulating metabolic pathways. However, their contribution to metabolic reprogramming in CRC remains unclear.

Phosphorylation, ubiquitination, and malonylation were analyzed in paired CRC and adjacent normal tissues using high-resolution mass spectrometry. Differential PTM patterns were analyzed, followed by identification of key regulatory enzymes and modification sites. Functional enrichment, protein-protein interaction (PPI) networks, and multi-omics integration were used to explore PTMs' role in CRC metabolism.

We identified 59 differential phosphorylation sites, 263 ubiquitination sites, and 64 malonylation sites in CRC tissues compared with normal tissues, affecting key metabolic enzymes such as IDH1, LDHA, PDHA1, and GAPDH. Altered ubiquitination of IDH1 and LDHA may be associated with changes in protein stability and activity. Phosphorylation of PDHA1 correlated with its modified levels, potentially promoting glycolytic preference in CRC, while increased malonylation of GAPDH may influence its enzymatic activity and glycolytic flux. Protein interaction and pathway analyses further revealed a PTM-regulated metabolic network, suggesting a potential role of PTMs in CRC metabolic reprogramming.

This study suggests that PTMs may contribute to metabolic reprogramming in CRC by modulating key metabolic enzymes, including IDH1, LDHA, PDHA1, and GAPDH. These modifications may influence glycolysis and energy metabolism, highlighting PTM-regulated pathways as potential therapeutic targets. The integrated PTM atlas offers insights into the metabolic landscape of CRC.

Previous studies suggest that thyroid cancers in the isthmus are associated with more aggressive behavior and unfavorable patient outcomes compared to lobar tumors. This study aimed to characterize the molecular features of isthmus thyroid cancer.

Transcriptomic and proteomic data were retrieved from the Cancer Genome Atlas. We performed 1:2 propensity score matching based on age, sex, tumor subtype, size, extrathyroidal extension, lymph node metastasis, and stage, matching 15 papillary thyroid cancers with the BRAF V600E mutation located in the isthmus to 30 tumors in the unilateral lobar area.

Despite balanced oncogenic drivers, BRAF-mutant isthmus tumors exhibited significantly lower BRAF-RAS scores and higher ERK output scores. Additionally, these tumors had a slightly elevated DNA methylation-based stemness index. Principal component analysis revealed distinct gene expression patterns between the two groups. Differential expression and gene set enrichment analysis indicated enrichment in extracellular matrix remodeling, cell adhesion pathways, and TGF-β signaling. Notable upregulated genes included oligoadenylate synthases, TNR, HAS3, and LIF. Hierarchical proteomic clustering highlighted increased co-expression of several oncoproteins and DNA damage response markers in isthmus tumors.

BRAF-mutant papillary thyroid cancers in the isthmus display distinct molecular characteristics, including increased ERK signaling activity. These findings suggest that topographical location independently influences tumor biology and may account for the more aggressive clinical behavior of isthmus tumors at the molecular level.

Chemotherapy with gemcitabine and cisplatin remains the cornerstone of treatment for advanced urothelial carcinoma (UC), yet response rates vary significantly among patients. Predicting treatment response is crucial to avoid unnecessary toxicity and optimize therapeutic strategies. This study aims to develop a deep learning model leveraging RNA sequencing data to predict chemotherapy response in UC patients.

We developed a deep learning model using RNA sequencing gene expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus to predict chemotherapy (gemcitabine and cisplatin) response in UC patients. The model was externally validated using an independent cohort from the Pusan National University Yangsan Hospital. Model interpretation was performed through gene ontology and survival analyses using predictions from TCGA samples not included in the training set.

The deep learning model demonstrated excellent predictive performance, achieving 94.7% accuracy in the training dataset and 90.0% accuracy in external validation. Gene ontology analysis revealed four key functional clusters associated with chemotherapy response: DNA damage response, cell cycle regulation, kinesins/microtubule dynamics, and mitotic cytokinesis. Notably, the model showed significant prognostic value in early-stage, with predicted responders displaying markedly better survival outcomes (p=0.019).

