A previous study implied that long intergenic non-coding RNA 1410 (LINC01410) promotes angiogenesis and metastasis of gastric cancer. However, the role of LINC01410 in colon cancer (CC) has remained elusive. In the present study, LINC01410 was identified to be highly expressed in CC tissues compared to adjacent normal tissues. It was indicated that high expression of LINC01410 in CC tissues was associated with poor prognosis. Further functional study suggested that LINC01410 knockdown significantly reduced the proliferation and invasive capacity of HT-29 and SW620 cells, and inhibited the cell cycle. Regarding the mechanism, LINC01410 was indicated to serve as a sponge for microRNA (miR)-3128, as evidenced by a luciferase reporter assay. Furthermore, knockdown of LINC01410 significantly increased the levels of miR-3128. In addition, miR-3128 was markedly downregulated in CC tissues compared with that in adjacent normal tissues. A rescue assay revealed that inhibition of miR-3128 significantly abrogated the effects of LINC01410 knockdown on CC cell proliferation and invasion. In conclusion, the present study demonstrated that LINC01410 functions as an oncogene in CC, at least in part by directly inhibiting miR-3128.

Introduction:Resveratrol plays a protective role against sepsis development, and the long noncoding Rna (lncRna) maLaT1 is an inflammation-relevant biomarker. This investigation attempted to reveal whether resveratrol attenuated inflammation of sepsis-induced acute kidney injury (aKi) by regulating maLaT1.Material and methods: in total 120 rats were divided into a control group (n = 20), a Sham group (n = 20), a sepsis group (n = 40) and a resveratrol group (n = 40), and serum levels of inflammatory cytokines and aKi biomarkers were determined. an equal number of rats under identical treatments were, additionally, tracked for their survival, and the serum level of lncRna maLaT1 was measured by RT-PCR. moreover, septic cell models were constructed by treating hK-2 cells with lipopolysaccharide (LPS), and tumor necrosis factor α (TnF-α), interleukin (iL)-1β, iL-6 levels released by the cells were determined with ELiSa.Results: Resveratrol treatment significantly brought down serum levels of inflammatory cytokines (i.e. TnF-α, iL-1β and iL-6), kidney function indicators (i.e. Scr, blood urea nitrogen [Bun] and Scys C), aKi biomarkers (i.e. ngaL and Kim-1) and maLaT1 in cecal ligation and puncture (CLP)-induced septic model rats (all p < 0.05), and the life span of septic rats was elongated by resveratrol treatment (p < 0.05). Viability and cytokine release of LPS-treated hK2 cells were rescued by resveratrol (p < 0.05), which was accompanied by a marked fall of maLaT1 expression (p < 0.05). in addition, si-maLaT1 diminished viability and suppressed cytokine release of hK2 cells, while pcDna3.1-maLaT1 hindered the impact of resveratrol on the inflammatory response of hK2 cells (p < 0.05). ultimately, miR-205, a protective molecule in sepsis-relevant aKi, was down-regulated by resveratrol and si-maLaT1 (p < 0.05).Conclusions: Resveratrol relieved sepsis-induced aKi by restraining the lncRna maLaT1/miR-205 axis.

Abstracts Background Anastomotic leakage (AL) is a serious complication after anterior resection. The purpose of this study was to determine the role of microvascular density (MVD) in AL and to develop a nomogram to accurately predict AL. Methods This study retrospectively enrolled 477 consecutive patients who underwent anterior resection for rectal cancer from January 2011 to January 2019. Tissue samples of the resection margins were assessed for MVD. Univariate and multivariate regression analyses were used to identify the risk factors for AL. Results The incidence of clinical AL was 6.7%. MVD in the distal margin was associated with AL (P < .001). Univariate and multivariate regression analysis identified the following variables as independent risk factors for AL: preoperative albumin ≤35 g/L (odds ratio [OR] = 2.511), neoadjuvant treatment (OR = 3.560), location of tumor ≤7 cm (OR = 3.381), blood loss ≥100 mL (OR = 2.717), and MVD in the distal margin ≤20 (OR = 4.265). Then, a nomogram including these predictors was developed. The nomogram showed good discrimination (AUC = 0.816) and calibration (concordance index = 0.816). The decision curve analysis demonstrated that the nomogram was clinically useful. Conclusions MVD in the distal margin is closely associated with AL. The nomogram can be used for individualized prediction of AL after anterior resection for patients with rectal cancer.

ObjectivesMetachronous liver metastasis (LM) significantly impacts the prognosis of stage I-III colorectal cancer (CRC) patients. An effective biomarker to predict LM after surgery is urgently needed. We aimed to develop deep learning-based models to assist in predicting LM in stage I-III CRC patients using digital pathological images.MethodsSix-hundred eleven patients were retrospectively included in the study and randomly divided into training (428 patients) and validation (183 patients) cohorts according to the 7:3 ratio. Digital HE images from training cohort patients were used to construct the LM risk score based on a 50-layer residual convolutional neural network (ResNet-50). An LM prediction model was established by multivariable Cox analysis and confirmed in the validation cohort. The performance of the integrated nomogram was assessed with respect to its calibration, discrimination, and clinical application value.ResultsPatients were divided into low- and high-LM risk score groups according to the cutoff value and significant differences were observed in the LM of the different risk score groups in the training and validation cohorts (P&lt;0.001). Multivariable analysis revealed that the LM risk score, VELIPI, pT stage and pN stage were independent predictors of LM. Then, the prediction model was developed and presented as a nomogram to predict the 1-, 2-, and 3-year probability of LM. The integrated nomogram achieved satisfactory discrimination, with C-indexes of 0.807 (95% CI: 0.787, 0.827) and 0.812 (95% CI: 0.773, 0.850) and AUCs of 0.840 (95% CI: 0.795, 0.885) and 0.848 (95% CI: 0.766, 0.931) in the training and validation cohorts, respectively. Favorable calibration of the nomogram was confirmed in the training and validation cohorts. Integrated discrimination improvement and net reclassification index indicated that the integrated nomogram was superior to the traditional clinicopathological model. Decision curve analysis confirmed that the nomogram has clinical application value.ConclusionsThe LM risk score based on ResNet-50 and digital HE images was significantly associated with LM. The integrated nomogram could identify stage I-III CRC patients at high risk of LM after primary colectomy, so it may serve as a potential tool to choose the appropriate treatment to improve the prognosis of stage I-III CRC patients.