Verteporfin fluorescence in antineoplastic-treated pancreatic cancer cells found concentrated in mitochondria.

BACKGROUND: Traditional treatments for pancreatic cancer (PC) are inadequate. Photodynamic therapy (PDT) is non-invasive, and proven safe to kill cancer cells, including PC. However, the mitochondrial concentration of the photosensitizer, such as verteporfin, is key. AIM: To investigate the distribution of fluorescence of verteporfin in PC cells treated with antitumor drugs, post-PDT. METHODS: Workable survival rates of PC cells (AsPC-1, BxPC-3) were determined with chemotherapy [doxorubicin (DOX) and gemcitabine (GEM)] and non-chemotherapy [sirolimus (SRL) and cetuximab (CTX)] drugs in vitro, with or without verteporfin, as measured via MTT, flow cytometry, and laser confocal microscopy. Reduced cell proliferation was associated with GEM that was more enduring compared with DOX. Confocal laser microscopy allowed observation of GEM- and verteporfin-treated PC cells co-stained with 4',6-diamidino-2-phenylindole and MitoTracker Green to differentiate living and dead cells and subcellular localization of verteporfin, respectively. RESULTS: Cell survival significantly dropped upon exposure to either chemotherapy drug, but not to SRL or CTX. Both cell lines responded similarly to GEM. The intensity of fluorescence was associated with the concentration of verteporfin. Additional experiments using GEM showed that survival rates of the PC cells treated with 10 µmol/L verteporfin (but not less) were significantly lower relative to nil verteporfin. Living and dead stained cells treated with GEM were distinguishable. After GEM treatment, verteporfin was observed primarily in the mitochondria. CONCLUSION: Verteporfin was observed in living cells. In GEM -treated human PC cells, verteporfin was particularly prevalent in the mitochondria. This study supports further study of PDT for the treatment of PC after neoadjuvant chemotherapy.

Combination of DCE-MRI and NME-DWI via Deep Neural Network for Predicting Breast Cancer Molecular Subtypes.

BACKGROUND: To explore whether the combination of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and nonmono-exponential (NME) model-based diffusion-weighted imaging (DWI) via deep neural network (DNN) can improve the prediction of breast cancer molecular subtypes compared to either imaging technique used alone. PATIENTS AND METHODS: This prospective study examined 480 breast cancers in 475 patients undergoing DCE-MRI and NME-DWI at 3.0 T. Breast cancers were classified as follows: human epidermal growth factor receptor 2 enriched (HER2-enriched), luminal A, luminal B (HER2-), luminal B (HER2+), and triple-negative subtypes. A total of 20% cases were withheld as an independent test dataset, and the remaining cases were used to train DNN with an 80% to 20% training-validation split and 5-fold cross-validation. The diagnostic accuracies of DNN in 5-way subtype classification between the DCE-MRI, NME-DWI, and their combined multiparametric-MRI datasets were compared using analysis of variance with least significant difference posthoc test. Areas under the receiver-operating characteristic curves were calculated to assess the performances of DNN in binary subtype classification between the 3 datasets. RESULTS: The 5-way classification accuracies of DNN on both DCE-MRI (0.71) and NME-DWI (0.64) were significantly lower (P < .05) than on multiparametric-MRI (0.76), while on DCE-MRI was significantly higher (P < .05) than on NME-DWI. The comparative results of binary classification between the 3 datasets were consistent with the 5-way classification. CONCLUSION: The combination of DCE-MRI and NME-DWI via DNN achieved a significant improvement in breast cancer molecular subtype prediction compared to either imaging technique used alone. Additionally, DCE-MRI outperformed NME-DWI in differentiating subtypes.

Long noncoding RNA DLEU1 promotes proliferation and glycolysis of gastric cancer cells via APOC1 upregulation by recruiting SMYD2 to induce trimethylation of H3K4 modification.

OBJECTIVES: APOC1 has been reported to promote tumor progression. Nevertheless, its impact on cell proliferation and glycolysis in gastric cancer (GC) remains to be probed. Hence, this study explored the related impacts and mechanisms. METHODS: DLEU1, SMYD2, and APOC1 expression was detected in GC cells. Afterward, ectopic expression and knockdown experiments were conducted in GC cells, followed by measurement of cell proliferation, glucose uptake capability, lactic acid production, ATP content, extracellular acidification rate (ECAR), oxygen consumption rate (OCR), and GLUT1, HK2, and LDHA expression. In addition, interactions between DLEU1 and SMYD2 were analyzed with RIP and RNA pull down assays, and the binding of SMYD2 to APOC1 promoter and the methylation modification of SMYD2 in H3K4me3 were assessed with a ChIP assay. The ectopic tumor formation experiment in nude mice was conducted for in vivo validation. RESULTS: DLEU1, SMYD2, and APOC1 were highly expressed in GC cells. The downregulation of DLEU1 or APOC1 inhibited glucose uptake capability, lactic acid production, ECAR, the expression of GLUT1, HK2, and LDHA, ATP contents, and proliferation but augmented OCR in GC cells, which was also verified in animal experiments. Mechanistically, DLEU1 interacted with SMYD2 and recruited SMYD2 to APOC1 promoter to promote H3K4me3 modification, thus facilitating APOC1 expression. Furthermore, the effects of DLEU1 silencing on GC cell proliferation and glycolysis were negated by overexpressing SMYD2 or APOC1. CONCLUSION: LncRNA DLEU1 recruited SMYD2 to upregulate APOC1 expression, thus boosting GC cell proliferation and glycolysis.