Our transcriptome-based deep learning approach offers a promising computational strategy for predicting chemotherapy response in urothelial carcinoma. By integrating high-dimensional RNA-seq data and advanced machine learning techniques, we provide a potential decision-support tool for personalized treatment planning.

Hepatocellular carcinoma (HCC) exhibits substantial metabolic plasticity that supports tumor progression and therapeutic resistance. LGALS3BP has been primarily characterized as a secreted immunomodulatory protein; however, its cell-intrinsic role in regulating subcellular organization and metabolism in HCC remains poorly understood.

Transcriptomic analyses of public HCC cohorts were performed to assess metabolic programs associated with LGALS3BP expression. Proteomic profiling was conducted to define the LGALS3BP interactome. Mitochondrial function was evaluated by measuring adenosine triphosphate (ATP) levels, mitochondrial membrane potential (ΔΨm), and AMP-activated protein kinase (AMPK) activation. Lipid metabolic programs were assessed by gene expression analyses and lipid accumulation assays. Clinical relevance was independently validated in an institutional HCC cohort.

Low LGALS3BP expression was associated with activation of peroxisome proliferator-activated receptor alpha (PPARα)-driven peroxisomal and lipid catabolic gene programs. Proteomic analyses revealed that LGALS3BP associates with centrosomal γ-tubulin ring complex components and mitochondrial proteins, suggesting a role in centrosome-mitochondria subcellular organization. Functionally, LGALS3BP deficiency resulted in impaired mitochondrial energetic fitness, reduced ATP production, and activation of AMPK. This energetic stress was accompanied by induction of PPARα and compensatory lipid catabolic transcriptional programs. Consistent inverse associations between LGALS3BP expression and PPARα-peroxisome-related genes, including PPARA, ACOX1, EPHX2, and SCP2, were observed across public HCC datasets and were independently validated in an institutional HCC patient cohort.

LGALS3BP acts as a cell-intrinsic organizer that links centrosomal architecture to mitochondrial energetic homeostasis in HCC. Loss of this organizational axis induces mitochondrial energetic stress and promotes compensatory PPARα-peroxisome-mediated lipid catabolic reprogramming, highlighting a previously unrecognized connection between subcellular organization and metabolic plasticity in liver cancer.

Although aldehyde dehydrogenases 1 family member A1 (ALDH1A1) has been extensively studied in cancer, its role in hepatocellular carcinoma (HCC) remains poorly understood. This study was designed to characterize the expression pattern and functional roles of ALDH1A1 in HCC, and to further investigate its underlying molecular mechanisms using integrated proteomic analysis.

This study performed a pan-cancer analysis of ALDH1A1 expression, followed by validation of its expression levels in HCC tissue samples and cell lines. The expression of ALDH1A1 was manipulated using small interfering RNAs and lentiviral vector in HCC cell lines. And then the proliferation, migration, invasion, and tumorigenicity of HCC cells were observed in vitro and in vivo. Quantitative proteomic and phosphoproteomic profiling were conducted by liquid chromatography-tandem mass spectrometry, with comprehensive analyses and key findings validated by experiments.

ALDH1A1 was highly expressed in HCC tissues but exhibited notable expression heterogeneity among HCC cell lines. Functionally, ALDH1A1 promoted the proliferation, migration, and tumorigenicity of HCC cells, while suppressing apoptosis and modestly attenuates invasion. Quantitative proteomic and phosphoproteomic analyses revealed that ALDH1A1 profoundly reshaped both protein expression and phosphorylation landscapes in HCC. Furthermore, we validated that ALDH1A1 positively regulates the expression of DMPK, PCMTD2, VAMP4, ARHGAP19, NOL4L, and ST7, while suppressing SLC31A1, DSTYK, CYP4F12, GPNMB expression. Rescue experiments indicated that ALDH1A1 may promote HCC via activating Rho family small GTPase. ALDH1A1 also significantly upregulated mTOR phosphorylation.

