Title: LUM as a novel prognostic marker and its correlation with immune infiltration in gastric
cancer: a study based on immunohistochemical analysis and bioinformatics
Version: 1 Date: 19 Oct 2023
Reviewer:  Kun Fang
Reviewer's report:
The author has used the single-gene online database to its limit. There is almost no bioinformatics
analysis data mining part of its own, and the innovation must be quite satisfactory. Perhaps
the only bright spot is the IHC verification of 40 pathological tissue sections. Of course, when
screening differential genes, A small part of R language data mining is used, but it belongs to the
discussion of bioinformation mining a few years ago. Overall, it feels like this article is relatively
bland and not very innovative. It is recommended to increase bioinformatics in-depth mining; it
is recommended to increase single-gene in vitro phenotypic function experiments to clarify the
potential regulatory value of LUM on cancer.
Title: LUM as a novel prognostic marker and its correlation with immune infiltration in gastric
cancer: a study based on immunohistochemical analysis and bioinformatics
Version: 1 Date: 19 Oct 2023
Reviewer:  Kun Fang
Reviewer's report:
The author has used the single-gene online database to its limit. There is almost no bioinformatics
analysis data mining part of its own, and the innovation must be quite satisfactory. Perhaps
the only bright spot is the IHC verification of 40 pathological tissue sections. Of course, when
screening differential genes, A small part of R language data mining is used, but it belongs to the
discussion of bioinformation mining a few years ago. Overall, it feels like this article is relatively
bland and not very innovative. It is recommended to increase bioinformatics in-depth mining; it
is recommended to increase single-gene in vitro phenotypic function experiments to clarify the
potential regulatory value of LUM on cancer.
Please review this paper.
user
Title: LUM as a novel prognostic marker and its correlation with immune infiltration in gastric
cancer: a study based on immunohistochemical analysis and bioinformatics
Version: 1 Date: 19 Oct 2023
Reviewer:  Kun Fang
Reviewer's report:
The author has used the single-gene online database to its limit. There is almost no bioinformatics
analysis data mining part of its own, and the innovation must be quite satisfactory. Perhaps
the only bright spot is the IHC verification of 40 pathological tissue sections. Of course, when
screening differential genes, A small part of R language data mining is used, but it belongs to the
discussion of bioinformation mining a few years ago. Overall, it feels like this article is relatively
bland and not very innovative. It is recommended to increase bioinformatics in-depth mining; it
is recommended to increase single-gene in vitro phenotypic function experiments to clarify the
potential regulatory value of LUM on cancer.
assistant