Support Vector Machine for Lung Adenocarcinoma Staging Through Variant Pathways

MicroRNA-133a-3p Inhibits Lung Adenocarcinoma Development and Cisplatin Resistance through Targeting GINS4

GINS subunit complex 4 (GINS4) is fundamental to DNA replication and G1/S phase transition of the cell cycle in eukaryotes. Further, recent studies implied that GINS4 can mediate the progression of several tumors, but its mechanism in lung adenocarcinoma (LUAD) is not clarified. Therefore, the role of GINS4 in LUAD was explored. miR-133a-3p and GINS4 mRNA expression were tested through qRT-PCR. Protein levels of the two genes were assayed by Western blot. Their targeting relationship was predicted and verified by bioinformatics prediction and dual-luciferase analysis. The functions of miR-133a-3p and GINS4 in LUAD were evaluated by Transwell, wound healing, CCK-8, and flow cytometry assays. MTT assay and caspase-3 activity detection were utilized to measure the regulation of miR-133a-3p/GINS4 in the cisplatin sensitivity of LUAD cells. The results showed that GINS4 was highly expressed in LUAD cells (p < 0.05). miR-133a-3p, the upstream gene of GINS4 in LUAD, negatively mediated GINS4 expression. Moreover, overexpressing GINS4 enhanced the proliferative, migratory, and invasive abilities of LUAD cells and inhibited cell apoptosis and the sensitivity to cisplatin, while overexpressing miR-133a-3p caused the contrary results. However, the promoting effects of GINS4 overexpression on LUAD could be offset by miR-133a-3p overexpression. miR-133a-3p could regulate malignant behaviors and cisplatin sensitivity of LUAD cells through negatively regulating GINS4. In conclusion, our findings demonstrated that GINS4 was overexpressed in LUAD and promoted the malignant behavior of LUAD cells. Moreover, miR-133a-3p could negatively regulate GINS4, thereby suppressing the malignant progression and increasing the cisplatin sensitivity of LUAD.

Construction, validation and, visualization of a web-based nomogram for predicting the overall survival and cancer-specific survival of leiomyosarcoma patients with lung metastasis

Background

This study sought to assess the prognostic factors for leiomyosarcoma (LMS) patients with lung metastasis and construct web-based nomograms to predict overall survival (OS) and cancer-specific survival (CSS).

Method

Patients diagnosed with LMS combined with lung metastasis between 2010 and 2016 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly divided into a training set and a testing set. The X-tile analysis provides the best age and tumor size cut-off point, and changes continuous variables into categorical variables. The independent prognostic factors were determined by Cox regression analysis, and 2 nomograms were established. Receiver operating characteristic curves and calibration curves were used to evaluate the nomograms. Based on the nomograms, 2 web-based nomograms were established.

Results

Two hundred and twenty-eight cases were included in the OS nomogram construction, and were randomly divided into a training set (n=160) and a validation set (n=68). Age, T stage, bone metastasis, surgery, chemotherapy, marital status, tumor size, and tumor site were found to be correlated with OS. One hundred and eighty-three cases were enrolled in the CSS nomogram construction, and randomly divided into a training set (n=129) and a validation set (n=54). Age, bone metastasis, surgery, chemotherapy, tumor size, and tumor site were found to be correlated with CSS. Two nomograms were established to predict OS and CSS. In the training set, the areas under the curve of the nomogram for predicting 1-, 2-, and 3-year OS were 0.783, 0.830, and 0.832, respectively, and those for predicting 1-, 2-, and 3-year CSS were 0.889, 0.777, and 0.884, respectively. Two web-based nomograms were established to predict OS (https://wenn23.shinyapps.io/lmslmosapp/), and CSS (https://wenn23.shinyapps.io/lmslmcssapp/).

Conclusion

The developed web-based nomogram is a useful tool for accurately analyzing the prognosis of LMS patients with lung metastasis, and could help clinical doctors to make personalized clinical decisions.