ALDH1A1 acts as an oncogenic driver and regulatory hub in HCC.

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC) and remains associated with poor clinical outcomes due to pronounced molecular heterogeneity and limited prognostic biomarkers. Long non-coding RNAs (lncRNAs) have emerged as important regulators of cancer biology, yet their systematic association with disease progression and survival in LUAD remains incompletely defined. This study aimed to identify lncRNAs that robustly associate with LUAD progression and prognosis.

Pre-processed lncRNA expression data for 488 LUAD tumors and 58 normal lung tissues were obtained from The Cancer Genome Atlas (TCGA) via the TANRIC platform. Following expression filtering, stage-wise differential expression analysis was performed using Welch's t-test with false discovery rate correction. Kaplan-Meier survival analysis was used to evaluate prognostic relevance. Rank-based trend analyses using Spearman correlation were conducted to assess monotonic expression changes across tumor stage, lymph-node status, and tumor size.

We identified 68 lncRNAs consistently upregulated across all LUAD stages relative to normal lung tissue. Survival analysis revealed that higher expression of several lncRNAs was associated with poorer overall survival. Among these, FAM83A-AS1, CYTOR, and MIR4435-2HG emerged as prominent candidates, exhibiting consistent tumor-associated overexpression and adverse survival association. Importantly, these three lncRNAs also demonstrated significant monotonic trends across increasing lymph-node involvement and primary tumor size, indicating a close association with tumor burden and disease aggressiveness.

This integrative analysis identifies FAM83A-AS1, CYTOR, and MIR4435-2HG as robust poor-prognosis-associated lncRNAs in LUAD. Their coordinated behavior across expression, survival, nodal status, and tumor size highlights their potential utility as prognostic biomarkers and provides a framework for lncRNA-based risk stratification in lung adenocarcinoma.

Hepatocellular carcinoma, the third leading cause of cancer-related deaths globally, presents a critical public health burden in China due to its high incidence and mortality. While targeted therapies and immunotherapies have improved survival in advanced HCC, drug resistance remains a major therapeutic challenge. Recent studies suggest that gefitinib, an EGFR inhibitor, overcomes lenvatinib resistance, yet its mechanistic underpinnings are incompletely understood. To investigate gefitinib's metabolic effects in HCC, we conducted untargeted metabolomic profiling using two separate platforms: gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) with both hydrophilic interaction liquid chromatography (HILIC) and reversed-phase modes. Raw data were processed by Mass Hunter, normalized with internal standards, and analyzed via SIMCA for pattern recognition. Principal component analysis (PCA) of quality control samples and experimental groups (n = 6 each) confirmed system stability and clear inter-group separation. Orthogonal projections to latent structures discriminant analysis models were validated by 200 permutation tests. Analysis identified 42 metabolites with VIP > 1, of which 25 showed significant alterations (p < 0.05) post-gefitinib treatment. KEGG/RaMP-DB enrichment revealed perturbations in four key pathways: arginine-proline metabolism, nitrogen metabolism, branched-chain amino acid biosynthesis, and taurine metabolism. These results delineate gefitinib-induced metabolic reprogramming in HCC cells, providing a foundation for targeting metabolic vulnerabilities to overcome therapy resistance.

Cervical cancer remains a leading cause of cancer-related mortality among women globally, particularly in low- and middle-income countries. Cisplatin, a standard chemotherapeutic agent, is limited by severe toxicities and chemoresistance. This study aimed to assess the effects of cisplatin in combination with phytocannabinoids, Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) on cell proliferation, morphology, cell cycle progression, cell death, and DNA damage.

Synergistic interactions between THC, CBD, and cisplatin were assessed in HeLa, SiHa, and MCF-12A cells using the checkerboard assay and SRB assay. Cell morphology, cell cycle progression, apoptosis induction, autophagic activity, and DNA repair gene expression were evaluated using various techniques.

