Identification of small proline‐rich protein 1B ( SPRR1B ) as a prognostically predictive biomarker for lung adenocarcinoma by integrative bioinformatic analysis

Background: Long non-coding RNAs (lncRNAs) have been reported to play important roles in the progression of human cancers. Herein, bioinformatic analysis identified that LINC00942 was a highly overexpressed lncRNA in lung adenocarcinoma (LUAD). The present study aimed to explore the roles and possible molecular mechanisms of LINC00942 in LUAD. Methods: First, on the basis of TCGA database, the expression and prognosis of LINC00942 were analyzed in LUAD tissues. Then, si-LINC00942 was transfected into A549 and H1299 cells to knockdown the expression of LINC00942. Cell viability was detected by MTT assay. Flow cytometry was used to analyze cell apoptosis. The expressions of PCNA, Bax, Bcl-2, and wnt/β-catenin pathway proteins were detected by western blotting. Dual-luciferase reporter assay was used to evaluate the regulatory relationship between LINC00942 and miR-5006-5p, or miR-5006-5p and FZD1. Results: We discovered that LINC00942 was up-regulated in LUAD tissues compared with adjacent tissues. Besides, we found the increased LINC00942 expression was associated with poor survival. In addition, silencing of LINC00942 suppressed the proliferation, migration, invasion and facilitated the apoptosis of A549 and H1299 cells. Moreover, silencing of LINC00942 repressed the expression of PCNA, Bcl-2, and enhanced Bax expression in A549 and H1299 cells. Mechanically, LINC00942 exerted its effects via enhancing Wnt signaling. LINC00942 functioned as competing endogenous RNA (ceRNA) by binding to miR-5006-5p, upregulating the expression of FZD1, which was a direct target of miR-5006-5p. Conclusion: Our findings indicated that LINC00942/miR-5006-5p/FZD1 axis played important roles in LUAD growth through enhancing Wnt signaling. LINC00942/miR-5006-5p/FZD1 axis might serve as a potential biomarker and therapeutic target for LUAD treatment.

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The Utility of Actinin-4 Protein Overexpression as a Predictive Biomarker for Therapeutic Effect of Adjuvant Chemotherapy to the Resected Lung Adenocarcinoma

Annals of Oncology ◽

10.1093/annonc/mdv048.09 ◽

2015 ◽

Vol 26 ◽

pp. i18

Author(s):

H. Shiraishi ◽

Y. Fujiwara ◽

T. Kakuya ◽

K. Tsuta ◽

S. Watanabe ◽

...

Keyword(s):

Adjuvant Chemotherapy ◽

Lung Adenocarcinoma ◽

Therapeutic Effect ◽

Predictive Biomarker ◽

Protein Overexpression

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Cardiomyocyte microRNA-150 confers cardiac protection and directly represses proapoptotic small proline–rich protein 1A

JCI Insight ◽

10.1172/jci.insight.150405 ◽

2021 ◽

Vol 6 (18) ◽

Author(s):

Tatsuya Aonuma ◽

Bruno Moukette ◽

Satoshi Kawaguchi ◽

Nipuni P. Barupala ◽

Marisa N. Sepúlveda ◽

...

Keyword(s):

Cardiac Protection ◽

Small Proline Rich Protein ◽

Proline Rich Protein

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Small proline rich protein 2a in benign and malignant liver disease

Hepatology ◽

10.1002/hep.26889 ◽

2014 ◽

Vol 59 (3) ◽

pp. 1130-1143 ◽

Cited By ~ 15

Author(s):

Yoshiaki Mizuguchi ◽

Kumiko Isse ◽

Susan Specht ◽

John G. Lunz ◽

Natasha Corbitt ◽

...

Keyword(s):

Liver Disease ◽

Malignant Liver Disease ◽

Malignant Liver ◽

Small Proline Rich Protein ◽

Proline Rich Protein

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Small proline-rich protein 2 family is a cluster of genes induced by estrogenic compounds through nuclear estrogen receptors in the mouse uterus

Reproductive Toxicology ◽

10.1016/j.reprotox.2010.04.009 ◽

2010 ◽

Vol 30 (3) ◽

pp. 469-476 ◽

Cited By ~ 8

Author(s):

Seok-Ho Hong ◽

Jae Eun Lee ◽

Jin Ju Jeong ◽

Soo Jin Hwang ◽

Se Na Bae ◽

...

Keyword(s):

Estrogen Receptors ◽

Mouse Uterus ◽

Estrogenic Compounds ◽

Small Proline Rich Protein ◽

Nuclear Estrogen Receptors ◽

Proline Rich Protein

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Identification of predictive biomarker for immunotherapy by associating with CD8+T cell Infiltration in lung adenocarcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e21177 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e21177-e21177

Author(s):

Puyuan Xing ◽

Teng Li ◽

Han Wang ◽

Lin Yang ◽

Guoqiang Wang ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Predictive Biomarkers ◽

Tumor Infiltrating Lymphocytes ◽

Predictive Performance ◽

Predictive Biomarker ◽

Pooled Analysis ◽

The Cancer Genome Atlas ◽

T Cell Infiltration ◽

Or Groups ◽

Cancer Genome Atlas

e21177 Background: Tumor immune microenvironment (TIME) has been proved associated with response to immunotherapy(I/O). We hypothesized that screening potential mutation pattern which could significantly impact the tumor infiltrating lymphocytes(TILs) can help us to identify predictive biomarkers for I/O in Lung adenocarcinoma(LUAD). Methods: Multiple-dimensional data from The Cancer Genome Atlas LUAD cohort (n = 514) was used for building a mathematical model beween mutation signature and CD8+TIL score (based on MCP-counter). An independent public validation cohort (cohort 1: LUAD, n = 598) were used to assess the immunotherapeutic predictive performance of the potential mutation patterns. Results: Top 100 gene associated with CD8+TIL score were selected based on MC+ model which can provides the minimum non-convexity of the penalized loss given the level of bias. Seven TIME genes (SPTA1 coef 0.09; MET coef 0.02; HSD3B1 coef -0.00; STAT4 coef -0.01; EGFR coef -0.08; PIK3CB coef -0.08; KEAP1 coef -0.24) were generated by taking the intersection of the top 100 mutant genes and FoundationOne (F1) CDx NGS 315 genes panel and verified in cohort 1. Survival analysis showed that SPTA1mt was the only one that associated with both significantly longer PFS (median PFS 3.15 vs 2.89 months; HR 0.65; 95% CI 0.45 to 0.93; p = 0.02) and OS (median PFS 15.08 vs 7.36 months; HR 0.59; 95% CI 0.40 to 0.88; p = 0.01) for patients who received I/O compared with chemotherapy(CT) among seven TIME genes. In order to test our hypothesis fully, a pooled analysis of SPTA1mt (a core positive predictors of CD8+TILs) and KEAP1mt (a core negative predictors for CD8+TILs ) were conducted and yielded that co occurrence of SPTA1mt and KEAP1mt had a compound effects for TIME. The validation showed that co mutation with SPTA1mt was accompanied by an decrease HR for I/O vs. CT in both PFS (HR S+K vs. K only 0.59 vs 1.56) and OS (HR S+K vs. K only 0.39 vs 0.80) for KEAP1mt patients. Conclusions: Our data show that it is feasible to identify individuals or groups of individual with specific mutations to immunotherapy responses from TIME view. SPTA1mt was a core predictors for higher CD8+ TILs and can be identified as a predictive biomarker for benefit from I/O compared with CT. Prospective studies are warranted for further investigation.

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