Comprehensive analysis identifies cuproptosis-related gene DLAT as a potential prognostic and immunological biomarker in pancreatic adenocarcinoma.

BACKGROUND: Cuproptosis is a regulated cell death form associated with tumor progression, clinical outcomes, and immune response. However, the role of cuproptosis in pancreatic adenocarcinoma (PAAD) remains unclear. This study aims to investigate the implications of cuproptosis-related genes (CRGs) in PAAD by integrated bioinformatic methods and clinical validation. METHODS: Gene expression data and clinical information were downloaded from UCSC Xena platform. We analyzed the expression, mutation, methylation, and correlations of CRGs in PAAD. Then, based on the expression profiles of CRGs, patients were divided into 3 groups by consensus clustering algorithm. Dihydrolipoamide acetyltransferase (DLAT) was chosen for further exploration, including prognostic analysis, co-expression analysis, functional enrichment analysis, and immune landscape analysis. The DLAT-based risk model was established by Cox and LASSO regression analysis in the training cohort, and then verified in the validation cohort. Quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) assays were performed to examine the expression levels of DLAT in vitro and in vivo, respectively. RESULTS: Most CRGs were highly expressed in PAAD. Among these genes, increased DLAT could serve as an independent risk factor for survival. Co-expression network and functional enrichment analysis indicated that DLAT was engaged in multiple tumor-related pathways. Moreover, DLAT expression was positively correlated with diverse immunological characteristics, such as immune cell infiltration, cancer-immunity cycle, immunotherapy-predicted pathways, and inhibitory immune checkpoints. Submap analysis demonstrated that DLAT-high patients were more responsive to immunotherapeutic agents. Notably, the DLAT-based risk score model possessed high accuracy in predicting prognosis. Finally, the upregulated expression of DLAT was verified by RT-qPCR and IHC assays. CONCLUSIONS: We developed a DLAT-based model to predict patients' clinical outcomes and demonstrated that DLAT was a promising prognostic and immunological biomarker in PAAD, thereby providing a new possibility for tumor therapy.

miR-324-5p upregulation potentiates resistance to cisplatin by targeting FBXO11 signalling in non-small cell lung cancer cells.

Dysregulation of microRNAs (miRNAs) plays a key role during the pathogenesis of chemoresistance in lung cancer (LCa). Previous study suggests that miR-324-5p may serve as a unique miRNA signature for LCa, but its role and the corresponding molecular basis remain largely explored. Herein, we report that miR-324-5p expression was significantly increased in cisplatin (CDDP)-resistant LCa tissues and cells, and this upregulation predicted a poor post-chemotherapy prognosis in LCa patients. miR-324-5p was further shown to impact CDDP response: Ectopic miR-324-5p expression in drug-naïve LCa cells was sufficient to attenuate sensitivity to CDDP and to confer more robust tumour growth in CDDP-challenged nude mice. Conversely, ablation of miR-324-5p expression in resistant cells effectively potentiated CDDP-suppressed cell growth in vitro and in vivo. Using multiple approaches, we further identified the tumour suppressor FBXO11 as the direct down-stream target of miR-324-5p. Stable expression of FBXO11 could abrogate the pro-survival effects of miR-324-5p in CDDP-challenged LCa cells. Together, these findings suggest that miR-324-5p upregulation mediates, at least partially, the CDDP resistance by directly targeting FBXO11 signalling in LCa cells. In-depth elucidation of the molecular basis underpinning miR-324-5p action bears potential implications for mechanism-based strategies to improve CDDP responses in LCa.

Determination of Hepatocellular Carcinoma and Characterization of Hepatic Focal Lesions with Adaptive Multi-Exponential Intravoxel Incoherent Motion Model.

PURPOSE: To distinguish hepatocellular carcinoma (HCC) from other types of hepatic lesions with the adaptive multi-exponential IVIM model. METHODS: 94 hepatic focal lesions, including 38 HCC, 16 metastasis, 12 focal nodular hyperplasia, 13 cholangiocarcinoma, and 15 hemangioma, were examined in this study. Diffusion-weighted images were acquired with 13 b values (b = 0, 3, …, 500 s/mm2) to measure the adaptive multi-exponential IVIM parameters, namely, pure diffusion coefficient (D), diffusion fraction (fd), pseudo-diffusion coefficient (Di*) and perfusion-related diffusion fraction (fi) of the ith perfusion component. Comparison of the parameters of and their diagnostic performance was determined using Mann-Whitney U test, independent-sample t test, one-way analysis of variance, Z test and receiver-operating characteristic analysis. RESULTS: D, D1* and D2* presented significantly difference between HCCs and other hepatic lesions, whereas fd, f1 and f2 did not show statistical differences. In the differential diagnosis of HCCs from other hepatic lesions, D2* (AUC, 0.927) provided best diagnostic performance among all parameters. Additionally, the number of exponential terms in the model was also an important indicator for distinguishing HCCs from other hepatic lesions. In the benign and malignant analysis, D gave the greatest AUC values, 0.895 or 0.853, for differentiation between malignant and benign lesions with three or two exponential terms. Most parameters were not significantly different between hypovascular and hypervascular lesions. For multiple comparisons, significant differences of D, D1* or D2* were found between certain lesion types. CONCLUSION: The adaptive multi-exponential IVIM model was useful and reliable to distinguish HCC from other hepatic lesions.