The THC-CBD-cisplatin combination exhibited the strongest apoptotic response in cancer cells (HeLa 53%, SiHa 58%), while minimally affecting MCF-12A cells (32%). Cannabinoid co-treatment amplified the antiproliferative and pro-apoptotic effects of cisplatin in HeLa and SiHa cells. The triple combination induced a G2/M arrest in HeLa cells and sub-G1 accumulation in SiHa cells. Autophagic activity, indicated by LC3B puncta formation, increased in HeLa and SiHa cells following THC and CBD exposure. DNA repair genes XRCC1 and RAD51 were downregulated by the cannabinoid-cisplatin combination.

These findings demonstrate that combining THC and CBD with cisplatin results in enhanced and mechanistically diverse anticancer effects, with a higher degree of selectivity for cervical cancer cells compared to non-cancerous MCF-12A cells by inducing apoptosis and autophagy while inhibiting DNA repair capacity. This study highlights the potential of cannabinoid-based combination therapies as a promising approach for cervical cancer treatment.

The evidence for effect of specialist palliative care (SPC) on acute healthcare utilization is limited and inconclusive. Furthermore, randomized controlled trials may not reflect real-world clinical settings.

Effect of SPC on subsequent emergency department (ED) visits and inpatient hospitalizations among patients with stage IV cancer using real-world data and a novel prior event rate ratio (PERR) approach with bias-correction.

Retrospective observational cohort study using the PERR method to compare ED visits and inpatient hospitalizations among patients with stage IV cancer who did versus did not receive SPC. We included patients diagnosed with stage IV cancer at National Cancer Centre Singapore between January 2019 and December 2022 and followed them up to July 2023. Each patient who received SPC was then matched with replacement to up to three patients who did not receive SPC during the study duration.

Among 7,144 patients with stage IV cancer, 1,499 (21.0%) received SPC by July 2023. After 1:3 matching with replacement, there were 5,948 patients in the matched cohort. Compared to those who never received SPC, those who received SPC had a PERR hazard ratio estimate of 0.644 (95% CI 0.524 to 0.792; P<0.001) for ED visits and 0.633 (95% CI 0.539 to 0.743; P<0.001) for hospitalizations. Although reduction was greater with earlier initiation of SPC (test of interaction, P<0.001), the reduction remained significant even if SPC was initiated about one year after stage IV cancer diagnosis.

SPC was associated with lower rates of ED visits and hospitalizations.

To evaluate the correlation between cytology and histopathology of splenic masses or nodular lesions in dogs.

Retrospective medical record review from January 2014 to July 2022 of dogs that had a splenic mass or nodular lesion and cytologic and histopathologic evaluation within 90 days of each other. Slide review was conducted by a single pathologist from each subspecialty and recorded as neoplastic, possibly neoplastic, or nonneoplastic.

33 dogs were included and had a median of 4 days (range, 0 to 90 days) between cytologic and histopathologic evaluation. Cytologic and histopathologic results agreed in 18 of 33 dogs (55%; 12 nonneoplastic and 6 neoplastic), although the type of neoplasm differed in one of these (sarcoma vs lymphoma). There was disagreement in 8 of 33 dogs (24%; 7 neoplastic on histopathology and nonneoplastic on cytology, 1 nonneoplastic on histopathology and neoplastic on cytology). When including a possibly neoplastic cytologic diagnosis as neoplastic, there was agreement in 22 of 33 dogs (67%) and disagreement in 11 of 33 dogs (33%). In dogs with a neoplastic or possibly neoplastic cytologic diagnosis, 10 of 14 (71%) were neoplastic on histopathology. In dogs with a nonneoplastic cytologic diagnosis, 12 of 19 (63%) were nonneoplastic on histopathology.

Cytologic diagnosis correlated with histopathologic diagnosis of splenic masses or nodular lesions in 55% to 67% of dogs. Cytology was accurate in 71% of neoplastic and 63% of nonneoplastic splenic masses or nodular lesions.

There is poor to fair agreement between cytology and histopathology in dogs with splenic masses or nodular lesions. A neoplastic cytologic diagnosis more commonly agrees with histopathology than a nonneoplastic diagnosis.